clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_hdf('train.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from twosigmafunc import scatter_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+yRq9lUXLqPyL6FqnZcf+6NOD+O8vHMT4e1dq8rraz6NZQKE9rpd5MusuGoun\nsOudC/Cdm5gTdJYiGFINm7aYl2xmm+6Yr1F/72YpjWKKzVEsXG855ZpGN8Q7Nq80fG965tsAhzfm\nVG+l/LwRUWOpR+ZwBwCnz+e7S1GUOwH8DYBPAoCiKD0A/gTARgBOAG8qivKqz+f705nHHQDeBPDE\nzLk2ANju8/nGavweFrVNAz144+jlei+DqOFo2bFEKo1T54Nln6fav4ApzDyZdRfVjPjDpo8XU6xp\nS7GSTStrLPU1NWaB59CwP/dZzTfTZnRDHI0lS3pv822Awxtzqifzn7fKZMWJqHrqERzeA+CXAODz\n+Q4oirIx77HbAezz+XwqAFVRlNMABgBobe7+GMC/+3y+4zMZyD4A/6QoynUAnvP5fM/X7F0sYqpO\nC3wiupods0kOBHU6dVrR1izhyS9sQDAUw7+99RGOnwmg0nFiYebJbHC6Zr7Bqta0xSjAKlayabZG\np2TTHc1htVGMWeA5PqXiO6+cRLNsx5FTY2Vn2sxuiE+eCxp2d9UznwY4vDGnUmg/r57Wpoqd0+qo\nGyJqTPUIDlsA5Jd+phRFsft8vqTOYyEArQCgKIqEbLnp7TOPNQP4BwD/JwAbgD2Korzj8/mOGb1w\ne7sLdjv/USzm4Pvzb+lPtBh1tTVh1Q2dAABvexNGg9GSzzExHcc3/vUIXE47PrwUKv6EMnS2OrHq\nhk44pat/xW9atww/feOs4XNEEUgbTN5wSjao8RS87U24/eYeAMDBdy9jbCKKrrYm3HnLEvzOJ27C\nd37+Pg6cuAT/RBTema9/8aGbYbPNDrB6DdZgtMatt6+AKAg4cOLSrNfUOzcAeL2eWX/2tDaZXq8D\nJ2aXB2uZNleThCd2rDVY7WyXxqYRCBndEKvYsmE5Xnvn/JzHVi5tQTiasPS+5r+OGGySA96u5rLO\nXQmF14bqI5VK4/mfvWvp57VUZj9v2t+h+X83UXH8uWlci/Ha1OOncwpA/icpzgSGeo95AEzM/P9W\nAK/n7SmMAPimz+eLAICiKLuR3cdoGBwGg5H5r56IrlkDqzoxNhZGUhBw45IW3ZufXm9z0dEY5QSV\n+ZZ0uHApYPz3WV9vG0KTUeSHng/dtQKRaBxvHrukm4W7rs34nLF4Cptu6cHntyu5rNN/vGPFrAzg\n/3z56KxSxtFgFD994ywi0bjlUkZtjYWlp5+8+3rYRBG/cfvy3GsCwMkz/jkZSK/XA79/btA9sKpT\nd8+jmX+nUwamAAAgAElEQVT/9Uf4+MZls7q5GkklUujwGM9J/NTmGyAgo1tWm0xlZn2WgUD5o1WK\nrSMVT+h+PrVgdG2o9gobVJXz82rG6OdtYFXnnL+byBx/bhrXQr42ZkFtPYLDfQAeAvDDmT2Hx/Me\nOwjg64qiOAHIAG4CcGLmsa0AfpF3bD+AlxRFGQQgIluu+p0qr52IrkGdLTLWrurEdDSBP/37N6Dq\nDLd3SjbcvbYHyGQqNjdR7zXuGViCHZtvxNPPHTQsE5UcAlLp9KySSK2sc8fmlfjBq8M4eS6IYEiF\n5LAByOBSIALZIUBN6NeXnjw3MevP+U1bKlXKWKz0VHbY0NnqLKvZyqMPrEY0lsS+E9b3U8fiKXz/\n1WF86TdvLnpssTmJLtlh+N5sIipWZlereY20cNWi9LiaY2GIqLrqERz+GMA2RVH2Izvr+XcVRfkz\nAKd9Pt9PFUX5ewBvIBvwPenz+WIzz1MAfFc7ic/ne19RlO8BOAAgAeC7Pp/v3Vq+ESJa/G7/WDea\nZTveeveyYddMIBtIZNIZHD1dvf5YWkMcswAAAH41dAl2m003A+CS7fi93/wY1EQK33vFh/15wZJR\nYAiY7xWq9B4js26hpTZbyd8D+fntCt7/KGB57x8ADPn8ULenLN0sW7kh1ntvlR45wRvz2ltIY0Nq\nsSewUmNhiKj2ah4c+ny+NIA/LPjyybzHvw3g2zrP+486X/sGgG9Ueo1ERJpDJ0eRMo4JZ9l3/DLi\nSYsHlyF/XIQ2MH7v0IhuM5nDPn/RDIDvnPWOq2YdNM2aycy382Y+KxkPjdE4h8F+L147NKJ7Dv3X\nTMMfjKC3u/i+klJviKs1coI35rWzEMeG1OrnFSh/LAwR1U9j/s1FRNQgrAaGAKoaGAKzb9xsoojt\nty037DIaCKn4/is+pAy6zJQ6OkKvJFFNpDA6s5d7sN9r+XnlspLx0GgZxvEpFRlczTBmAGzd2IvO\nFidEAehsceKOm7rNX1gQSlqndkNc7H0brfGl3adNn1fpdejRrq2aMO5ebeWYxa7a17AatMoDPSw9\nJiK2iyIiagB3r70Oh31jus1iNIU3bm6Xw3DMAwDsO3EZTU67brllq1s2HK8gO0S4mxwIhlTdkkS9\nbMm6vi48uGEZjpwar1opo9WMh1mG8eipcTz7xB2zsmoAcPTMuO7n6JRs8LZVrs2/plFHTljJhBkd\ns2PzjQhHEtdMprJRr6EVhaXHXW1NGFjVydJjImJwSETUCO78WA9kuw17hi7OeUxrRFN44/aTNz4w\nDSYB45tU2WFDc5N+cOhta8KXHvoYIAjwtjXNea7evr/dh0awdWMvnn3ijqqVMlpttmJ1T1V+udum\ntT265aab1vZU5Qa/UWfBWdnTaXTMm8cuQY2nyi6tXEj79oDGvYZWFJYer7oh20WUiIjBIRFRnYkC\n8LcvHUVHi4zl3W5MRxOYCKtoc8tYc307dm7rmzNOwSxrkc/oJlVNpBCJJXSfc3FsGk89/zY6dW7y\nrWRLqnFDrAUOOzbfmHstowxlOXuqPvNgHwRByGbDQio6PFffezXUct+XVVb3dBodo/2ioliToEIL\ncd8e0JjXsFRa6bFTsnO8BBEBYHBIRA1Esgto98i4EowVP7gMskPUHUNRb9q+wfEpFeNTKrasX4Yt\nty4FBAGtzRLCkQRsojgrm2J1z2C7R55VbqllZsyen7+ewpv8WmZL1EQKgakYdh26gGOnx2YFDl/7\nvdsRjsR1s0zljHOwiSIevm8V7l23FMhk4C1zr55VlRo5Uclsm9U9nVb3qlotrSy1A22j4NgQIlqM\nGBwSUcOIJzNVCwwBVDUwlCURiWQaBv1fSvLWics4OhMMiUI2WOvwSFivdOeGpscTKcOsRT6X0wG7\nTcCLu4ZnZWYGVnVaej4wu/PpfLIlVgOZ/ExS4etYDRxKGedQr8zVfEZOVGPNVq+t1e8bK78sWMj7\n9gCODSGixYfBIRHRPLib7Pid/7AG/cvb8L/2fYDdJYxJMBKLp3IleloWLxCKY9c7F+A7N4FILIHA\nlApZKh4EhCNxvPjq8Ky9jONTKvYMXcTybrelm3yt8+njn1hTVrak1ECmMJOkp1jgUMo4h3plruYz\ncqIaa7Z6bc1mbOazUlq5kPftARwbQkSLT+MW8xMRLQDhaBL/949P4Knnfo1wNIH7Bpegw5O9IRZL\nm4BgyfnRcK5tfiyeTVNKDuO/yoPhOPblDbrPNx1NYMv6ZehscUIQzNe778TlXHv+Rx9YPWccxNaN\nvYbZklLa/VvdSxmYiuHsyGTRMQr54xz0Ri8Uy1zVYkxDqSMnqrlmK9e28BinpL9uK6WVWrZSz0LZ\ntwfMb2wIEVEjYeaQiKgCJqcTOPjeKGwisGntEtymdOOdYT/2HpnbfbTSmhzZktaMwczDuEE57URY\nxfbbluPTW1ZjMqzilYPndLulavKzdVazJaWWDVrdSykIwP/41yOWyinNMpcLMXNVzTVbyYQVHuN2\nSfjJG2fLKq3kvj0iosbC4JCIyMSSDhcuBSKWj0+lgdePXsLrRy9BsgsQRVRkH6KZyUiyrOdpzWq0\nrMfObf1QE2nsN8g0FgYe2vNM11ZiIGO27y2fWdOcQmYlmA/ft2rBdZysRZdMK9c2/5j5lFZy3x4R\nUeNgWSkRkQHZIZYUGBaKJzNVDwznY82K9lk38TZRxGPbFXRWsMyv1LJBLZNkxKj01aicsljmEoDh\n6zVq5srsMyp3zXolt+Wsq5zSSi0T+ewTd+CvvnQnnn3iDuzc2t/QYyyIiBYrZg6JiIwYlGlWm1MS\nEU+kc9mx6ryGDZ/dNjfTVukyP7PzuZx22G1zoz29TNLAqg4M9nvxty8d1X0dLQvZW/B1K5nLhZi5\nqtSaG2nGoJVsJRERVReDQyIiHXYRUJP1SfvdM7AU738UxIh/uoqvsQQu2Q41kYJ/Ijprtl+lAo/8\nwfW+cxM4Pxqe9fj50TBe2n16Vjmo9pyH71s1p0xRNRnfYZTVtFKCuRA7TubmMg4sAQQB3ramsta8\nUGcMEhFRdTA4JCLSIUt2JGPl7eUrRhCADo8Ta1d34PSFSVz0TyOdyZZMLvO68dCmG3HYN6r/XMw/\noemwi4jFE/inn52YKcfMBsFOScTda5fgsw/2zStYKsxGtXskRFT9ckWtKY3dJhTNYJWT1SzlOQsl\nc1WpbN9CnzFIRESVx+CQiEhHVK1OYCg7RDz52AZ421340a9O48Lo1exgOjOTTXvtFIKhuO7zK1Fp\nmkim8eaxK3O+HounsfvQCERBwM6t/WUHS4XZqIDBewGulnbuOnTBUgarnKzmQiwbNVOpbN9C7NRK\nRETVxeCQiEhHqfv91q7qwG/duxKvH7mIfScuG46PSCTTkGayMfuO63cFPTQ8inaPpBtUdXhkrF3d\niTePXkSqSlWvh33+srNGVucUato9TjTJdssZrHJKQMt5jlbeWo0S0/mcu5LZvlp0PSUiooWFwSER\nkQ5RKC1AHBmdRk9HMx7bvgYPbboRf/mtt3T3LGrjI/wTUcTi+qWWajyNwdVtOPDe3NLSdas7sWV9\nL46fHjPNyDklEXfefB2GhscwOZ2w/kYABENq2Vkjq3MKNYP9XYiqyZIzWOVkNa08p5oNWipx7kpm\n+zhjkIiICjE4JCLSsczrntNAxcxE+GpA1eaW0d3h0n2+y+nI3nQbTayfsf2OFXC7JBz2+REMqWhz\nS3C7JBw7M45fDV0sWl4aT6TxH26/HpmMgL1HjAfb62lzS2VnjcyyUU7JhmanHcGQOqu0M5nKNEwG\nq5oNWipx7kpn+xZbyS0REc0Pg0MiojztHhkbFC8+de+N+OvvD2HEH7aUQZQcttyNuZpIYTqqn9Wb\njsahJlLwtrvglETE4nOzi07JBm9bE4Bs8xoAiKhJBMPGmcK57yNbrnni7Ljl52huuqGj7KyRWTbq\nnoEluqWdyVQKyop27D8xt8y2lhmsajZoqdS5K53tW4idWomIqHoYHBIR5YnEEshkMnh579mSMof5\nJsOqYUOZQCiO773iw+9+Yg3uXrsEuw+NzDlm401evPjqqVnBkmqwh9FIsXJNI05JxCP3r8JoMFJ2\noGCWjbKJYq7ssbDM0illX0uNp9DRUvsMVjUbtFTy3NXI9i2UTq1ERFRdDA6JiPKoiTReOzQCW4nb\ny2LxFM6OTGLlslbT0j8A2H/iMlxOOz77YB9EQcBh3+is/YP7jl6eV1dSd5Mdj9y/EpmMYLoOPV2t\nTXjmhbcxEY6jwyNhzfUd2LmtDy7ZYfkcxbJRWkOWV94+jz2HrwbH2h7MTbf04PPblZpnsKrZoKWS\n52a2j4iIqoXBIRGRjlI7gYoC8I1/PYLOFhkupwPhqHkTmKHhMTx09w3YuqEX8WQKrx+5lHtsvuMq\nwtEkXtp9Bp/eshprVrRjn065ZqHOFhlqIoUL/qujNQKhOPafuIzDw37cM7Ck5KYshdmo/Ezh+JQK\nUdB/3slzE7P+XM3OoYXrrVaDlmqcm9k+IiKqNAaHREQVoO1LHJ9SLWXqxqdiePr5g5gIxw2DpPnY\nf/wSjp7yIxCKQ3YISKQySBsEvHff0gO7XZgVoOaLxVMVacpS2JDFaC+nVmbZ2eq01N0zP3icr0cf\nWI1UOoMjw2OYmFbRUcEGLWz+QkREjY7BIRFRnUzMNJgpdaYiAAgwzzCqiTTURHzm/42PXN7txs5t\nffhv3z5Y9DXn05SllPmHWpllse6eeqMhNq1bhofuWlHW2AntfMdOjyEYznaIHVjVUZExFgDLQYmI\nqPHN/187IiKqufmWnmqmowkEJmOYCBfPdmoZvXKUMv9wsL8LAAyDycM+P9REKhc8jk+pyCAbPP70\njbN4affpstaYfz4gG7zvGbpY9vmMaOWgDAyJiKjRMDgkIrqGBUMqIGQb1xTT2iyjSS6v4ERryGKm\ns8WJrRt78egDq02DyUBIxXd+cdJ0NISayDa3URMpjAYjuT8bKTZqotjziYiIFgOWlRIRXcPaPRK8\nbU2GzVLyBcMqnnnhbd19f8XIDhsGVnfN6k6ar80t4anHN8LjkgCYd/cEgAPvXTFeZyiGwFQMe4ZG\niu5X1FRzjAUREdFCwcwhEVEDcTfV9nd2k9NxvLz3DB65fyW2buzNzRo0ou37K6fUcuuGXsPHpqbj\niKrJ3J+1YNKMUSOfdo8Tuw5dmFNyarbuVreMdo9keL5KNLtpZFYzrEREtLgxc0hE1CA6W2TIkg3h\naLL4wRWSSiOXMXz4vlUYGvbn5g2aKac5TUeLE50WZv1pjWGOnjJvYGPUyGdgVQeOnR6zvO5UOo2X\n955BRNV/3/MdY1FJlR7rodfUp5zMMBERLQ4MDomIGoSaSJU0sL6ShobHcO+6pZabxpRTaik7bFjX\n14Xdh+aWlt6yqj0X9Ly890zRElcg27H13luX4sTZQG40xKZ1S3HHGi9+NXTR8roLu6JqnJItN9+x\nXrRg0O2S8JM3zlY8iCvWEZaIiK4tDA6JiGrIJgrYvK4HNlHEkVPjuaDmlpUdeP2IfkBTKlEUkC5x\nPkYwFEMqnYYs2SxlDssttUwbDFv89btX8PrQJXS0yJiOJSydKwPgN+5Ygc882JcLLHuXtuHCxQnD\n/YqF6zZrRNPstOPh+1bVLIOWnxW024RZGT1ZEhGLX/3sKhHEFWvCU+7YEiIiWrgYHBIR1VAqnYFN\nFPG5bQoeuf9qMHB2ZBJ7KxQcru/vgmy34eS5IIIhFe0eJ1xOO86Phg2f0+5x4vWjlywFhkDppZap\ndBov7jqF149c0n1cC3xKyZx2eORceWV+JlB22Awb7BSu27wRjaqbHTUr7Syn7FOvtNPldMy6XvmB\nYb75BHFswkNERIUYHBIR1di+45fxyP2rZwU1vd1uiILxPrpSHDs9hkQyg3aPhDtv7sHObX0ABPzg\n1WG8MzwKVSfQMNunV8jdZC+51PKl3acNO5WWa73iNQyKtPUNDY/lsrOD/V1z1m3WFbUwy2i2Pw9A\n2Xv39Eo7rQbJ8wnizN+7vCCb8FR6TyYR0bWGwSERUY3F4in4J6Lo9bpzX/O4JCzzuk2ze1bFk9kI\nMxCKY/+Jyzg/GkYklkBgSkWb24EOtxNqIomJcDwXNG0ZXGa4T69QOJpEKBJHm9tp6Xiz8sVSOCUb\n4omUYaCXzyaK2Lm1Hw/ft8o0WCgly2i2Pw9AWXv35vvZlFPemx9AGb336VgCL+89s2Aa07CxDhFR\nZTA4JCKqg8BkDN62plnBx5NfWI9nv3MIF/zTps91SjbIdhGTEWt78/IDzmA4ASCBLYNLsf32Fbmg\nSU2kTOcKFvrOL334L4+ss3SsWfmiFZ0t2WBwx+YbEY4kSsoKFZac6rGSZTQL4g77/BAMxmoUK/uc\n72dTSnlvfgA1PqWizS1hXV8nHtywDPuOX55VUhyLpxdUYxo21iEiqgz+Oo2IGprdJmBgVUe9l1Fx\nf/ejY/jKtw/gxV3DSM00aZHsdvzRb60t+txbV3fiK7+zEW1u/bl8Vhw7E5gVZGkZNMvPPz2Of/rp\nu4ios8du6M3La5LtaCuzRLHdLeOpxzdi59Z+uGQHWt0yJsOq4Ty+WDxZ8rw+Lcv47BN34K++dCee\nfeIO7NzaPyvjVGxvYrG9e0a00k6rnJINopANmLdu7C2pvFcLoLRfAEyE49g7dAm+cxNokvRvB4aG\nxxp+9mGxxjqNvn4iokbCzCERNbRkKoPjZwP1XkZV6GU3Wt2y4SxAzYH3RnHk9Bi6WpswEY6X9dp6\ne9UKM2iA8R7IDIAD713BkdN+3DOwFI/cvxI/+tXsUQvr+rogADhyagxBgwDp/sGlSCTS2Hfisu7j\nk9MqomoSLqfdtGxQa3hz7PQ4AlOxWY8nUxlL+9DMsozF9ucJgn4zHclhg9vlMH1No9LO5d1uRGLJ\nWdnMcrKngHkAZZapXgiNadhYh4iochgcElHDy1SgSUsjyy89NJsFmC8WT+OCfxo2UUBqJoITRcBg\nUsQcbW4Z8WQaaiKVCzIK9+nZbCK+/K39SJmcUys/9J2bmFW+Oj6lmr6HzhYZa1a045H7V8MmCnj/\nowACobmBrranzqxs8NEHVuOZF96Z8/raurT9llrAuGPzSoQj8ZLLU42CuPVKNuOq91gsnsJP3vjA\ntLTRrKxVL7B1ycbBppHJsHmTG6NmSOWOLKmlUpoKERGROQaHRER1FpgqyG6UEA2n8u7orQaGQLbh\nyNPPHdRt3JGfQduyvtfSQPoRv/VGOpJDRCaTwf4Tl3HyXBCD/V7c2u/VDSYH+7sAwLRsMJ5IGjby\n0QsY3zx2EWo8XXLTErMgTk2k8eaxi7ojJ4rtOzRrnmMTUZGsV6tbRptbMsw0G2WISx1ZUg+lNBUi\nIiJzDA6JiOrM45LQJGf/OlYTKew/caUqryMgG5ipiTTUxNW5grveuYBMJoPPbVNyx2odLXdsvhGp\nVBp7j1w0HbNRygiOeCKNQCI+6/Uf3LAMWzf26gZe45Mxw6zX+FQMQ6fGrb84Zs9ULKVpiVkQF47E\ndEeEANZLG600zzFSbISD7LBhsK8Leww60na2yBhY1YljZwKmoz8aldXRJUREZI7BIRFRnU1F4njm\nhbcx2O/FprU9lgfRl6LNLeGPfusWfOMHR3Qf12Yv2m2C7t6+e29dannURTmOnBrHs0/coRt4tbpl\nOCVRNysn20WELHZtNVLqIHm9IK7VLaPdI+mWxra5qzczsJQRDju39eP0yJRulnWw34udW/sX7JxA\nq6NLiIjIHLuVEhE1AC2L9ctfn6/K+Qf7uiA57LmMYSFt9mJ+R8tM3rpEUcDWjb1wStW54daya1rg\nNffG3mBWhAh0eMrv2pr/2vMhO2xobtJfRzWDFKPr9dLu03OOtYkinnp8I7asX4Z2d7aJTmHXU+PP\nf2FY6OsnIqo3Zg6JiGpMQLbbp57hc0HDLFm5lne7sXNbPy6Nmc9PDEXieOOIfnbwyPAYvv6lO/GJ\nO6/H91/x4cSH44gnzGtJ7xnoQSqdge+jCUyEs3P1JqcTs/ZJaswah0yGVagG2dREIo01/R3Yr9Pt\ntNlpx3QsqfMs669tlZpIIRLTz2BeCkTw5D+9hfVKd0WHshcb4aCXDbWJIh77uIJPb1nNDBsREc1R\n8+BQURQRwD8CWAdABfD7Pp/vdN7jTwD4AwBJAM/6fL5/UxSlA8AwgBMzh/3Y5/N9U+/YGr4VIqKy\n2EQgaRD7BcNx3H3zddj/7tx9h93tTowGY0XP75SyQ+3bmmXc2t+FnVv7YBNFeNtdhoGnTQS++f8e\nQ9xgYYGQimf++W2oiSQCobhRHm+W42cDmArH0eaRcdfNPbDbBew9ckn3WLPGIcXGSGy7rReyQ8TR\n0+MIhFQIQranj+wQ0dHiRiSWQDCkQnLYdEt2K9G0pNgw+0AoXvGh7PMZ4TCf/Y1ERLR41SNzuAOA\n0+fz3aUoyp0A/gbAJwFAUZQeAH8CYCMAJ4A3FUV5FcB6AD/w+Xx/rJ3E6Fifzze/2iAioiozCgw1\nj2xZDVeTA4d9fgRCKmSHCEEQigaGTsmGu9f24LfuXak7C0922HD32iW6XUFT6ez+NTOXApHc/1vp\nPzM50xkzGFKx78RlyA79jJlTsmHH5pWG55EdNgys6tRtpjIdS+CZf34HHS0yXE47AiE11+w1EIoj\nEIpjy+BSbL99BdwuB37yxgdVaVpiFsDmK3V/Y7mvyREORERUjnoEh/cA+CUA+Hy+A4qibMx77HYA\n+2YCPFVRlNMABgBsALBBUZS9AEaRDQpvMzj27dq9FSKiygtH4rnmGt9/xWc4IL5QLJ6CKAhwyQ7D\nWXiffbAPoiDgsM+PYEhFu0dCRE1WtIzViNF+x3gihXAkDpdsn9MQRWu4cuxMtiOpNo/PYReQSGZm\ndR7NFqPMdexMAJ9+oA+yw1a1piV2mwCX01E0OKzkUHaOcCAiokqrR3DYAmAy788pRVHsPp8vqfNY\nCEArgJMADvl8vl2KonwOwD8A+InBsUREC5sgQE2k4A9GcPJcsKSnljpTL55I4enn6/s7tTa3DLdL\nwou7hud03cxkMngtL9OpbVdMJK3PzigMyKpRUvmD104ZzlrMV+mMXr1GOCyUrqahSBwXRsPo7XbD\n45pf4yIiomtBPYLDKQCevD+LM4Gh3mMeABMAfg1Aq2f6MYBnAHzX4FhD7e0u2O2N+48YEVGTbMeB\n90fxzvtXMBqMlvz88akYkoKAXq8Hk2EVH16awg1LWnQDkl4AsXgS3vamsl4rn5bRa/dICOqMczAT\njSfx1/9yGBd0BtZr8x/no6utCatu6IRTqs4/ebF4Em9ZzO5uWrcUvUvbKvr6/+WzGxCLJxGcUtHe\nIlftfQJAKpXG8z97FwdOXIJ/IgpvWxPuvGUJvvjQzbDZijfa8Xo9RY+phHg8if/6D2/gw8tTSKcB\nUQRu6GnBN/54M6Qqfj4LWa2uDZWO16ZxLcZrU4+/IfcBeAjAD2f2HB7Pe+wggK8riuIEIAO4Cdkm\nNN8B8DKAHwJ4EMAhk2MNBYMRs4eJiObNrBOpFZ2tMn6+/8N5reEr//gGUhkBkVgS6Uw2cFvmdePJ\nL6yHZL/6176ayI6vuHFJy7yCw3a3jIHVnfj4bcvhbnLgL761X7dM1SmJuOvmHrz17pVZjWGiampW\nYJgvqhbvNlrMwKpOhCajCM37TPoujIYQVc1nU3a2ZDN6D921An5/dVZiB6r6PgHgxV3Ds8pYR4NR\n/PSNs4hE40Ub7Xi9nqq990JPP39wViY3nQbOXpzCn/7tXnzti7fXZA0LSS2vDZWG16ZxLeRrYxbU\n1iM4/DGAbYqi7Ef2Pup3FUX5MwCnfT7fTxVF+XsAbyA7g/FJn88XUxTlywCeVxTlPwOYRrbD6WW9\nY+vwfoiIcuYTGDolEdOR0rJueiYjswOVdAY4PxrG1797GF/74u1IpdP419dOYd/xy7kgzSYCNlFA\n3KBcUxSAJtmGqJpC4SSKYFjF3iMX4bBnS1a7WptwwT93bEZXaxM+/UAfjp4e0+0aWiniTLfSjpba\nlFhCMO/d+qePrIVyfUdDl19aUc7ojHoIReIY8ev/smHEH0YoEmeJKRGRgZoHhz6fLw3gDwu+fDLv\n8W8D+HbBcz4AsEXnXHOOJSKqN8kmIJ4qPUxUE+mqNobRbox/tv/DWfv4AK1bqfGa0xlgOmYe0A0N\nj+Ghu28wnPcXiSXhn4giUELZqVPSHz9hJgPgzz9zK1Yua61JsOJtazJcp1OyLYrAEJjf6IxqMNr3\neGE0POcXGJp0Jvv4TTd01GiVREQLCwvviYgqrJzAEADa3BLS6Qwmp/WDq/lKZ4APLk4aZn8A47JY\nbU+hmWAohgujYcM9hxNhFfFE0tK5NHev7UEmA+wdGrH8nA6Ps2aBIZBtcLNpbc+cgBsANq3tadjA\nsNSmMo0yOkPrYFvYvOjRB1bDJoro7XYbfo+JAtDb7a7JOomIFiIGh0REVSI7RMQTaculptOxBOKJ\n+RSmmhMFwN0smY5bMHp1K4FZu8eJ3m63aQAhOeyWztWZd8M/PhnDnsNzAy8j9Rjj8JkH+yAIQjZg\nCano8Fxdf6MpFlwZaZTRGS/tPj1rDVrzIgDYubUfHpeEZV63bvfYZV52LSUiMsPgkIioStREGg6b\ngITFTKJeYNjZIsPldGA6mh3oPp+GN0u9zXjj6CUIM3vyjPR6mzE2GS25xHWwvwsel2QaQLQ2S2h1\nS5gMG5eW3n1LDx7bruSCjVa3jE6DgNMp2dDstM/MbHRi07qleOiuFSWtuxIKR4Q0yogHvexgseDK\nTL1GZ2is7nt88gvr8fXvHsaIPzynKRMRERljcEhEVEVWA0M97W4ZTz1+GzwuCWoihcBUDL/49UfY\nf/xy0eybS7YjFr/arXSptxmZTAZ7j1ws+roX/NOQHeZNVvJ1FjR+0Qsgbu3rRDKdNuxkqnFKNuzc\n1jXBe+cAACAASURBVDcrsDLLWN0zsGRWQNa7tK0u3ePyg7Ba7rszYpQd3LF55byaytQ7CLa671Gy\n2/G1L97OOYdERCVicEhE1KAmp1VE1SQ8Lgmyw4Y9QyN485i1eXoRNYl2twM3LmnF57f342f7PsSe\noeKBoUYtUt7a2SJjYFUntm5cjo4W56wAQS+AeHnvGfxKZ09eoXgihXAkAZfsmPV1s4yVTRTR6pbh\nn4giKYiwZ9I1C1jKLdGsNqPsYDSWrEhTGdlhq3kQnEqn8crBc4aZc8lhm7Pv0eOS2HyGiKgEDA6J\niBpUu0dGPJGCmsh2wTRrJKMnGE4geGoMrW4JQ6fGKrq2gVWdeGz7GgDZrNloMDIni6QFEGoihcO+\nUUvnNWpsYpSxSqXT+JdXfbPGcjglEXevXYLPPthX9QBtPiWa1WL2eZ88F0S7R9LtGFvLpjLleGn3\n6ZJ+wQGU3nSHiOhax+CQiKhBTccSePr5t9HRIkNZ0W6Y8Snm8LC/4h1Qj50JIKIm8ZM3zhbNmk2G\nVcvjK4o1NskPOEeDEbzy9vk5zWpi8TR2HxqBKAhVDdAace5fKp3G91/xGX7ewZCKO2/uwf4TczPQ\n9WjkY5XZZ507Jp7KZT4bNaNLRNToGBwSETUYbWaetjdvfErVvZm3qtzAUHaIUBP6+wODoRh+8Oow\n9uWtyyhr1uqW0WGQrdJ0eGSsV4p398y/6R+fUiGabI087PPPCdAqmUlqtLl/QDa7ts/ke6Xd48TO\nbX1wOe11aypjVf61MvusNR0tVzOfjZjRJSJaCBgcEhE1kD95eC2+98rJkge/l+u69iZcCUZ1H4sn\n02hzS5jQ6Sza2izh3Q8Dus8rzJrJDhvWK926DWUAYNMtPfh8XndSM4U3/WaNeYIhtaqZJLO5f5LD\nBrfLofOs6rGSXRvs74JLdjRkZ1WN3rUaWN1lWA6r0TKfjZjRJSJaKFhbQUTUIDo8Eg6+fwXBcGVL\nQM384Y6b0dmiv8+sw+PEYF+X7mORWFI3aASuZs00aiKFLYPLcN+tS+CUrt6UOyUbHtywDI9/Yo2l\nm3UrwU++do+MJtmO0WAEL+46hV3vXMD4lIoMrmaSXtp92vL5CmldVPXE4in85I0Pyj53OYpl1+6+\npWdWdlAr0dU+e61UV9vjWi/aLwDyr9WewyNobtLvNuqUbNi6sTf33qxkdImISB8zh0REDSKeTOPA\ne9Yat1SCKACvH7mIW/u68JpOJ9FcN1CbmCtBlBzZklc1aTyOQmtsopcBuuuWHty7bglsoghvW1Mu\n0zM+ObehTSErpYX5mpsceOaFt03LT+ebSdqxeSXePHZRdzxHrbNUZpnMzhYZj21XdLOkjbQ/z+wX\nAJFYAlsGl+LYmQCCoRja3DLWXN+eLZPN625r9jk0etMdIqJ6Y3BIRNQgIrFkTV8vnQH2DF3EAxuW\nYevGXsMxEVoJoj8YwTd/dKxoyatW3vfiruE5+772HB6BTcw2ikml03hx17DloMTspj+fU7Khq82J\n86PhWe9VT/7ewHL2I4YjcagGcxtrve/QbB7kYL93VoYw/31a3Z9Xi86f5lk/FdtvX4FPP9Bnug7z\nz6Fxm+4QETUCBodERA2i2GD7+ej1NuPi2LTuaxw9NYZnn7jTdA+a7LBBcthMM3dtbgkb13Tj0QdW\nm2aAtEYxL+89U1LTELOb/i2DS7FlfS/a25uRjifwzAtvG64zX7vHCbdLygWp41Mq2twSBvu6sHNb\nf9HMWaNlqczmQRrt5Tt6ynx/nt0m1CyzaOXztDJj0exzICIiYwwOiYgahChUPkDUuoBuGVyGJ7/9\na91jxqdUfO8VH373E2tMb7pNb9zdMr76xdvgcWX3hY1PRgwDyUBIxXd+8T6Gz0/oPm5Wjml2028T\nRXi9Hrw7fMVy+elgfxd+8sbZWQHnRDiOPUMXcXpkCk89vtE0AGq0LJXRPEgAhplcI1rmc9ehCzXr\n/Fmpz9PscyAiImMMDomIGsR17S5cCkQqdr7/+plbsXJZa25fX6dJSeb+E5fhctpzN/t6JYRmN+4b\n1nhzgSFQvATUbG9lwKQc08pNv9lriwKQwUyznf4u7Nh8I55+7qDuOs6PhvHirlN47OOK4VqBxsxS\nFWbXzDK5Rr+UaPc40STba975s5Kfp5UsIxERXcXgkIioATglG7782Hr8bN+HOHTSj+A8Oyq2uyWs\nXNYKABgNZpu9GAV2mkMn/fjEndfj5wc+mlN6uHVDLzpanJZv3M0CSY1RUCIAeOXgOdOyTrObfrPX\nvu/Wpdh++4pcUDkajJjuYTwyPIZPb1ltGgDNJ0uVH4QDqFqWy2wvn1G2erC/C1E1WfNZjsz6ERHV\nD4NDIqIGcM/AEniaJOzc2o+H7r4BTz9/0HBUhBURNYm/+t4hRGKJXJCnrGjHHTd149fv62ftgmEV\nX/7WW4jndSLVSg/3HB5BZ95eMys37o8+sBqRWBL7DYayGwUlWqMcURTwuW3ZrF2pzVAefWA10pkM\n9h+/nGug45RsEEUBna3OXNDZ6pYNZzkCwMS0ajkAKiVLVbj/T5ZsADKIxdOzPudK7ekr1sl0YFVn\nrgtofsCfTGXqtqeSWT8iotpjcEhEVEfansAdm1fmMnweV7axi1nWLZ8gAJmCQEtNpGd16xyfUrH/\nxGXIDhGyQ4Sa0O+wGTcZUVG416zYjbtNFPHYdgW+c0Hd4MIpibojIDT7jl/Gp+5diZ+88UHJzVBs\noghREGZ1Vo3FU3jt0AgEQciVz8oOGwb7urBn6KLueTqqFAAVdgjNX2c19vQV62S6c2u/bgBuE9FQ\neyqJiKi6ajvAiIiIcgQAn/t4H1LpDJ5+7tf4y//nAL7y7QN4cdcwHrl/JbZu7EVnixOikO0EaqQw\nMDSjJtKGgaFVQ8Njlgelmw2KLyYWT+H7r/jKGl5vtseucP07t/Vjebdb99hqBEBma8tXyudsxaMP\nrJ71PdXZ4pw1PF7L1BW+32LPW0zURAqjwUhFP3ciooWEmUMiojrJAPiHl0/M+lph1kgr32yS7fiL\nb71VdMagVbJDhOSwIRRJlPzcUvea6e1TXLOiDfsMyk3zvf9RUPfrxZqhmM/Lm7v+vuWtM0FBNnB2\nSjZsWttTlQDIbG3F1jkf5e7luxb2AOqN+ajWuA4iokbG4JCIqAG9eewSdmxeCZdsR3e7C6FIHOnM\n/DJ++bQMol5JajGl7jXTCy4A4KRBuWm+yWn94LVY4FTK/MGXdp/G7kOzRzrE4ikIglCVwKBYJ1ej\ndVZKuXv5FvMewMIy32qO6yAiamT8dRgRUYUIyGbkKiEWT+EHrw4jlU7jxV3D+OrzbyOeqPAQRJQe\nGALll1rmly1aLTdtNwiOigVOZufPX38p5aeVYvW9c09fbdTje6DYeljaSkT1wswhEVGF3HZTNw4a\ndAItx8lzQby465TpoHIzbqcd0XgSqQolHLXmOaWWWhp1Gn30gdVIpdKGzWBkh4hb+7t037+VwMnK\n2I1Sy08rpXBtkhasxlPoaKn/nMRrSb2+BwqxtJWIGgGDQyKiCvnoylRFzxeYUnFkeKys5y7zNuMv\nP78eTz130NL+tmIEAfjTT69Dr1e/cYseo5vdHZtvzK4pk8GOzSux/8Rl3SY5giDg4ftWwSYKJQ9E\n1wLSh+9bZbpXrpTy00oyKrVdrHv6Glm9vgcKsbSViBoBg0Miogq5EohV9HwtbgkT4dICuxaXHRuU\nbuzc1o/xyRiCFgNDo4H0mtZmCa3NUknzBo1udvccHkFq5sVkuwjVYHyGGk8hHInnZj9eGA2jt9sN\nj8u4c+t0NI7n/u09nDwXtJR9MR/xUP2yzsJ9fIt1T1+llDrv0op6fw8AxUtbzZovERFVEoNDIqIG\nFYkmIEu2kjqUKivasHNbP2yiCLdLglxklqCmWHA4EY7jL761H4AwU/poHnSZ3eym8l7IKDAEgI4W\nJ9wuB17cNVy01E7LUu47fglRtbSZgVbKT6m+ql1yWe/vgUYpbSUiYnBIRNSg/n/27jy+jfu8E/9n\ncMwAIMCbtCRSsnVQI1+UKMmybFmxpUhRk1Zb/dbZOFasbOLUm3bbbn7d/n7t5mhUu0mPtN0m3W66\nqddeJ06UOo1T1WnTKpYly7JkWQcpUrKtISk5kklK4gGQBAhgBhhg/wAB4ZgLwAAEyOf9erWxiYMA\n6Jc0Hz7P93kichwROb+hFGcujeOG9yy++pmNOHj8iqFgCADRGMDZGYgaQ2/Snys9dCm1bRpd16Cl\na3UzDh5/31CrXXaVMluy+pJ8bZmL3uf/qoZqV+qWy7n+b6BSWlsJIYTCISGEzBHWxgBgIGlUzwrx\nwWgAL/5cwDtXvHk97u47mtA9kN8Zxzf7rqNbGIXPL2VUc4yua0hX72YxPSOlqjZ7tq7A/ufeVrxv\nequdGJHRLWgPAvJOh/H9Q4Jmu6neqoZStDQSfUZbLs34+czVuo5KaG0lhBCAwiEhhMwZKRoHYP56\nCgA43z+B6aBk+P4WBnhiF4/GOgd6+sfhnQ4bemVhSU61vWZXc9QudpXU17D48r4NkGPx1MX9qC9o\nqNVuKiDC69d+rxxrxYmLN1L/nk/lyWhLI4XH0tBrufROh3G0Z7jqp3zOdWsrIYQAFA4JIWReyicY\nAkBbixv1bi7VWjfmC+Kv/qEPPn/+raHJao7Suga185OTMxL+9AfdqYt6wHirnZOz6Z6Z1HutgPqk\nUK2WxkcfXgnvdBiHz36AvssTVR1OKpXefweHz36QsQ6lWqd8znVrKyGEABQOCSFkXtIKS04uEdLi\n8cT92lrc+PKn16du5+xWtLd6cNftDRnVNqPSq3rpF7tuF4t/fOMyTly4oRgSsy/qtVrtOlc2pi6c\nQ2JUMxiu62jC+YEJ1df64iEBQla76Z6tyxEIRuDkbKotq8d7R9DTP5YTWqo1nFQqzf8OVjWhb1C5\nFbpap3zOVWsrIYQAFA4JIWRe0gpLX3xiA+pqWLw/MgV3DYu2ZjdYW+4F9OM7V+PMpdG8z0RmD9BI\nv9j91E4eH39kFUbGAvjrl/swNRPJeXz6RX2yitgtjMHrF1Oht+/yBA4c7k+db2xSqSzVu1nUONT/\nqmPtVpxUaDd9s+86RElGnZvFZEC5CitGYhAj6pVVM8IJtaomqLVcbutqw+vdw4qPoSmfhBCSPwqH\nhBAyz3D2RCuj0mJ5AHitewh2q0X3jBZnt6C1wYmhsZm8vr/eAA3OboXdZlEMhkDmRX2y1U6OxXG0\nezgVerOrc2s7mnHkXG5ImAxIeOviTdXXEo8rp+hkZVMtGBpRTDgp1eqGag2bai2XYkSmKZ+EEGIi\nCoeEEDLPqIXCpLffuZnR1pkMWrIcwye2d6Quvl8+djnvYLi01Z0xQCM7jKSHHjUNHi7jol6MyLqt\ng4zGa9Kqoup9VsVQCidGw5nZqxtKvSewXLJbLmnKJyGEmIvCISGELDBqQ2GO9ozg5MUbkCIxNNZy\nmAkrV/a0BMNRROU4AOUwEo/H8ZpChS/dTDiCl49dTgUXvWmVY5MhnM9zBUdSoYNsjEgPJ/mEM6Or\nG/JR6j2Bc4mmfBJCiHkoHBJCCElJVtLy2U+YLtlKefjckGIYcbD6oSYsxTKCi960SsTjquFRTymC\nYVNtbjjJJ5zpheF8W1VLETZLodCWV5rySQgh5qFwSAghJC9NtRzi8bjibsEGjwNOzqYaRtSqlkrO\nXRrD7gfvgMfFarYOtjS4VMOjURYGiMcBu90CKc9WUwZAY60DnauasGNDO9xOO0JiooJqteQfzoyu\n8DDK7LBpNrNaXmnKJyGEFI/CISGEkLw4HTYgDsVw2LW6GSExWlRQS/IFROx//jQ2rmnFxx9ZAUC5\nddBqsaiGx3SszYJINAalYmEsDvz/n1yHxc01+NLfnTIcYptqOXzh451oaXDBZmUUQ862rra8wpnZ\n5+jMDptmm88tr4QQUm0oHBJCCMnL0KjykJrkMJqoHIeDtSAs5VbgrBZAzqMwNxmQMoKCWutgsoWz\n7/IERn0hxefafE8rTr87phj8LAxwtn8Me5fV46HOxbpBM6lrdQvaWz0AgAOH+xVDjizH8g5nZp6j\nq+ShLdXS8koIIQsFhUNCCCGmCIajECMyXn79smIwBACrhcF9a1oxMDQJn19Eg8cBl8OGD0YDms+d\nHhSUWgeT584+/6gT/VfGcfjsB+gdnJj9HizuvL0RDGNRrQjG4sDR7mFYLYxiMFvX0YQ4gN6BCcWw\nphVy+i570bmqGUcV9vGphTOzz9FV6tCWSm95JYSQhYbCISGEEFP4/GEceHUgY6l8Nikax6l3b6LR\nw2Lz3Yuwd2cHOLsVLx0ZxPHeEdXVEkaDgoO1YXFTDfbtWoNHH4ngwKsDuHTVixMXb8Cite9iVjKE\nqgWz//CI8tAUvZCzY0M7rBYm73Bm1jm6YsNmqfYjVnrLKyGELDQUDgkhpArZZud0REu3pi9vDR4O\nl656Dd3X65dw8uINuBy2VGg5d+mmajjM3n1oxMHj72cEVSOTSdNDqFIwUwtrWiGn3s1BjsXx6MMr\n53yiptLr1wp+ciyGA6/2o2dgHJMBCU0m70es5JZXMv+V6pcehFQzCoeEEFJlOJsFViuDoGh88mc5\nrFnWoFk1VJKs1E0FREwG1Pcq3r7Io3pb+gVe+tfU2jy1FFqt0go5QTGK/c+drrjF83pTQuVYDM+8\ncDaj5desYTHpP7NKbXkl85dZE3IJmY8oHBJCSJWx2SyYCUfn+mWkOFgrHupcjD1bV+DSNV9ek0on\npsMY8wV111F094/jK8+eygkv2Rd4W9a2YfcDyzTbPLUUU63KDjms3YqwJKfOOVbaFE69KaEHDg+o\nngUtdFiM1kX5XFdVjaBK0/xAE3IJUVfWcMjzvAXAtwGsBSAC+DVBEAbTbn8KwOcBRAF8TRCEf+Z5\nfhmA52dfKwPgPwmCIPA8/zsAfg1A8lfDnxcEQSjfuyGEkLlRjmBosTCIGdwQX+OwpYKCkZUS2b71\n4z6s7WhOrMjQCHTZF3BKF3ivHL+CYEjCow+vVA2bFibRYupgExf3UkQ2pVqVfq5vzBfEt37cpzgA\npxKmcOpNCd394B043z+u+njvdGHDYvQuyit1+AxVmuYPmpBLiLZyVw73AHAIgvAAz/ObAfwlgF8F\nAJ7nFwH4LwA2AnAAeJPn+VcB/BGAvxEE4SDP87sA/AmAfw9gA4BPC4JwrszvgRBCKloy/BQjnscT\n+PxiKiikV8+80+HEc+k8fmJaxJFzuZM81STDi94FnlpQfXjdEuzatCzVPqpXCcq3WsTZrWDt1oqb\nwpn+PvQG6AyNBjAZUA/qdW427/bbsBSt2otyqjTNHzQhlxBt5Q6HDwH4NwAQBOEUz/Mb027bBOCE\nIAgiAJHn+UEAnQB+F8DU7H1sAMKz/7wBwBdnQ+W/CILwJ+V4A4QQUslYGyCZUFjMJ1umn9PLnor5\ns7ev4o3z14t/QWm804nwoneBp3SWrXNlI3ZsXJoR9NQuBIupFmkNqKmr4eDkyvfXr9L76FzVjAYP\nC69fyrl/g8eB9la3ZptvV0f+7be+6eq8KKdK0/xCE3IJ0VbucFiLW0EPAGSe522CIEQVbvMDqBME\nYRwAeJ7nAfwFEtVHAPh7AP8TwDSAf+R5/lcEQfhnrW/e0OCCzUZ/gBNC5i8zgiGQ6OE3GhC3rF2C\n9iX1CEtR+KZFNNRyaLRa8K+n38Glq5PmvKA0Ds6KtXcuQkuDU3HhfXO9EyvvaIKDteELj29AWIpi\nfDKEnx6/grPv3cTr50fQUu/E5nsW48ndd8NqVQ56f/tyr2K1yOVk8dSee3Vf55a1bXjl+JWcr/sC\nIr7+4jnd72+WZw9eyHkfR7uHsWJJrWI43LJ2CVbc3qT6+lcsqcUXHt+Q9+sOS1FDP7NKc318Bl6/\neqi1sna0NNeU+VWZr6VFfejTfKP233byz7JKs5B+NtVmPv5syv2n8DSA9E/RMhsMlW7zAJgEAJ7n\ntyFxVnHf7HlDBsA3BUGYmr39XwB0AdAMhz5f0JQ3QQghlSifQKdH7XmWtroRDEczpkp+7P52fOuH\n5zIqUy6HXXexfcGvLQ74p0PoXNmk2DbaubIJ/qkQ/Glfe/m1/oz7jvpCqfOJ2W2BciyGA4cHcKxH\nudX1RO8IPrppqW61aPcDyxAMSejpH8fEdDjjNq3vbyYxIuNEr/L7mAqI2Na1BH2XvRk/z90PLMPY\nmD/j9Xv9YdTXcFi3uhl7d3TA653J+7W0tHjy+plVCjkio9GjXmmSpQjGxirxlRvX0uKp+veQj/T/\ntpX+268kC+1nU02q+WejFWrLHQ5PANgN4EezZw4vpN12GsDXeZ53AOAA3Ang4mww/BaAXxIE4ers\nfWtnb7sTwAyA7UgMrSGEkAXLrGCo5NZE0uW4ORGEPxjB8iW18LhYHDjcn1OZymdiab6kiIyxyRC2\ndbUhEpXx9rujqcEvTs6GeDwOORZDVI5jKiDCydnyagt86cggjnarn4FMb4HUOo+YbLHd/eAd2P/8\naUwGcqt0pW5L1D5fJWLXpmX4xPYOxfeQ3SJsxoTOalxbQbsY559S/LdNyHxR7nD4jwB28jx/Eolf\ncn+W5/n/CmBQEIRXeJ7/awDHAVgAfFkQhDDP898EwAL4bqKzFIIgCJ/nef5LAI4iMfX0NUEQflbm\n90IIIRVj7aomvPcLL6RoaSJijcOGqCzjd//mRGpRvYO14P67F+HCoPpUy2KwdgZSJPf9sHYrvvmj\n8/D5JXCsBWEplrotJEbx2rlh9H8whWA4Au+0iHo3B5/KcBVv1lk3MSKjWxjVfF0NHgfcs6HYyHnE\nkBjFlEIwBEp/1s7I+SrObtX8/nq356NaL8qrMdQSfWb+t03IfMHE46X8XXNlGRvzL5w3q+LJPz0y\n1y+BEEJSGCbRJpqtqZZD58omHO0ZKen3tzCJ6aV7dyZaO1/42SWcuHhD8zHbupbAarUoVpJ2bGzP\naRMVIzK+/HdvKZ7va6p14GtP3V+ygCRGZHz/kKD4npReaylVcwtW0nzdczgffjbzFf1sKlc1/2xa\nWjyM2m2Vd/KbEEJIVSnmrGNbcw2GxnLPr6WqcFZL6sxbXQ2LsBTNqBQWKxYHjvaMpIar6AVDAOgd\nHEdQzN1fCOS2icqxGF4+dln1/qVqS0yfUDoxLcLBWgAwBe90nK+hKF9UaSKEzHcUDgkhhBTF2PCa\nxJCamVAEk4FEq2eN046ZUKKaltzN2JTVnvnY9lWQ5Rh6BsYVz+zlSy3IdgtjYFR/j5pJqQKYlN0m\nmr0fLyl5hjM9oJkZwLK/bzJQP3jPIuzbxRt+flr+TgghCwuFQ0IIIUWpr2HRtbo5Z+rlY9tXpYbC\nJANPMgAdOvNBxtCX2Gxi61zZlNHq+NKRQdNaSxs0zh76VFYV5P090vakae3Hq3HY8OjDK2G1WHQD\nWL6hUev7CtfyWy1Cy9/JXKKKNSHlR+GQEEJIUbr4Fuz7CJ+6kHNyNoTEKKJyPKcNj7NbUefm0Kcy\nxKbvshdiRE4FSbWQo8XttCEQyl34uG51M/oGx1WGs3BgGBQ9ZTW9TVRvUmiywqgWwGLxOCwMk3fV\nTvv7Gh+AQ8vfyVyhijUhc4fCISGEkIK1t9Rg744OAIDNyuDwuaHUBV29+9ZevPQLOqPhRet+Wji7\nFfetaVWsZFotjGKb53q+BQAUb1NjtSAxCdUvKp7jMzIpVCuAnbxwI7WiAzBetTPyfY0wK2RqocoQ\nUUIVa0LmDoVDQgghebNZgY18K57YxaeC34HDAxmtor6AiKPdwxgcmsJXP7MxdT+j4UXrfqyNUV3b\n4Z1W39+ntJKgc2UjtnW1wcnZ8GbfiOGBN3Is0Qa7a9MyxXBjZD/eqC+oGsDSg2E6vaqdWXv5zAqZ\nSqgyRNRQxZqQuUXhkBBCqpjVkhiwEjNvgKchURl4+91RDAxNYV1HM8SojBN9ypM+PxgN4MCr/di3\naw0A4+FF635SNA6rJRHQsnGsVXV/X/qePe90GIfPDaFvcByv94yg3s3lPQm177IXn9jeoXqxqrcf\nTyuAqTFStTNjL18pl79TZYioKUfFmhCijsIhIYRUsXo3h7uXN6C7f1zxnF0pxZG4qH/t3LDufc8J\no7j9NjfuXt6Epjon9mxdgVA4ikvXfKptmQCwZ+sKHO8dhhjJrRIqBUMAiERlyLHc+2e3MB7tGc6p\ndOZL72JVb+m7VgBzsFbF6qGRqp1Zy+ZLsfydKkNESykr1oQQfRQOCSGkik1Mi3ijV38331ybDkbx\nwr/1A0icTax12eHzS2jwsNh89yLs3dkBF2fPeIwci+GHr/YrBkMtcgz44av9eGIXj6mACLfLjoPH\n389oYexc2YS+yxOGn7OYoAZo78dTC2DxeFwxeOdTtSt2L59ZITMdVYaIllJWrAkh+igcEkIIydHg\n5rC2owlvnB+BQhGuKFE5ntoV6PVLOHnxBlwOW0474UtHBjWX0qvtLASAs8Io3rvqhc8vgcsKdhPT\nou56jHo3i+kZydSgpkYpgAGAdzqMWCyuOFgnX8UOfjFz+TtVhoieUlSsCSHGUDgkhBCSoa7Gjj98\n8j54XCwsFgZHDLSNFiu7ndDIGotFjS5c9wYVbxMjMYiRRABVG+yipqnWga9+ZiNCYjQVpuRYDAzD\nmHKxqhbUOLsVTXWOnEEtnauasWNDOxprHXkHu0oc/EKVIaKnFBVrQogxFA4JIYRkqK3h4HGxAIDH\nP9wBC8OgWxiDzy+CtVsgRsyffpPdTjgVEDWHtHB2C373k534yv8+k3f409O5qikjGALmXKwaCWpK\ng1qOdg/DamEKGtRSqYNfqDJEjDCzYk0IMYbCISGEVCmtdQ7FCIYjGBr1o2X2omzHhnbsfvAOhMQo\nWLsVX/q7t/Ke6qknu52wzs2h3s1iMiAp3l+KxhCVgYc6F+e1m1ALZ2fQ2lCD3oExvN49rBjezwmX\nEwAAIABJREFUirlY1QtqZg9qqeTBL1QZIoSQykThkBBCqlRUNj8YAonQ8tXnz8DBWgAwECU5FZS2\ndbVBNDkYAsDajqaccLBmWQNOvXtT8f6NHgecnA3butogy7HUubzmeiemZ8SCwms8nli7kWRmlc1I\nUDN7UEs1DH6hyhAhhFQWCoeEEFKlzB4Uky09YCWDkhyLG9rLx9kZMIwFYUnWHByTxMz+b3rr5cS0\nqLrL0OWw4ZkXzuScy1u9ohnfeblXsZqoV2lVu82MKpuRoGb2oBYznk9vkE2xg24IIYRUFgqHhBCy\nwBTTjto3OIHOlU260z4T6ydkbLlnEZ7YxcM7FcIfv9iNGVF5F+P5gXF8/JFVePnY5YxglwyGnN0C\nKRpDo8cBl8OWU+FLnsvrXLNI9TzbA3cvwh9992ze79mMKpuRoGb2oJZink/vfGQlDrohhBBSPAqH\nhBCygDy8bhEYxoLXdcKdGq8/jK7VLYjGYjhuYL/ipWuTAACr1aIaDBPPK2JsMqTaeulkbfi9x+9F\nc70Tz7xwRvE+Pf3jCEtR1fNsL/5c0HytnMqwHSNVtmQFzcnZcobZJJ7bWFAze1BLoc+ndz6yUgfd\nEEIIKQ6FQ0IIWSAYBvjwhmX45o/OF/4cAP77S71wsMaqWN70lkkPm9pvmK3RwwHxuGrr5eSMhP/x\nkwu4645GzfZM37SY+ost/TybGJHRNziu+jrbWmrAL6tXXNuhVWXLboO1MIl230YPi/V8a0YlzUhQ\nM3tQSyHPp3c+cveDd1TsoBtCCCHFoXBICCELRKPHkQhgKgHNiOQ5R8PrI+LAodPXsHfnaqznW1Un\ni3atbkFLg0vzPONkQMLJizfgyFpqn9TgcaChlsP4eCAjCMmxGF48JGiek/zPe+5Ba4MTljx3GWZX\n0JKfj9cv5VTS8glqZg9qyef59M5HDo0GKn7QDSGEkMJQOCSEkAWia3Uz6twc6mrsmJqJlOV7xoHU\n+cRt69vx3i+8GB6/tbjeamHw8LrF2LN1BaYCIjpXNeNod271zoh1HU148Wfv4UTvcMY5uHg8jpMX\n1Vtgm2o5NNY68q6yaVXYkpQqaWpBrVKGu+idj2xvdZs6OIcQQkjloHBICCHzlIO1QorIaPA4sK6j\nCbF4HM+8cKZswTDdsfMjikNs5Fgcp98bRe/gBLzTIho8LJa2uhEIRuALKFenRCkx6ObStcmMCl8s\nHscrx6+k7pc8B6fXAjsTjuDlY5dTLaBGq2xaFbYkI5W0Shvuonc+0uNiTR2cQwghpHJQOCSEkCrH\n2SxorHVAjEQxGZBSYWnP1uUIBCOoc3N4+dhlvKazLN5mZeBiLZgOGWwZzYPW2o1AKIpAKDGsxuuX\n4PVL2Np5G95+dwxSNHdATGOtA0/s4gEAY5MhIB5HnZtTHVSj1wIblmI5y+iNVPC0KmxJRipplTjc\nRe98pNmDc0imSqkiE0IWHgqHhBBS5cRoDNe9QWy+qxUf23w7WhpcqQtKF2c31P4IAFE5jnCkxMsT\nDTpzaVwxGAKJ6pTNymRU2+rcLCYDhZ+lBIBuYQxyLI6+wXFDFTytClv6ay20NXUuh7tkt9gmp7BG\n5TisFvMH5ywkWsGv0qrIhJCFh8IhIYTME6feHcXA0FTOxaSR9scktUCmpcZhw0xYfU1FIdSqfQ7W\nij1bl+dU27SCodoAm2xev5hx3tFIBe9WBS17WimH9XyLbiVNb/jLXA93sVkZHD43pBpWzB6cM58Z\nCX6VWEUmhCwsFA4JIWQeSV5MyrE49n0k0XpppP2xUBYGqHOzpodDNVJEhndaNFQJTXrw3kWwMAze\n7LtufMpqGq0KnlqFzWglTW/4S77DXcxuR6SwYh69z7JSq8iEkIWFehQIIWQeOtYzjBcPXYIci6Xa\nH0vBxdkQCpdvwE1Dch2H1jk/NwcLAzTVOrBjYzse/3AHHn14JWochf0+dGI6DO90WPM+yQqax8Wi\ndbatV4zIGPUFIUbUA6nWzyaf4S5yLIYDh/vxlWdP4YvfOYWvPHsKBw73Q47lXwlO0gsrWu9L6zn1\nPpP5yMhnqVdFHvMFF+RnRwgpL6ocEkLIPBSL31ohsWvTMuzZuhyyHMOx8yOaw2HyFShTxTCpa3Wz\n5j7E1gYnvrxvQ071bmIqaLi1Vsnhc0OpSixg7rkxM4a7lKLCZ2bLqxyL4dmDF3LWjCyUs3RGPkut\nKjJrt+JbP+5bkJ8dIaS8KBwSQsg8duz8CF7vGUldUG5dtwTHFFZKlAvDAPE8wynDAI1pE1inAiI6\nVzYprsbYfM9ieFwsPC4WwK0Q5+RsRbXW9g1OQNwm5wzCMePcWLHDXbSrUmMFtyOa2fK60NtTjXyW\nWgOOwpKcaoleaJ8dIaS86FdOhBBSpVgbg813tWreJxZPLKJPXlBGIjFs6VwEpjwvMUe+wbDRw+Lp\nJzfh6c9tghyL4w+ePY0vfucUegbG0d5Sg0ZPZgvpk7vvBpDbZvnMC2fgctgLft3J6k4y5ExMixmf\n60tHBgEU14qZbE3NN8hNBUTV0DsxLWJKZV+kHrNaXkvRnjoXimmJNfpZPrZ9FXZsbEdTrQMWJjHY\nSG1PZzV9doSQ6kGVQ0IIqVJSNA4xIuNDnYvx5oXrhtpFT168AQuTCIzVoMbJYnGTC8+8cBYfjAZS\nX58MSJgMSGhvqcHvfnIdGmsdAIBRXwihoISXjgzi5MUbqftPTCcC1NJWN2ZCEXj9+QWmBo8DTs6m\nOzBkLqaPOjkbGCj/TJnZ2wtlRstrpU9k1WPWegkjn2V2FVmKxrD/udOKz1cNnx0hpPpQOCSEkCrW\nMzABzmbBoiYXRsaDhh5j5pnDUpsJRfDiz4WMYJhuaGwG/3TifdRwNvQOjsPrl1LrJJQEw1Hs/+x9\n+NGRQZxIC496ulY3IyRGizo3Vsj00XRq5xxDYlQ17Mdnb0+22ebLjH2GpfxMysGslth8PstkFVmM\nyFX92RFCqg+FQ0IIqXJiNIaR8SBqHFbYbdail8FXEp9fxPn+Cc37nH53NOPftcKvzx9GSIziMx9b\nA6fDhp7+cUzoTCJd2urGY9tXISrHizo3lk8rZjq9ypWTs6kGYgtTXOUwqZh9hqX4TADz13aofQ+z\n10vk81mW6rMjhBA1FA4JIWSemAnLAObXGaQ6N2tq2E2GuGQVZ/eDd2D/86c1v0cwHEVUjhu+UFdq\nH+xc2YhtXW0QI3LeF/R6lauQGFUNxLG4euWwHOEq6bHtq+BysjjRO1Jwe2qSWW2eRlRCS6wZrb2E\nEGIUhUNCCCFlsfmuVgwMTetW6tJ1dTSj7/JEwVNGc54vq9oSEqOY0gmf6SEg33Nj3ukwDp8bQt/g\neMbUWKNBxkjlqs7NoUmlotlUy+W0HpYzXCVZLRY8tedefHTT0qIDaTknn1ZCS6wZrb2EEGIUhUNC\nCCFl0f/BJNZ2tGBbVxuO9gyjb3BCMygubXVj787VsFoHFat1+Wj0cFjPt+RUW7Qu/pPq3bcCltKF\nOgBMTIVzLto5uxVHe4ZxtHs49bV8g4zRypV6RbMl4zWJERkvHhJyhvWUazVCMe2pQGnaPLVUUltn\nsZ8dIYQYQeGQEEIWqPoiWjYZJnGxGo/FIUZj4GwWiNGY5mO8fglHu4dhtTDY9xEe4jYZN7wz+NPv\nd0OM5D422c752PZVEK5Nqg6l0bPlnkV4YheveCHP2a1Ys6xBczjNmtsbch7L2a1oqnNoVuDMCDJG\nK1d6Fc30aqFaEC5FuDLbXLR5UlsnIWQhoXBICCEL1G89ei++/ZML8PrzD4hWJM4DSlIUUiCGGqcN\n9mgMgVBU97HpIcTJ2iApBEMgcwJoMBzJ+zU2GWyXfHznapzrH0VYyn0dDtaKvTs7FB+n195oRpAx\nWrnSq2i+fOyybvW1GlYjzEWbJ7V1EkIWEt1wyPP8fYIgnCnHiyGEEFIeDW4WLXVOrLm9MaPF0Kho\nHLjpDaX+PZ+AmR5CjFzsa4UsNfVuFl/9zH2GVji4OBse6lyiGJ4e6lwMF2fP+brRs4BmBJl8Kldq\nFc0ZA+G6GlYjzGWbJ7V1EkIWAiOVwz/jeb4FwPcAvCgIQv5XEYQQQiqKPxTB0//nNLx+CXYbAwaA\nFC3PAsT0EGLkYt/IucBs0zMSQmIUrN1qqNqTb+tg8WcBjQeZfCtXShVNI6plNQK1eRJCSOnohkNB\nELbzPH87gH0ADvE8/wGAFwD8kyAI+ff5EEIImXNROZ6q9kXKFAqTskOI3sW+VoBUU+/mcOjMB+gb\nHDc0kTPfAGbWWcB8GKlcaVU01agN66lU1OZJCCGlY+jMoSAIV3me/x6AKIBfB/BfAHyd5/n/JgjC\nP5byBRJCCKluDtYKKSKrBiMjF/t7ti5HICjhVNbCezU1TntRU0IlnR2AxZwFLGWQybcFNzmsB1Ce\nuFrJqM2TEELMZ+TM4a8hUTVcDOC7AB4SBGGI5/klAHoAUDgkhBCiaGmrG7//qfXwToeBeBwtDS5E\n5TgmpoKKqx+yL/azd/LpTUVtrOWwdmUT+i5PKN6uNpEz+X26hVF4/RIsTGKBvNJQm+Ty+D1bl6ee\nM70quGfrcoz6Mt9fuYKMVkXTwVrh4myYDIip1/rxR1bgx69fLuvOQ0IIIZXLSOXwQwD2C4LwevoX\nBUEY4Xn+P5fkVRFCCJkXZkISXn59EH2XJxLhjrUCiCMsxQxNE80+P6cVDBvcHP76d7dhaGQSr/eM\nKN5HbSJn9veJzXbaplccH9u+SnF1xdOf24RAUILbxeLg8SvY/9zpOQtaWhXNhzoX51QwDxzuL9tC\neUIIIZXPyJnDT2vc9rK5L4cQQsh84vVLOJoW1MKSnPpnvSCS7/m5DWtaUOfm4M9zSqiR79PTPw5Z\njmW8l+zXrxa05Fgc+z7CG34fxdI652i1WFLBuNwL5QkhhFS+su855HneAuDbANYCEAH8miAIg2m3\nPwXg80icb/yaIAj/zPN8M4ADAJwARgB8VhCEoNJ9y/tuCCGEFEstiOidn6urYeEPSooDbNZ1NOO1\nc8M5j1nX0ZTzfcZ8Qd2Jnl5/GD0D46qvf/eDd6gGrWM9w0A8jr07VxdUQRR1zj8q3cfIOce5WChP\nCCGkspU9HALYA8AhCMIDPM9vBvCXAH4VAHieX4TEsJuNABwA3uR5/lUAXwVwQBCEF3ie/28APs/z\n/A+V7isIQn7LsAghhMwptSCit8LCagEeuHsRHt+5Gi4u868ztfmr6V9PP8+op76Ggy+gHqSGRgOq\nQSsWB472jMBqteTVqpl93jK7TVWMyPBOh3H43JDiVFa9c45zsVC+mhkJ6WY+jhBC5sJchMOHAPwb\nAAiCcIrn+Y1pt20CcGI24Ik8zw8C6Jx9zB/P3udfZ//5ssp9z5TnbRBCCDGDWhBhmDjEiKzwiASv\nX8KJizfgdNgyQpcYkdGrVuUTxvGrW5bD42JzzhlqWbe6GX2D46pBqr3VrbuLMd9WTaV9hYfPDiEe\nj4NhGPT0j+V8v3zODGqdT1yzrN7Qa6xG+YY1vZBu9uMIIWQuzUU4rAUwlfbvMs/zNkEQogq3+QHU\nZX1d6WvpX1fV0OCCzUa/tSOEkEqyZe0StC/JDSO//ZdHEQhFdR/fd3kCn3/UCQeb+CvNytrh9atU\n+QIinn7hDDbfs1h1oimA1LTS1gYnNt+zGE/uvhvP//QdvHL8iuLrX3F7E+6/ZzF+dvIXqs/p84dh\nZe1oaa7RfU9TARE9/coB9+TFmwiJ2p9L9mei5rc+0QWXk8Wpi9cxPhkCx9oAxHHi4g0MDE+l3rvV\nmggzYSkK37SIhlpO97mVtLR48n6MWWQ5hud/+g5OXbyOsckQWuqdOe9PybMHLyiGdJeTxVN77jX9\ncXNlLn82RBv9bCrXfPzZzEU4nAaQ/klaZoOh0m0eAJNpXw8pfC37vqp8vmBRL5wQQuYTqwWQ1Yd/\nlgRnt4BhGIiSjMbaxFnB3Q8sw9iYP+N+/qCEX4xMG3rO8ckQLv9iAq0NLrS0eCBLETR61Kt43mlR\nM8QxDPClT2+A22FPVZe83hnsfmAZgiEpZ9BL8vVvufs2zeetq+EgS5Gc95ouWW06e2kUkwFJ8T56\nwRDI/Ez07NlyBz66aSlePCTg5MUbqa+P+kJ45fgVBEOS6qTWfKpgLS0ezfdeatkDg9Lfn1qVVYzI\nONGbe3YVAE70juCjm5YqVh8LfdxcmeufDVFHP5vKVc0/G61QOxfh8ASA3QB+NHvm8ELabacBfJ3n\neQcADsCdAC7OPuZjAF4A8FEAxzXuSwghxIAahw3+YFT1fJ7ZNt/Viv/40TsBQLWtL9ny98FowPDz\nprelhqUopgIiOlc142i38sV5UrI6mK3R40BbszvntekttG+sdaBJo7V03epm3UCQT6urlkLODArX\nfIpf7+kfhxyLZ3ye1bbyotDJrIUO7aFhP4SQajUX4fAfAezkef4kAAbAZ3me/68ABgVBeIXn+b9G\nIvxZAHxZEIQwz/NfA/Dd2emk4wD2CoIwo3TfOXg/hBBSlaaD+hUoM+3esjx1Aa637N7jMv7XU9fq\nZtisDA4c7kff5QmM+UKod9uxuNGFkBRVrcApBcPk83F2K/xBCUOjAbS3uuFxsanb1Qa9aJ3hW9rq\nxt4dHZrvI9/VHVq6DATRdFphxjsdxnmVFtdqWXlRaFgrdGgPDfshhFSrsodDQRBiAH4968uX0m5/\nFsCzWY+5CeCXFJ4r576EEEIqT1OtA421jlRl0MnZEBKjqepbdsXMSHB1sFZsuXdRquUx/fG+QARA\nBKyNAWtnIEVyk2Cjh8Pajmb0DU5ktIn+Px9ajv3Pn8bwWACxeKLC2Nbixpc/vR6sTfuvzfQdg97p\nMOrcLLo6mnPWWCgNRdFb3WFEg5vDurS1HkZphZk6N4tJjUmt1VAFKzSsaQV+rQBe6OMIIWSuzUXl\nkBBCyALj4Cz4+yMD6B0Yx2RASrV0NnpYrO1oQe9A/hWzsCSDYRhE5bhqxU2KqjfNrudbsHfHaojb\nMoPa/udPZ7S1xuLAB6MBfP173Xj6yU2ar0mp9RQAJqbCqHNzsFkZ1bN7dW4ODR4WXr9ypVONhQFu\na3IhFI7CFxDRNzgOq4XJ6zygZpjpaEbf5Yk5r4IVsxKimLCWHvjTf4mgF8ALfRwhhMwlCoeEEEJK\nbngsiOGxW0PBki2dXr+kezaw1mVTrST29I/jQ2uX6FbcHKwVLs6GyYCYc5GebBMVIzLeH5nCkMp5\nx+GxAPxBKaPFVA1nt6KpzpETBF0Oe0bwTJ7di8XjsDAMgqL66g41sThwffzWZ1voeUCtMGO1Kp+F\nLEcVzKyVEIWGNb2zpmY/jhBC5hKFQ0IIIYY5WCvCUv4BplBNtQ78xp678LXvdSve7vOHgXhcd8eg\nFJHxpX0bwNosORfp6eFD6zlicWBoNIA772hUvD27sqW0p1Dt+U9euKH4uTpYK1rqnZoDetQG6+R7\nHlArzMxlFUxt3yOQX/gtNqypnTUt1eMIIWQuUDgkhBCiiLVZIEUTuy6S5/t++YHbcXl4Gn//2oBm\nkDJL1+pmtLV4VKeANngcaGlwqbYMZtyv3qkYBoxOCLUwQHurO+frSpWtzlXNebXKqgXuGocNv/+p\nLhw8/j7e7LuueD+1wTqFngdUCjNzVQUrdMqoFgprhBCijsIhIYQQRclgCCTCi/DBJE6oVLjM1ujh\nsJ5PtA5G5Tj4ZQ0ZO/iSkm2NyQrWiQvXEVJozXQ5bLBZmZyv5zMhdHFzjWJLqVJlS69V1iifX0Qg\nGMHeHauxZ+sK/PDVfly65oPPn2iP7VzVhN6BMcVziqU4D1juYEUrIQghpLwoHBJCCDFkaHSmbN9r\nbUdzzuJ1B5uoEImSjMbazLbGZGXrs7vvxlN//BqCWYviPxgN4KUjgzltiPlMCA2LURw43J9x1s2s\n9RNq7brpAc/F2fC5X7krp33VamGqciqmkQEztBKCEELKi8IhIYRUIQYo2/L6fNitDCJy5iuzWgA5\npvIAFX2DE0A8jqM9I6mvJcPTlnsW4YldvGKg+MEhIScYJr3RO4KPbV6Gercj9TWt8JFN6axbsesn\nGtwcNqxpQTwex2vncquNSgEvu3pXbVMx8xkwQyshCCGkvCgcEkJIFarEYAggJxjef9dt2LdrNX7y\nxhWcvHAdYclYSvT6w+gZUF68fq5/DI/v7AAwW0mcrUCxdgsOn7mm+pxSJIYv/q9T2LpuSSqIaIUP\nNeln3bTCpdqgmCSGAb60bz2a6pyQYzEwDFNQwCvXecBiVkmky3fATLWFX0IIqWYUDgkhhJTM4NAU\nrBYLLAxjOBgCAGu1YDKgvO8vLMk48OoAPvuxNRkVqPQBOmrEaCwniCRDRrcwBq9fvwqYftZNK1y2\ntbg1p4zG40BIkjHqC6LOzeHRh1fiQ52LAYZRHZ6jpVTnAc1aJQEUNmCm0lZCmBWSCSGkElE4JISQ\neaTWZYfbZcdI2t67ueTzhzHmC+Z9Lk/UCXmXrvpw4PBAxuAXvcekSw8i6eHj+4cEnFAYfJMu+6xb\nbmWLw5plDfjEh1fh4BtXcOz8iGIF0cFa8M0fnYfPL4FjrQDiCEsxNBURvkrBrFUSQHEDZswIv8UE\nOzNDMiGEVCoKh4QQMk8wAKaDEUwHI3P9UlJYuxVgmKLO5Snx+UWc71duOzX2+Nwgwtmt+MzH1sDp\nsKGnfxwT02HFx3a012b8ezJc7tm6HAdeHcClq16cvHgDl6750LW6BR9atwSvp52dTApLMYQlafaf\nbw2jKSZ8GZUMSU7OhpAYVQ1L/qCEc5fMWyUxVwNmzAh2ZoZkQgipVBQOCSFknqjUc4hO1oo6N6va\nJlqI2hoWvkARg2BUgkh60Pv+q/3oEUYhRjI/2VPvjuL84DgevHcxHv9wRypcHDz+fsa6jWR42L6h\nDTs2tmdUFmfCEd0220L3+GlJD0kT02LqXGSjh8V6vjUVlpL3O3tpVPXnVsgqibkaMFNssCvFvkVC\nCKlEFA4JIYSUTFiS8fUXz5kaDAEgKhe3a3FdR5PmxfzB4+/j1MWbqreHpRiOnBuGhWGwd8dqzfDQ\nOzCBrz11f+rMnBSRsf/5M7qvsRR7/LJDUrLd1euXMsJS9v2UFFrpK/eAGTOCHe1bJIQsFBQOCSFk\nAfO47PCXuA3V7GAIADNh9XCoNyUU0K6y5rO7sFsYS4U+I+GhtcEFMSKDU9lrmK7ezZnaZmnkffX0\nj2P3g3cYev+FVvrKPWDGjGBH+xYJIQsFnaAmhJAFrK25Zq5fgum++Kn1qK9hNe/TOzABMaIczqYC\noqG9h0Di7GMy4DTWKgcE5fCg3wRc47SbGpqMvC+fP4yh0YDmGdEGN4cdG9uLrvQlB8yUuh0z/59N\nrmQ7rBLat0gImU8oHBJCyAJW52HB2SvrrwLOZkGjh4OFAerd2iEvW2uDE+23ebDxzlbN+yUrRkrq\n3Jzh71vvZlPrM4yGh6mAaGitRzAcUQ2whXByNlgY7fs0eBxob3WrhinWZsEffGYj9u5YXTUTOs0K\ndo9tX4UdG9vRVOuAhQGaah2mhORqJkYSq1jM/O+UEDK3qK2UEELmGQdrQTwOiJEYnJwVIVH9wk24\nOol1HU14+938Vk2UkiTH8OVPrAVrs8DJ2fDMC2cMV/I237MYnN2Kx7avgizHVNdIaFWMOLsVnSsb\n8Uav9koLILGncP9zp9FYy2FdRzO2b2hD78CE5lm6OjeHJpUWxXTJqqRZZ9lCYlS33bZrdTM8LlZ1\naIwUjeFnp65W3XROM845Vtq+xblEaz0Imb8oHBJCyDyy+Z7bwNkteOvCdQDQDIZA4jzgL91/O3oH\nvbpn4MzEMIkl8EoaPY6MJfBqQSXbh9YtxpO774bXOwOrxYJ9u9YADJOxCzFJr2K0a9PtmuHQYgFi\nsVsrKCamRbx2bhg7Nrbja0/drxketCZ2pjP7LFudm0Ojh4XXn3sGlAHwyPq2VFjas3U53uy7rvjf\nRDVO5zQz2Jmxb7Ha0VoPQuYv+vUOIYTMAw7Wiu0b2lDD2XCs5zqkqLHHWRjgjfMj2Hz3baV9gVlY\nm/pfP9nBLdnOV+vSbvVcv6oFVmvm8+7d0YFtXUtQ72bBwHgrYGOtA40e9e9nU+nP7Jndvah3li69\nRVFN8nMwq3WPs1uxnldut32kawn2fYRPVX0CwQhElV8WaLXkFqKcrYnlOuc4n+lNf6UWU0KqG1UO\nCSFkHghLMuJx4PxAfu2hsThwtGcErC0z7FgYwOWwgbVZFCtNxRIjymfuHKwVe7auyPhasuqz676l\n+L3/9ZZqxfG7hwRcuRnA7geWZezq67s8gamAhHo3h85VTYZa3zi7FWs7WhSrjgAgRZVfhNHpl+mV\nLO90GIfPDaFvMLMddfeW5Xjun9/FpWs+01r3kqG4WxiDzy+iwcNhPd+i2Ppa6umc1JpYnWitByHz\nG4VDQgiZJ873j8NX4NqI7LATiwOBUBSb72rFzvuW4vmfvYfhsaAZL1P7dURkBIISXFzuX0/1Hg41\nDhsCIeWyqM8v4pXjVxAMSYq7+nwBEUe7h2G1MIZa33ZsaFcNh2r0gpMYkTPaGjm7FYubarDvIzzE\nbYnb3C47Dh5/H7/37RMZIdrM1j2GyfzfbOVYVk+tidWJ1noQMr/Rr+YIIWSemJwR4XaY2y536t1R\nfOMHPaYHQwer/NeP1sXlS0cGVYNhup7+cfiDUtGtb421DjSpTO10sMqfM7+sXvHrciyGA4f78ZVn\nT+GL3zmFrzx7CgcO9yMoRlMtlcmWx5+8cQWHzw6pVleLad1LBrKJaRFx3ApkLx0ZzLlvKadzUmti\n9aK1HoTMb1Q5JISQeaK+hgPHWRAIh0x9XjGqv3bBqKbZ1sFYPI4j54wPislnMb3PH8b716eLbn3T\nqp49eO8iWBgGPf3j8E6HwbFWxONxvHXxBoRrvpz2SLUq2Zt9IxClWKqlcs/W5Tg5O0wdvHFDAAAg\nAElEQVRI6/0V0rqnHcjGcobMlHI6J7UmVjczpr8SQioThUNCCJknnA4rRsZL3/pZKAbAFz7eifZW\nD4JiBGFRxqWrPkwGRN2LS60wkY21W/Hdf31Pdc28UnUyu90zSesi2Gqx4NGHV+LFQwJOXrw12TQZ\n/OLxOD61k9cMZcl9h8nH+IMR3R2IhbbuaX2GE9MiXjwk4LMfW5Nz3q8U0zmpNbG60VoPQuYvCoeE\nEDIP2KxMxQRDzm5RbIlsrOXQWOfAgcP9GUNIHrh7ER7fuVrxnGGSVpjIFpZkzbUc6dVJvaEoRi6C\nu1WC35sXruPhtUsAhjEcbC9d9enep9DWPb3P8OTFG3A5bGU571eOM42k9GitByHzD505JISQeSCm\nt928RGxWBg2zayLqauxob6lRHXIyE47gz37Qk3Pm7cTFGzh4/Irm99E65+RgrbNn4jg4OfVQwdkt\n+PCGtozqpNEzeGorEMYmQ6pBVJRi+OrzZ/DNH50Hp3JGMdv0jAROY83HtvVtBbfuaX2GSeU871fK\nM42EEEIKQ5VDQgiZB+YoGyIqx1HjtANg4AuImJqJqN43LMXwwWhA8bZuYQwfWrsELfVO1aqRWovn\nnq3LEQhGIEVk7P8/Z1S/vxiJgWGYVNukVrtntzCGB+66DXabBS1ae/HU9mqkyWcVSIOHg9Nhw/DY\nTM5tj3Qtxr6P8IafS8lj21chFI7iRFobbLpynvej1kRCCKk8FA4JIYQUZUghyOTL6xex/7nTmrvu\ntMKEi7NDjMhoqXdi1Kc+kKenfzw1eEXrDJ7XL+KPvncOQGKy6oP3LsbjH+7IeU0tDS5YLYBsYGaP\ng7WixmGDzy+CtVsVK441TrtigF7a6sandhYXDIHEZ/jELh7vXfUqhta5OO9HrYmEEFI5qK2UEEJI\nRdBbrZCU3uIpRuSMVRCb71ms+T2SlTExIkOKxtDgYXVfV1iK4ci5YdXXZLOq9NFmkSIyvvDxTvzx\nf9qMv/jNB3NaKrd1LUEwrFx5DYajiMrmlIc5uxXr+VbF2+i8HyGELGxUOSSEEFJx0it8ShJ7Awdw\nvn8ck4Fbg2R+49G16L50U7WaWVfD4idvXEH/B5OYCkiGzwICiVbT7NeUCJrGQluDx5HRoppdBZ0K\niHi9Z0TxsWa1eyansu7ZugIArSIghBCSicIhIYTMQ8zs/zNwJK4iaYUhORbDMy+czWi/TFYcWdaG\nkBhVf96AhNPvjab+PdnaaaQ11OcXc15TnZtDo4c1dK5QqSqX3lJZzHoHtVUcSWpTWZ/+3H0IBCML\n8ryf3mdGCCELEYVDQgiZh+Kp/1dZWJsFUlT/gF52GEq/kP/RkQHVwTZvv3PD0LqLbEZCdF0NC2fa\nug05FsM/vD6IqRn9YLjlnkW6VblC1jvoreJISk5lTUqGaQBlWV1RSYx+ZoQQshBROCSEEGIq1sZA\niiqnLSPBELgVhrIv5Os9HGZC6hNRfdNh1LtZTAaMTwgFjE17nZyR8MwLZ1JB4qUjgzhyblj3cY0e\nDk/s4g0FD7WJrGrB0kjo05rKqte+Ox9RUCaEEHUUDgkhhJiqa3Ur3n73puJtDLQLmqyVwcY7b8Oe\nrcsB5F7I+/zaVcGGWgc6VzTiqMrZvWIlg4QUiaJ3cMLQY9bzLYbDVz7rHYyGPq2prOVcXVEJKCgT\nQog2CoeEEEJMY2GAX9yYVr1dr0AXkeM4efEGLl31Yu2qZvRdNhbAku676zY8unU5rFZLWvWNQyAk\nGR4cY8Txvhu6raj1bhYb17QWNOTFyHoHo6GvmLOMRlTT2T0KyoRUp2r6c6baUTgkhBBimlgcuOlV\n3zOoJ5m3vH6poOqfzWpJVd92P3gHhkYDaG9146cnf6F4lq/g12kgGD795CZ4XPqrMgqlHfo4SBE5\nteIj37OMRlTj2b1SB2VCiLmq8c+ZakfhkBBCSMWyMMbOAyadfucGfum+dhw8/n7GxcS6jmY8sn4J\nTl28mZpQytotuP+uVrxzRXkhfDE2rmktaTAEtAfYzIQj2P/8mdSF1McfMX91RTWe3StVUCaElEY1\n/jlT7SgcEkIIKcimNa04fWlU/45FyCcYAsD4ZAgvHurPOPM4MS3itXPD2LGxHX/12w9heMyPQCiK\n5Ytr4XGxOHC437SqooO1Ysu9+pNJzZI9wIa1WxGWZISlxOCf7AspI2cZjSjn2T2z28nyHfpDCJkb\ndEZ4blA4JIQQUpCPbGrH5ZGpglZHlArHWnHmPeVhON3CGORYHH2D4xntSblVNQ4uhx2BoASfwamn\n9W4Wv/XovWhrdpf1YiV9gM3YZAjf/NH5VGU0XfqFlBln6spxdq9U7WT5DP0xG52bIsQ4OiM8Nygc\nEkIIKQhrt6FzVTOOduuvc1DCMIk2P6Uwo/o9bQyicjxVIcsWEtWfy+sXM16rXlXtxZ8Lht/bxjWt\nWLG4zvD7KAX/jKTaHmv2hVQ5zu6Vup3MrKBsBJ2bIiR/Zvw5Q7+QyR+FQ0IIWeBqHFZE5Vhe0zyt\nFgaNtRx2bGgvOBzef9dt2LtjFV45cRU9/WOGKpBROY5Nd7bi8Z08fnri/VS1r97NIShGNYOm2vnF\nnv5x7H7wDoTEaOoCQozI6BscV3weB2tFjcMGn1+c85bE7NCh9h7NHrZS6rN7RtrJAFTNRR+dmyIk\nf8X8OUO/kCkchUNCCFmg6mrsWL+6BXt3roYYkXHg1QG89wsvfAEJnD3xl6cYUV5aL8fiOHj8fTz6\n8MqCls4DwKl3bsLJ2bDvI3yiLdIXxF/9Q5/mLsNYHDj17iisFgue2MWnqn1SNIb9z53W/H5q5xcn\npsPY//xpTAWk1AXEtq421XYmKSLjS0+sB2u3mhJMivnNdnboUJuiWophK6U8u6fXTvbiIQHCNV9V\nXPTRuSlCClfonzP0C5nCUTgkhJAFhrUz+OKnNqCx1oGQGEVUjsPF2fFrv3IX/EEptf7hJ29cwbHz\n6uskzl0axaMPr0RXR3PBS+eP9QwD8Tj27lwNq9Wiu+Q+6cTFG3jvqhfr+cQewagcV20/sjDAQ52L\n8M77PtXqZDLcJi8gZDmm2c7U0uAq+oK+2N9sa4UOC5MIio21patslvLsnlY7GWu34uTFG6l/r/SL\nPjo3RUjhCvlzhn4hU5yyhkOe550Avg+gFYAfwH8UBGEs6z77AfwygCiA/1cQhNM8z68D8D8AyABE\nAJ8WBOEmz/PfAvDQ7HMBwK8KgjBVnndDCCHV6YG7F+GN3hH0DIxjMiChqZbD2o5mMADODySGtTR4\nWMyEoprP4wtI+P4hAZ/4cAcuvu/F2GQ479cSiwNHe0YQQxwWJr+qj9cvZYQCtfajh7vasHdHB555\n4azh4Tl9l72q5ynNqsIV+5ttrdARB/D/fXIdVrTVlfwiqBRn97TayW5tw8xUqRd9tFuRkOLl8+cM\n/UKmOOXuv/gNABcEQdgK4HsAvpJ+I8/z6wE8DOB+AJ8E8D9nb/oWgN8WBOERAD8B8PuzX98AYJcg\nCI/M/h8FQ0II0dDeUoPLI9M42jOSUS07cm4Yr50bxsS0iDgSwUuMKreUpjtx8QZ+79snNIMhZ2fA\n6DzPsZ7rOHnheh7v5Jae/nGIERmPbV+Ff7d1BZpqHbAwQFOtAzs2tmPvjg68dGQQH4wGDD+nzx/G\njg3t2LGxPef5zKjC6f1mW4zoD+lJhg4ljR5HWYJhKT22fVXO57/lnkWpNR3Zkhd9lSYZdJXQbkVC\nzKf1ZyP9QkZfudtKHwLwjdl//lcAf6Bw+88FQYgDuMbzvI3n+RYAnxQEIXnVYAMQ5nneAqADwN/x\nPH8bgOcEQXi+9G+BEEKqT22NHRv4VsTjcbxeYAuoGrVziUmtDTUY9QV176d3u5r03wQ/tedefHTT\n0oz2I60gpqbB40BjraNkbZNm/GbbrKEwlTrNT6mdDAAuXVNuD67kiz7arUhI+ZR6YNZ8V7JwyPP8\n5wD8TtaXbwJIVvf8ALLnftcCmEj7dz+AOkEQBmef80EAvwXgQwBqkGg1/e8ArACO8jx/VhCEPjPf\nByGEVLt6N4unn9wE1m7Fl75zqizfk2GA+hoONU5bXhW7QmSHguz2I60gpoZfVq/4fGYFKbNaDYsJ\nHdUyzS/751mNF31zuVuRkIWIfiFTuJKFQ0EQngPwXPrXeJ7/CQDP7L96AExmPWw67faM+/A8/xiA\nLwP4ZUEQxnietwL4liAIwdnbjwBYC0A1HDY0uGCz0R/GhJCF5cHOJVhxexOuj89gcqY8bXdupw2+\ngIipoHnfr72lBkNjMzlf37J2CdqX3ApzLS2ejNs9dU60NDgx6gupPrfFAsRigJOzAmDw1js3MDg8\nhc33LMaTu+8GADz/03dw6uJ1jE2G0FLvTN1mtVoQlqLwTYtoqOXgYI391bplbRteOX5F9/3o+cLj\nGwr6/s8evKB45tHlZPHUnnsNf38t6a8LyP3ZFOK3PtEFl5PFqYvXMT4ZQnPWz6LStc/1C1Bhxs+G\nlAb9bApT6J+N+ZiPP5tyt5WeAPAxAKcBfBTAcYXbv8Hz/F8g8eenRRCEcZ7nnwDweQCPCILgnb3v\nagAv8TzfhcTZyYcAfFfrm/t8QdPeCCGEVIvJqRBu3JyCLMfR6FGuVpnNH0wMs4kV1imqavuGNvQO\nTGT8JvgjG9vwTv9N1Lk5tC+px9iYP+MxciwGu007NDzUuRhyNI4TaVMwR30hvHL8CoKhxNnM9CCV\nvG0mKIJhmIKqb7sfWIZgSMr5zfbuB5blvAcjbAD8UyEYeaQYkXGiV3k/5YneEXx009KiKltKVckt\na9uw+4FlplQl92y5I6d92OvN/cUBMaalxVPQf3Ok9OhnU7x8/mzMRzX/bLRCbbnD4d8C+C7P828C\nkADsBQCe578B4Mezk0mPA3gLicD3m7MVwr8GcA3AT3ieB4BjgiDs53n+RQCnAEQAfE8QhHfK/H4I\nIaTinXznJlxOu+ZEz2owNDaDjvY6fO2p+zEVEOF2sTh4/Ar2P3daM4C8dGQQwwoVx3TbutrwNy9f\nULytWxgDozJR58SFGwhLt4bH5DNxdC5bDUs9zU9pEmsyaJu1bqIUU1IJIWShK2s4nG0B/Q8KX/+9\ntH/+QwB/mHWXRpXn+3MAf27eKySEkPkpOeY/ed7i7KXRghbXm41jLZAiMdXl7dl6Bsbxie0daG1w\n4cDhft0AYmQYjYNNBDL1sKReaU0Phhmv0+BaBbOHwRh9vlKuV6AdY4QQUr3KXTkkhBAyB7zTYVwZ\nnsKKtjo8tn0VJgMSzl4aneuXhRrOjt/5+F3423+6iKmZiO79JwNSKvwYCSBGhtGEJRlvnB/RCEsc\nGAZ5tePqVd/MHgaT7/OVcpof7RgjhJDqReGQEEIWgDiAv/j782is5eBy2Es+QdQor1/Ed15511Aw\nBAALAxw6fQ07Ni41FEC0KmTptJber+cTO+qUgpSDtSju3dOrvim1Xeq1o2pVBQt5vlJN86Ol74QQ\nUr0oHBJCyAIRRyI05DuQptHDocZZukDpU1hcbrUAssIwm1gcODq7p1EvgCTDVOfKptRjVF/D7NJ7\nq4XRDEvZt8XicRw5lxsotapv+bZd6lUFC23jLNWZR9oxRggh1YvCISGEEFUMgH/30B3oXNmMf3nr\nFzkDWEqhwc3hDz6zEQffvILj569D6TiiVqVvXUcTXj52OSNMLW11YyYkwetXPmdpZOm90m1yLAYL\nox0os+XbdqlXFSy2jbMUg12UqpJb1i7B7geWmfp9CCGEmIvCISGEEFVxAC/8qwALI6CtxY1v/MZm\nTAYikGMxvHF+BG+9czOvsFjvZjE9I6G2hlUdiOMLiAiJUXzs/tvxxvnryvdRqfRtWbsEgaCI17LC\n1MS0iG3r2yBKMk6mratISq9oZYel7HbO9NsKqb7l03ZppCpYiW2cSp+L0poRQgghlYXCISGEEF2x\nOPDBaAB//sNePP3kJgDAvl21ePSRlfjBz/vx9rs3EdOZONpUy+Grn7kPITEKJ2fDMy+cUW1xPXTm\nGnasb0eTRuhRqvQ1N7vx639yWPE5+wYn8PTn7oPLYTNU6ctnyEs+1bd82i6NVgUrtY2T1k0QQkh1\noXBICCEkxcJAM+QNjwUwMRVCU50TAODi7Pj0L63Be1d9uqsx1ixrgMfFwuNiAQBOzgZAOfi82Xsd\nb5y/DgerPLlTrdLn0zhT6Z0OIxCMGK70FTLkxSijw2CMVgVLNVyG5DJ7/QghhFQSCoeEEEIAAOs7\nmvH4jg4I1ybxv//lPcX7xOLAM989i/vvui1VQZsKiJjSCYac3YLHd94KVGJERiCoPqE0GVCTk0Ad\nrBVSRNYNPQ21nOoEUY61psKUXkWr1Lv6jLajGq0ylmq4DLnF7PUjhBBSiSgcEkIIgYUBugfGcfWm\nH3cvb9CsIPqDkYwKmpF1EQ/euxgu7tZfOVMBEVMz2oEynYuz4Uv7NqCl3mkg9DCGn1dNuXb1GWm7\nzKcqSG2cpVPKSjIhhFQKCoeEEEJSQXBiWsQbvTfgdtoQCEU1H5NeQVuzrAEnFAa9JO3Y0J7x70b3\nDyZNBkSwNotiMExv84tOixBVBuRIs/drbXDptgZW0pAXqgrOvVJXkgkhpFJQOCSEEJJDishoa6nB\n9fEZ1QpiegXt8Z2rca5/VLGds97Nwu20Z3xNq11SiVIgU2rzu/+exZqhzu2y48Dhft3WwErc1UdV\nwblTrkoyIYTMNWqSJ4QQkkOKxnH7bR782a8/AI/Lrnif9MDm4mx4qHOJ4v0mAxKeeeEMDhzuhxy7\nFR4f274KbS01hl5PdiATIzJe+NklHD47hIlpEXEkqp4/O/kLuBzKr7dzZSMOHn8/5zGHzw7hpSOD\nqecd9QUhRmQ8tn0VdmxsR1OtAxYGaKp1YMfGdhrysgAlK8lK5mpdCCGElAJVDgkhhCi6dNUHdvsq\n3LuiSXc3IADs2bocwXAUl6764PVnVlmUzmdF5ThmQupDaQCgrsaO++68LRXIktXCbmFUdaH9TCiC\nbevb0DswDq9fTJ2f7L08gWBY+fv19I9BlmPouzyRU1Gkdk5SiZVkQggpBQqHhBBCFHn9Iv7w+TOY\nDIhwsImLX1GS0VibGIiyZ+tyjPqCcLvsOHj8/VSrZr2HA2ezQIzmtpimn8+aCoi66y8saa2eYkTG\ni4cExaCabjIgYtd9SwEAR7uHU22xam2BQCK8Hu0Zyfj39DBbTMug1vnGQm8j+sz+/GhdiHnov21C\nKheFQ0IIWaAYADp76+ELJAJVeHbIy+a7bsMTu3gcPH4F+587De+0CI61pm4HAJ9fPYSln8+qc3Oq\nS+7Tn+vw2SEI1yYRDEcMDbBp8Djg5GzoGxzXvW+S2nTWbmEMH1q7xOCU1Exqqw/2bF2BqYCIw+eG\n0Dc4nlOpBEArE4pQqpUTNBioeLQOhJDKR+GQEEIWKL1gqOT0ezcxNBbA0NhM6mthlemgStLPZ+Uz\nlOaD0YDh78Evq8fUjKRZKcymNnTH6xex/7nTBV3Eqq0+eLNvJGdwT3qlEkBFrUyotipPqVdO0GCg\nwtE6EEIqH4VDQgipQgyAtpaajJBWiFqXHdMay+izxeIo6ntmn89Kb9WbmA4X/LwA4GAtYBgL3rp4\nA5euenMqmrfuZ4WTtWIyIKHBw2FtRzN6B8ZUzzCmD64BjF3Eaq0+UJromtQtjIFRWdNY7pUJ1Vjl\noZUTlYt+NoRUh8r8050QQoimOID2FnfRz1OjMonUbI0eDjs2tmPP1hWpaaDArVa9rz11P5558j40\nqUyE1LO40YWwFENIjCIOwOuXVCuaLfVOWCyJBMYwgNXCYN3qFkPfp6d/PPXatWitPtDi8+uvTCiX\nZJVHbbJrJTKycoLMDfrZEFIdKBwSQkiV6rts/EydmpmgBAdb+r8KfvPf3wMA2P/c2/jid07hK8+e\nylhtwdmtWNxcAyeXX0NLo4fDtq4lkKLKgc3BWsDZb70/qyXRopodeBggtbZCpXAHwPhFrNbqAy0N\nnrldmZBc5eEPSppVHiMBWev5C328nmpfOVHqz2cuVfvPBpjfPx9CkqitlBBCqlRQLP4CZToY1QxD\nZvnLl3oRDEdT/67UpvnSkcG8Wla33LMIT+ziMRUQ8XrapNF02S2cskpH5/mBCXztqfvx6MMrMeYL\n4ls/7lMcfmP0Ijaf85Tp1vOJCma5VyZkt5DWu7nUMKJshSx9l2MxPHvwAk70Dpe0RbVaV05UYwtv\nvqr1ZwMsjJ8PIUkUDgkhpEp5XDb4g1H9O2po9HBgGBiaAlqM9GCYLnnWKPHPypWqJAuTaKdtTFsh\nYLVYUhUJpfegNoU0W3rgaW/1mHIRm736gLUrn4EEgKba3LUIhaxMKHR4TPagELVgCBRW5SnnIJJq\nXDmxUAa1VOPPBlg4Px9CAAqHhBBSlSwWYCZUXDAEEpWqWDyOI+eGTXhV+Utv09Q7o/fwuiXYtWlZ\nTvDRqkgYCYYAYLdZMgKPGRex2asPbu2DvPWcnSsbsWPjUjTWOjLeU74rE4qpbGgNClGSb5Wn3INI\nqm3lxEIa1FJtPxtgYf18CAEoHBJCSFWKqQ+8NISzW7B17RI8tn0V/v61AXNeVAEaPA64XXa8/Ppl\nMAwQVwhzFga4787b8OgjK+HilAfoJENb3+UJjE+G0OBxgGMtGBkPGnod2d83n4tYvWpd+uqDfC6M\n81mZUExlQ294Tr2bxfSMVHCVx8ggklKshqiWlRNz9fnMpWr52QAL8+dDFjYKh4QQsgBFojHIcgxi\nRMb5geIH2xSqa3UzDh5/H0dVzgwCiare6XdvYnBoUrUalgxzn3/Uif4r4zh05tr/Ze/O49yq73v/\nv7QeaUaaXYOXMRjb4+MQM8YLEBbHQIa4ScPvuoVA4oaGJiG5TW6z/O5tmzYUSJqk+aXt7zY3/TVt\nU2jSUDek0NAkbUpwMAQMjsELxgTOeAFsj21m08xIo335/aGRrNGcc7SMRqOZ+Twfjza2dHTOVzpj\nrLc/3+/ny7MvnSt5HLFEKvclrzDsGX3xq7RaV+0vxjOtbJhNy21vcnHvXVsIRxMVV3nMzj9fGpHM\nJvl86pvcH7HYSDgUQohFKJWGPYfO8uob/llfb5jPQnbdoMIm1ceOrau474Ffmr4mGs+USUuphrmc\ndvYc6ucXh0sPhpAJQZ4GJ7t295Uc9uplHdJMKxvFGoV4G5x4G5wVj6/ajUgqXVdZr+Zzo5bFQO6P\nWGwkHAohxCJ23h+u6fXsNognyW30Xsl+gGbVsEgsUdb6uaxMBfNkyWGvntYhVaOyMduNQu64aQ0N\nbid7Xzpb8fkXcsfI+dqoZbGQ+yMWEwmHQggxz5TagbMeZbcHywavZCptGGyMmFXD/OPlhc32yYCx\nY+ul3PfAft1j9MLeTKt11ax+VaOyMduNQmxWK3fvuJz3XLWi4vPXS6V2NszHRi2LidwfsZhIOBRC\niHkmlYZN3R0cnOW1gq0eJxtVH6lUml8cPltRIN2yrpNTb40z4I/oPn+4b4jLV7fyi5fOlz4uk2pY\na5NxFa2QBfjMbT10dXoZ8IfKCnuVVutmq/pVrcrGbDQKyQZhb7O74vPXU6V2Ns2nRi2LkdwfsRhI\nOBRCiHnGaoFbt63i5LlxRoOxqp/f47Lz2Ts24Gt2E44miCVShpvMF/PiawOmz/uDUY6cGGFFp4eJ\ncBx/IIqlSGXUrBrmctpL3ny+rcmFb/KLXrlhr9Jq3WxVv+qxslEYhH2tbnpWt1cUhKVjpBBC1IaE\nQyGEmGdSafjz7x+elWAIEIwk+PNdh7BYIBJL0eZ14nLqb+ButVwYU6VGgzFGgzFu3LSc7Veu4PEX\nTrPn4PR9F11OG9f3LC1aDSt18/n8EGcW9nrWtOsGrnKrdaVUvyAThNyKvaIOofVU2SgMwgP+cElB\nWG/KrXSMFEKI2pBwKIQQ89BsBcOsbIdQgJGA8bVSaejyNXJmcGLG1zxyfJjbb1zDzt5ubFZLXuhS\nWHdxKx+8eS0NivlfW5FYguGxCLduW226+bxeiJse9hQaXA5eOjbIUwf7p00BLbdaZ1b9GhmP8NDj\nGq+dynSPza4rbfM62aR21rTpSjXWQ1YyDdRsyq10jBRCiNqQcCiEEDWU3cphIQlFEizvaKR/SD8g\n2qyQTOk+NUX+9MByp0hmg8WRE8MM+MO0eJxs7O5g581rSw5xhcc9vv/UlP0XjaaAllqtM6t+OexW\n9h69sO4yW4kdCcRq1nSlmushK5kGWmzKbTXWVS60bTCEEKLaJBwKIUSN2G0WrljTzova3G06PxtG\nAlFaTOaVXtezhGOnxjk3EjI9T4tHwa3YGfCHctMq3Ypd98t84Zf8wmAxGoyx59BZjvePc+9dW7BZ\nrSWHOMVho9mjcOTEsO7zZg1QzMKHWfUrljBPz7VoulLN9ZDlTgMttdJY6brKhbwNhhBCVJOEQyGE\nqJFEMr3ggmHW6ITx1NN4Is3n79zEH/zNXqJx4xAZSyT54j/uZyQQy02rLJxeedsNq3jkqZNTvuT3\nrG43DHKnB4Ls2n2MO9+tlvV+yq18lRo+slWuA68N4C9javBsN12pdjfQcqeBlvN5V7KuciFvgyGE\nENUk4VAIIerQhjVtnBmYKGv/v3r1/NG3OP1W0DQYAgTDidyvs4XIwumV2qlRTg8Ec8cNj0enTP3U\nc7hviNtvXJMLJKVMLSy38lVu+AhFE9MeMzPbTVdKDWflTMssnAba0XKhW2mh2Ww4s1i2wRBCiGqQ\ncCiEEHXo1TdGuKjNU3E4bPU4CEWSRItMVyyFtcjWEqWoRsMagP7BoO7jZms5RyeiucY0u544xmtv\njuAPxEynFpZT+SonfBSGyFLNdtOVYuHM0+Bg1+6+sqZlFq7hXL2yncBYWPfY2Ww4I9tgCCFE6SQc\nCiFEHYolMlMiV3R6CEXiZYVEp81Cg9vBWChe1jUVhxW308bYRJxWr4LbZad/cGlY+6AAACAASURB\nVGLGwbCajMZiNsQ2r8Lj+0/x/CtvTdnSolh1r9QGKMXCx6A/hNNhw63YDUNkoQvTaRU2qb6yN7Mv\nV7Fw9tgzr1c8LTM7DdTltBMwOa4aDWf0yDYYQghROgmHQghRx4KhGJ/csZ5/+I9XecuvX3UpFEum\n6R80b/6iZ+uGZbkqj1ux86XvvFD2OWabURXTabcQS+hHxAaXw3TqqdHUwlK7nJqFD6fDxjceOcLI\neJRmj7OkLUjecVknH+xdW9E+hzNhFM52bF3FfQ/8Uvc11ZyWWe7WIKWSbTCEEKJ0Eg6FEKKO+YMx\nvvLQwVk5t+KwEk+kplRobFYrna0NDPhDhtWwamtvctHgsk9ZS2hkuc+je5xRMFQcVibC5oGs2NTC\nYg1QzMJHJJbMVStLCYYrOj189H2XYbNa8TY4ix5fTUbhzOxnYTamZVbScKaY2apKCiHEQiPhUAgh\nFim3YmeL2qa7ubxZNaxarFa4+m0X8VvvXpvbjuKgNshIIKrTrTQzvfKW61by8M+P89qbfkYCxccW\ni6eIxc1DWTWmFhaGjxaPQiiamDKN1YzisHLt5UvZ2ds951srFIazhTAtc7aqkkIIsdBIOBRCiEVq\nNBhj79HzOB1W7ty+bspzZtWwakml4PlX3qLR7WBn79opX97z9zkMRxN4Ghw89szrfOkfX2BkPEqr\n14litxZtuNPqVbBYMA251ZhaWBg+YokU9z2wv6TXLm1r4Asf3kyD4pjRGGbLQpqWORtVSSGEWEhk\n51chhFjknj58lu/9TCMUTTDgDxGNZ6pdd9y0ht4tXbics/vl/1DfUO6a2S/v3gYnna0NOCeDx6NP\nn2T3i2cYHo+SJrO1RSmdWBvdDq7o7tB9zuW00bulq6pTC7Pj97W4aWsqraIWS6TmvFpYTPZnob3J\nhdWSmQrcu6WLHVsvnfIzI4QQYn6TyqEQQixyqTTsOdjP80fPEY2lpmxTcOu21RzqGyxpeqTLaSUS\nK3/rjGKbyg+PZ6aZVuL0QJDuFc30bunKW2+msO7iVt3ptNVSTuV1PmynUFgZ9TQ4eeyZk9z3wP6S\nt7YQQghR/yQcCiHEAtXU4GB1VzOvnBwhVkKVLRvs8rcp6N3cVXJjmmsvX4rVYuHg5F54pSplU/mZ\nbKfx0rFhvnz31TVfb5a/DnFkPILFoNPqfFm3Bxcqo7t29+lubRGOJPjQdnVeTTUVQghxQU3Doaqq\nbuAhoBMIAB/WNG2w4Jj7gF8HEsBnNU3br6rqRuAnwLHJw76ladrDesfW6K0IIUTdGw/FOXU+QGer\nu6JN6A/1DXHLtSsNm5FYJv9f22Tnx9tuWMUjT52EdCYBKQ4LFouVaDyJ1QJJg3xazqbyehSHlVgi\nlb3sNPmVuVpW5wqrbY+/cJo9B/unHTff1u2Z3Z+9R8/z6psjbFI7pYoohBDzUK0rh78LvKxp2v2q\nqn4AuAf4TPZJVVU3AduAq4EVwKPAlcBm4P/VNO0vSzhWCCHEpEyoq6zjqD8QIRxN0LO6XXefwBs2\nLmP7VRfnKnGF1aRoPA0kufqyizh+ZlQ3YLqcNnZsvXTKY2abyutpdDn4g99Yz//3w5cZCUzvTDrX\nlblstS3TidQy77dTKHZ/RgKx3M/Bzt61tRqWEEKIKqh1OLwe+Prkr38K/InO8z/TNC0NnFJV1a6q\nqo9MOFRVVf1vZKqHnzU6trASKYQQojItHoX/2n+Kfa+cn/K44rByXc9SPviuC9suRONJDmoDuufR\nTvkZM9jjLxZPEgzFp3TqbPYotHqdukFPz2gwisNuZd0lbTx39Py05+ulMpetJN5y7UrODATpbHWT\nTKVJJNPY5lGBrdRtTg5qg9y6bXVdfPZCCCFKM2vhUFXVjwKfK3j4LWBs8tcBoLng+SZgOO/32WP2\nA/+gadoBVVW/ANwHjBocaxgOW1sbsNvlLykhhChFi1fhKZ2KYTSewtOgsOSizH/Ck8kU/+cHhw3D\n3FgwRluTi+HxyLTnOlrcrF7Zjstpz53rwR+/QriMxjaK08Y3f3iUodEwbsUOpIlEk/ha3bxj/VI+\ncsvbiSdT+MejtDYpuWvVWva9Pf/yWQZHI1itme08OvPGaSsxJUZiiTl9P9dtWM6PnjlpesxIIMq/\nPn2ST99+Re59+XzeWgxPVEDuTf2Se1O/FuK9mbW/UTRNewB4IP8xVVX/Dch+il4yAS/feN7z+cf8\nUNO07LE/BL4J/LvBsYb8/lAZ70AIIRYvuxXeODdu+Pyzh8/wnqtWAPDdx19j39G3DI9ta3LRs6Zd\nd71dz+p2AmNhAmSqj997XNOt/tmssLS9UXftZDiaJBwNT/46AcB165fwoe0qdpuFv/7BIQ5NNsmp\nVVfNaDw5rflN4bTb1GT+HfCH+dEzJwmFY7lpmHqvh6ldXOeyS+gt11xMKBzLdZM18uSLp7GSZmfv\nWnw+L4ODgZqNUZRO7k39kntTv+bzvTELtbX+58a9wHvJVALfAzyj8/zXVVX9C6ALsGqaNqSq6i9V\nVf29yYYz7wIOGB1bqzcihBALWbHmpiOBGF/6xxeIJpJF1wdm19UZrbcr3LZCTzIFa7qaWHdJa+4c\nLR6FUDShu83Gq2/6geldT/M7sc7Gejij8LZj66VFm+wc6htix9ZVPPbMScPwV+v3YyS/2Y5RoM86\n1DfErdtW12xsQgghKlfrcPgt4Luqqj4LxICdAKqqfh14ZLIz6TPA84AV+NTk634X+KaqqnHgPPBx\nTdPGDY4VQghRA+dGis/GuPqyi3LBJr9zp1lFzcjhY8P82SeuyZ0jlkhx3wP6TapHAlG++9NX6Tut\nP6EkP7BUc3sLo/AWDMeLhmh/IMK/PNHH3ryglR/+sntO6sm+n1qv71McNn7nvesADANitltsVy0H\nJoQQoiI1DYeapoWA9+s8/gd5v74fuL/g+YPAdTqvm3asEEKI+vHr11wyZbpjtnNnVjnbVowGYwz6\nQ3R1eulsbSAaT5o2Rtn3K/0GOZAJLN97XEM75a/a9Eyz97LvFeNpt1ktHoXXTvl1nzvUN8Q7Nywz\nDJj523XUms1q5c7tKtopv+69mOtusUIIIUo3j/qjCSGEmE9sVvC1uInGkwz4Q0Tj06d/lrttxTce\nOcKu3X3EEgkeffoEE5F4RWNzOmw8d/Q8w+NR0lyo0D385PGKzgflv5dC6y5pNQ1/pNO0NemHrLkO\nYIrDxsa1Pt3n6qVbrBBCiOLmpmWbEEKIBc9us/CDPcc5cnzIsDrX7FFo8Sr4A6WFqmyI006Ncnog\nOIPRpXUfffbIOXZsXUWDUv5fj+VuwWG1QCoN7XnrEs2qb77WBjau9elOwc0PYEbNbGZbdr/G+bKP\n41x9TkIIUc8kHAohhJgV0Xh6SofSbLALRxK5TqKPPn2CULj86l//YOXBsM2rMGIQRiOxJP/yRB8f\nfd9lZZ9XcdhodJceDv/4zk143M4p4aRY+DMLYHPdydRsXWk9mevPSQgh6pmEQyGEEFNkK1p6XE4b\nsXiSFo9CLJEkGE4YnsdigbTOefYePc+rb47Q6HZWXP0zGl8pRgJR0/f42ik/gVCMcDRRVsCJxpOE\nSpzm2uJxstznnXbuYtW3chr7zFUn08J1pXOtsEJYLx1fhRCiHkk4FEKIRc5CZpJle1MmiLz3HZfw\npe+8wGhwegWs0WXnjz+0CV9rA3abhV2T3TVj8el7X+gFw6yRQKzkCpses3AH5DaYN2L22uHxKPc/\n+AKjwfKqSuWsOdzYbTwNtJTqWzmNfeaqk+lc06sQ9qzp4KVj8jkJIYQRCYdCCDFLFIeVWDxlsLqt\nvvz+B65g1fJmFIeNAX+IMZ1gCOAPRHE6bLkv0HduX8etN6xm1xPHeO1Nv+F0zWpb7vOYVh2bGxyE\nokmiOqG1FP5g5n2UU1Vq9iim3VOzVnR62HnzWtPpjeVW38yC6Vx2Mp1LehXC/GnOhRbr5ySEEPlk\ncr0QQswSl8M2L4JhW5MrFwzhQsjRo9cVs0Fx8LH3XcZ9v3MlrbPYMdNiyVQ3e7d08enbenDaLIbH\njgbjbFY7q3btQ31Dut1W85l17ARo9SjcuGk59961ZcqG9tXomFruPVvozCqpVoMfm8X4OQkhRCGp\nHAohxCwZC1W2zUKlik21NFK41UA25Bg1RgEY8IdyX6QH/aHcAsNsxa3a2rwKn/zN9disVp461M+f\nfucFYknjN9vscbLz5m4aXHYOaoOGFU2b1UJzo5PRYJSmRqfuVFqAkRKrSnfctIZkMsWhY0OMBWO0\nNbnoWdNO7+Yu2ppcU6aSVmMaaP6U1FI6mS4WZpVUoz8ji/FzEkKIQhIOhRBigSg3GFotsO2KZdO2\nGojGk9y4cTnJVJojx4dzjVGu6G4nlU5zz7f3MTweRXFYSCTTJCdnbioOK067hVii9IEsbWsglkgW\nnYrZ6HbwrR8eLXpc1sbuDhoUB7duW801l13E//PPB3XDpMNu5Z4Pb+aRp07y6hvDhuezAI/vP8XO\nm9carj3MThM9cmKYsWCMFo9Cz5p2dvZ2T3vNTKeB6k1J3dDdwbs2L+fwseF5sZXEbDKb4tvepNCz\nup0jJ0YW/eckhBCFJBwKIcQCUW7lcNvG5dz5bjX3e90GHqvb6d2ygrYmF48+fYKf51WmovGpFyu2\nvs/ltNGg2PEHoijOTIXm/EiIVq8Tp8NCLD598FYLLOtoLKur6YpOD3e8aw27dvfl3ovRxxKNJXnk\nqZM8d/S86TlTadhz6Cw2m9Vw7WHhGjd/MLPGzWa1THuNWXgpZXqj3nq6Jw/007uliy/ffXVdbyVR\nC+bVbx87e9fKPodCCKFD1hwKIcQC4TbZuD1/nZXVMhmgblo95Ri9NXB7Dp1lz6FMEw+jaZDTrmXw\nN8v1PUv5ysffwbXrlxCJJYnEkqTJdC7VC4aQCWUTJttl5GtqcPLea1dy711beOSpk1Pei5G2JoXX\n3hwp6fxgvPaw2DTRwteYrU8sNr2x2LUAOlsbFn3gueOmNfRu6aK9yYU1b71qtkKYbfqz2D8nIYTI\nJ5VDIYRYICYixiEqv6KYSsPpgSAP//w426+6OFelMgsc79ywrORtGlIpeMdlF6GdGmV0Ikpb3rS9\nRDLNa6f8Jb+nFk9mPWAxTQ0OPv3+y9mwbglDQ8GSg+y6i1uLVg3zGU35rGSaaLE9DY1IZ9LSmO0J\nKYQQQp+EQyGEWKSePnyWpw6dpa1JYd3FrYbr+fyBCKTTJW3TAJlq3Ad7uxkLRsFiwdfizn0pHx4L\nlRwyIbN28MiJ4aLXjSWSfOW7B/C1ulmzvNn0GhbINYp554ZlvPrmSMl7LhpN+Wz2KLR6nbrnafEo\nuq+pNLzMdEpqvav2dM9ytwURQojFTMKhEEIsUtlq4vB4lL1Hz6M4LNPWEUImcPhaG+juamb4VwNF\nzzsWjPKHf/s80Vhy2ibyZsEmuyZxNBidUkWz2Y7rrh3LF4ll1jsO+MMM+MO4nDYisenTP9ubFP7H\nrZfzi8NnOXJ8iKcO9ufWP5bCaMqn4rDR6NYPh41uh2nIKTe8FOsmq3et+bC+zmzfR6MmQEIIIapL\nwqEQQtQpxWklGqtsE/dK6AVDuBA4tl99CftKCIfJFCQng1nhJvJmweb6nqW6VbQL0y8HGR6P5hrv\nNDU4iMaNNro3ei8+9r58nj2HzuYey4ZIxWE1bKpjtWQa+BhN+YzGk4Qi+luXhCJxovFkVUNZqVNS\n51Pg0muyk/+zI4QQYvZJOBRCiDq1eW0nDS57LhTVistpIxpP0tKocPmaNm7cuJxoPMmStgbDilwx\n+Xv3mQWbbHUxPyAWTr90K3bC0QSxRIr7Htive71sJTEbJNsnQ9GOrau474Ff6r6m0WVnY3eLbgDe\ndsWyKZ1dC5mvA4xWfR1gqVNS50vgqta+j0IIIWZGwqEQQtQhl9PGzpvXojisJJMp9h49T6zIVhGl\nKrblRTqdxuu24w9Gefalc/zi8DlaPE42rvVxzfqL2HPwrPGLDeQ3SjEKNslUasr2E4VVrvzpl94G\nJ9F4sug6yOz77Fndzs7etQz4jdc8jgRiWK1Wbtq8nJcK9grcsfVSBvwhwxBmNl3W6bDhaXCU/ZmV\nwmxK6nwKXNJkRwgh6kN9zSkRQggBwLWXL6FBsbNr9zH2HDpblWB448ZlfO0T72DbxuWmx0XjKcZD\nmc6n2XA1Goyx52A/x86McdPm5bQ3ZZqe5G+RYUavUUp+sBnwh9i1+9i0rTR2v3iGXU/06Z7TbDuI\nQkdOjBCNJ3MhzshzR89jtVj48t1X89WPv4MvfvQqAO57YD9/9Hf7uOfb+9i1u49kaur9MBtLJJbk\nsWdeL2mcMxGNJxnwh3LbZpQSuOqF2X1ZCE12hBBivpDKoRBC1KN0mu/89Fc881Lp2yyYuW79Enbe\nvHayatcN6TRPHz5rWkHUc2Zggu7lzXz57ncwFozidNj4812HODcSMn2dXqOU/PVwZtW/pw+fBYuF\nnb3d09bJ5U9RHQlESBu8n/zqk9Gax6xsVa2ztYFdu/tKnpa5Y+sqnj1yNjelNd+Lrw1wy7Ur8TY4\nDa9bKaN1hTu2XlrVrqaz2dSmkiY7Qgghqk/CoRBC1KEnK5i6acQC3J7XgMRmtXLn9nUk02l+cfhc\n2ec7dGyIGzcux9fawKNPnygaDF1OGzu2Xjrt8cL1cEZSadhzsB+b1TJtKmr+FNXB0TDffPQIg6OR\naefID0N33LSGUCRhuL9hNkg2exSTaZmD06ZlBkMxwwZCo8EY9z/4ApvXVb8ZjNm6wmoErmJNbaoV\nGivd91EIIUT1SDgUQogFLg2Eo4lc1Sqztu8YR44PAxfWILZ5nYxNxEgWmcE6Goxx74Mv0OZ1EooW\nb04TiycJhuI0KBfW3ZmthzOy52A/B157i9FgXLfrptNu5crLlvCfz70x7bU9q9tywSUTjlW0U37T\nqtpYMGpY0Rwen95kxmzdIYA/WP1mMMXWFX7xo1eRSqd57uXzuUZCLqeNdDpNMpUqKaQahc90Oo3F\nYqlaJ1TZtF4IIeaehEMhhFjg2rwXNmFPplJ86TsvcnogmHs+O7X08tUd/PJXb+W2oShmJhvHmwUv\nI8lUGn8ws11ENqCkUimsVmsuoHS0uFjR6WEiHMs0mJkMvkdODLNrd9+U5jbFqmpuxW7YvMdqAbdi\nn1Y1KzZlFarbDKbYusJgKIbVYpnSYTYSS/LzA/1YLBbTkBqNJxkcDXNQ09++ZG9e4IT67YQqhBCi\ndBIOhRBigduk+nJBZNcTfVOCYb4jx4cr2qaiGL0pjM0eBZfTqrs+rxxPHz5HMi+9ZaeUdvkaGQnE\ncsFOL7gUm8YYjiYM12Sm0pmKmnbKP6VqdtsNq4DMGsPRoH54rmb3TbNqZavXhVuxl92xtHAaqdGy\nVKOflUrD73zak1EIIRYqCYdCCLHAOB1WEonUtLATjSc5eGzI8HX+KnevbG8qtmasxFanJpIG6e3s\n0ITu4/nBpdg0xmaPQpvXqVshVRzWKWsWC8PnLdeu5P4HX9D9TKvZfbNYBTQcTZS9RUSpa0GNVBp+\n63FPxtlswiOEEPVIwqEQQiwgLqeNr378amLx1JQvtMlUiu89rjFmUM0CsFgw7PZZqjavwoY17fRu\nWUFbkwuA4bFIbuP67JjGgtFZqVJmGVX89IKL0V6BisPGJrVTNyhZDHJtNnx6G5xsXleb7ptmFdBE\nMl1Wx9Jy1oIaVX4r7YRaT3syShWzNBKehVh4JBwKIcQcs1jIfbGKxpK0ehXGQzESyfKT2vU9S2nx\nuKY9/vCTxw27c2bNNBhaLPDZ2zfQ5fPkvlwf1AamrP1ra1LYtNbHjq2raPE4DadezpTRWsFyg4te\n8Fp3cQt7i3Q67WxtqFn3TbMKqM1aXsdSszWMkKn1tk1WhFPpNE8e6C/pvMWUsidjNabhlqoeq5j1\nRMKzEAuXhEMhhJhjd767m2vWLwNg0B8iFEnwlw8fLuscisPKZrWTHVtXTXsuGk8aNhXJ19LoZHSi\n8rDW5nXha3ED079cZ4PaSF6ny57Vbfxihvs4Lu9ooH9o+lYay30e3bWV5QYXveAF8FqRTqdGr53N\n6opRBbSckGq2hrG9SeEzt/Xga21AcdhIplJYLZaqhN9iayerNQ23FPVWxaxHEp6FWLgkHAohxBz7\n/s+P0z8Uym05EI2X36QlGk/x3NHzaKf8U/4FP5lK8dDjWtHOom1ehQ3dHew5OL0SVKps8CplauLe\nl8/zh7+1saRw6HRYiel8Jis6PXzhtzfxyFMncwGlo8VNz+p2brth1ZTHW70ueta0c+PG5UTjybK/\n3BcGr3KqcUahrZhqTdkrJ6Sar2H00dXprei8xZTSPbZW6q2KWW8kPAuxsEk4FEKIORZLpPm5zvS8\nShT+C/7DTx43nAKZb+PaDj7wrm5s1kwlaGQ8gsVgamZ7k0LP6naOnBjRrRiVsk1FJJYklUwbNnzJ\nd33P0lyFamQ8QrPHycbuDnbevHZaQFm9sp3AWDj3/m/dtpqR8Qi7XzzNkeNDPHWwf0ZT4LKBbcfW\nS4HZmTJa6ZS9YmGy1JBa7nTYSsPvTK87W+qpilmPJDwLsbBJOBRCiCKsk41a8nPS8o4G1ItbeOn4\ncNn79dXCob4hbrl2ZcnNRdJMrwQ9/sJp3UrixrU+dvauNQwjpW5T4bBbDRu+wNRupzar1bRClQ0o\nLqedQMHjew71s+fQ2dxjlUyBMwpsX/zoVQRDsapOGS13yl6113/N1Wb0c3XdQvVUxaxHEp6FWNgk\nHAohRBF61bP+oRBrVrTgUuxA/YVDfyDCmYGgaXORfC8dG+b9N2SmWyoOG80ehd7NXUBm/0O9So55\nxch8mwqX04bPoGlLz5p2ejd30dbkmvJFPDuucoJDtabA1WqNVSXjna2xVasiWE/XLXWqbr1UMeuR\nhGchFjYJh0KIRcluhcTM9l/n6bxqVL1p9bpY2tGI02EhGi/ehjQ7Hay92TWtCtWz+sLWFKV88Stl\nm4qr334RkNnm4tZtq4tWiyqtjlVjClwt11iVO15Z/1Wacn9+6qWKWa8kPAuxcEk4FEIsSlajvQ4W\niJ417fznvjdLCoZwYTqYXhVqz6Gz2GzWkqtQzR6l6DYV8ViSe769r+SgV2l1rJQpcIFQjDMDQbo6\nPXgbnNOOMwtsI+PVXWNV7pQ9Wf9Vmkp/fuaqelrvJDwLsXDJZjRCiEUplliYwTC7OfuLr57n5wf0\n1/Lp2bi2A8CwCvXiawMEQqVtc6E4bGxY0274vMtp47lX3mJ4PEqaC1/UH37yuO7xxapj0bhxlTI7\nBU5Pz5o2vvq9A3zum8/y598/zOe++Sz3PbifWCIx5bhsYNNjscDjL5wmmZphGbqE8epN2TMbm6z/\nyij35ycaTzLgD5n+XImMbHiWYCjEwiHhUAghFpDsRvaBcLKkTe3bvAq9W7q446Y1plWo0WCM+x98\ngV27+0oKQnab8V8vKYOBPXvkHKFofNrjpVTHzNxx0xp6t3TR3uTCask0uund0sWxM2OcHgjmCsip\nNJweCPKVfzo45fVmgS2Vhj0H+w2DbSWMxqs3Za/cMDmf5Ie0mQS2Un9+kqkUu3b3cc+39/FHf7eP\ne769r+SfdyGEWChkWqkQQixCrV6Fz73/wobmYD6lEcAfLG0qXjSe5PCxIcPn9fYshMz2FrueOMbH\n3nfZlMdn2h1RbwpcLJ7kSYPKav9gkEAoNmWK6R03rSGZTPH04bO6s5Grub6v3Cl7heu/WjwK6y5p\nzW23Md8Urg9UnDYgTSSWor2CTqyl/vzIxu5CCCGVQyGEWJQ2q5kNzQu7gRpVofIVm8ppVqkp5rU3\n/dPOXa3qWP4UuDN5FcNCqTScGQhOecxmtXLjpi7DamwpFcxylTplLxsmv/jRq7jm7UuwWOD5o+e5\n74H9M658VVqxm0mlLxvSstOOI7FkbluUYlOQ9ZTy8zOTqctCCLGQSOVQCCHmWKPLxkSkdl8+b9i4\nLFdtKmztv2PrpTx75Jxpt1GjrpnZ8xSrQJoZDUZ1m6hUqztidpydrW6MehJZLdDV6cn9PlvJOqgN\nYDRTtx7W9z32zEn2Hj2f+/1MKl+Vdoc1e10pzEJavnIrtcV+fqSxjxBCZEg4FELMaw4rGMxSnDfC\n0doFwxs3LuPO7ety66sKv8TfuHE50SLbUOQHIaMwcEV3Bz8/0F/2+Fomw2WhmXZH1Btng8tOMJyY\nduxy39SupYXTDfXM9fq+am9pUekUS7PXfeaDm4tet9Sqc7mBrdjPj2zsLoQQGTKtVAgxr833YAi1\n2VEj23hm582ZL/aFU/eyX+J3Hzhj2P0yKz8IGZ0nDbmmKhYy1bhSNLodpiFGb6plKVMY9cYZDCfw\nuO25sVktsKLTwxd+e1PunIFQzLSS1d6kGDaLqaWZNu3JV+kUy2Kvi8SmB/FCZt1X81Ua2Iym6i7k\nxj5CCFEOqRwKIcQcs4DhdMVqCUXjuS6h0XiSg9qA7nEvHRtiw5p29hw6O+05xWHl2suXTpmSanye\nYb5899W5Ss3jL5xmz8HilcRQJE40niz6ZTwaTzIyHmH3i6c5cmI4Vw28bsNybrnmYmxWa24KqVux\nG4YWxWHnvruuZMAfpqvTQ4PLPqXC2GyyX6MF+MxtPXR1eou+r9lWzcpXpVMsi73OPx4t+qUjG9Lm\nolIrG7sLIYSEQyGEmHO12HExEkvx5IF+rBYLvZu7GAnoB56RQJQbN3Vhs1k51DfEyHgEp8NKGojG\nUxw5PoTNauG2G1bx0ON9hufJDxGdrQ3s7O3GZrVkzhmImDR20V9zmJU/PbQwCA2PR/nRMycJhqJY\nLZZcwGvxKPgNKmf+QIRkKs3bVrYBsGt335RgYhQMAdqaXPjqZB2aWagqN0hVGjSLva61SSEwFi56\n/cKQ5pwcezSWpK1p9gKbbOwuhBASDoUQomqcdihh5lxRVgtggWyTSasFCVp2MwAAIABJREFU7HYL\niUQah81KNFH5XNqD2iDbr1xh2IwF4Md73+Ajv/42bt22moce13SbnGinRjld0NEzX2GIyP/iPTga\n5q9+cFg3WBarcpWy/u+5l89PaahjFAwLr1dqM5SseptuWK3KV6VBs9jrXE47gRKurxfSgJoFtuzU\nUyGEWIwkHAohRJVUIxjCZGhLT/39dZcvY/uVK3A6bPzx3z+fa+1fLn8gyoA/bLrO8YXXBjjRP8qG\nbh+vvjmie0z/oHEwBOMQoThsdPk8bOj26U4zNQsfpYY3s06rZtcr1gyl1aMwNhGt2+mG1ax8VRo0\n9V7Xs6adGzcuL2nNYb7CkCaBTQghZl9Nw6Gqqm7gIaATCAAf1jRtsOCY+4BfBxLAZzVN26+q6veB\nJZOHrAT2aZr2AVVV/x3oAOJAWNO099TmnQghRG0dOT7M7TeuQXHYuL5nWdHqmZEWj5OlHQ0odvMK\n5EggZrpG0CxcXrt+CXfctGbaNhmQWfu464ljvPrGMECugtlewpYHY8FoRdtj5GvxOBmfiOmGHbNp\nkW1ehU/+5nqcdhu+FnddVQwLVaPyVWnQzH9dbk3o8SGeOtiPr9VNz+r2sjawF0IIUVu1rhz+LvCy\npmn3q6r6AeAe4DPZJ1VV3QRsA64GVgCPAldqmvaByedbgT3A5yZf0g28XdO0WizZEUKIkikOK9Eq\ntlIdyVvDl1+dGRmPoDgzX9pj8SStXhcNLrvhlM9wLMn//sGRkqemmu0FqPd4e5PCzpvXTts24oru\nDtJMn/KZPUfP6vaie/G5FbvpdNgsl9OmWz1sb3Jx711bCEcTumHHbFpkKJrgK989UPJ+fwtFpUFT\ncdjYc6h/SmOjAX+44n0XhRBC1Eatw+H1wNcnf/1T4E90nv/ZZNg7paqqXVVVX1518YvANzVNO6eq\n6kVAC/BjVVVbgK9pmvaTGrwHIYQwZQFiM1gXqKel8cL+f3pVnVg8yZmBIF2dHhSnlS//0wHODExM\nO08kljRdK1jIKIgt93l0z9OzpoPHnjk5ba+7YnseHjkxUrRLaTiaMA2GrR6FrRuXMxHSv97GtR14\nG5xT9jAspNcMJRJL5sLmTDaWX0yqve+iEEKI2pi1cKiq6ke5UOHLegsYm/x1AGgueL4JGM77ffaY\nQVVVO4F35Z3TCfwl8A2gDdirqup+TTPoqw60tjZgt8tfRsW899qV/Odzb8z1MISYt9K5/1c91/Qs\npWtZy7THlyZTPPjjV9h39ByDo2F8LW48boduMKyEr8VFo9vBqbcCpFJgtcLKJU187ZPX8dDjGvuO\nnmPAH8ZqzTTQefnEEBOR0tf8ZfkDEWxOB76ORsNjnG4nrV4nft1GNk6++b9uxON28A8/OopbsRGO\nZsbhVuy868oVfOz/Wo/NVrza95kPbiYSS3B+OMSXHthHJDa9w+aRE8N84lY3Lmd9Ld2PxBL4x6O0\nNilzOrZzQxOMBIw7xBa716L2fL6535JF6JN7U78W4r2Ztb85NE17AHgg/zFVVf8NyH6KXmC04GXj\nec8XHnMbsEvTtOy3jvPA32qalgAGVFU9BKiAYTj0+0MVvJPF5zeuX0k0GuepQ2dJ1mJ3biGEqRWd\nHn7j+pUMDk7v9Vi49cKAP8yAv/h2AaVyOe28ce7CdVMpOHl2nL9/7GV29q4lEIxkGtxMFkqHxipb\nE9jqdZGMxXXfY/72FXrBEGCz2kksHOPB3X385NnXpzwXjiaIROKMjJQXmCcCYYYMPsuh0TAn3hiu\nmyYp+Z9RdirvXE5/TcaTtHmNt7Uwutdibvh8XrkfdUruTf2az/fGLNTW+m+MvcB7J3/9HuAZnee3\nq6pqVVX1YsCqadrQ5HO9ZKaikvf7fwVQVdUDrAdena2BLyaJZJpUKi3BUIg51uJxcuPGZdx71xbd\nL/jlbr1QrmUdDQRC+mHsUN8QgVCMIyeGdZ8vV8/qNsNphtntK/SCRnuTi94tXbkGOPuOntM9x7NH\nzhGKltctM9ugRk+5G8vPtvzPKM2F6a8PP3l8TsaTXb+pp962ABFCCHFBrcPht4C3q6r6LPBxMmsI\nUVX166qqXqVp2gEygfF5Ms1oPpX3WhU4mf2Npmk/BfpUVd0H/Az447wgKWbg7GCQF7XZ+8IpxHxh\nmePrb+zu4PabuhkeixCNT5+qWWzrhVIZvc9zQyHDTeD9gQhnBoJVuT5kpmnu2t1HMjV1raZZAG71\nKHz+tzbSu7mLRDLNWDDK4Kh+pS8SS/IvT/SVNab5EnCKre/T+9mphTtuWkPvli7am1xYLdDZ6s4F\neSGEEPWppgsSNE0LAe/XefwP8n59P3C/zjFv13nss9UdoQD45qMvEwjF53oYQsyIFbBYITmDvjBp\nMtU7o4A0237x0lkOHxtkNBifNk0wmUrx+P5TWCyQnkGRv6nRwfiE/p93s9O2el10dXoMt34oV7bS\nlUymuHP7utzjZgHYH4zyle8dYCwYo61JoWdNB+1NiuHU1tdO+Ys2vSlUrY3lZ5PpZ5TX5bbWChsn\nrV7ZTmCselOehRBCVF99raQXdWF0Ym6+CAtRTanc/6uc1QINLvuchcNkCvzBTHAr7JL58JPHp2wT\nUGhFp4dQJJELNG7FxpnB6WvuNqudHDk+VHbAu6K7nR8/9wYTker+Q9LTh8+CxcLO3m5sVqvp3oNA\n7t4Mj0fZc7Cfrk4PYBSUomUFpew+jbduW12VjeVni9lnVA/TX7PbYbicdubn6hwhhFg8JBwKIYSB\nVBpC4fLWqc22Q31D3HLtStO1hu/sWcqdv6bmplo2exTsNgu7dh/jcN8QoxNR2iYrYLfdsIrjZ8ZK\nDoetHoXN63yk0ml+rrMfoPlrnYTztoXQk0rDnoP92KwWdvauNd17UE+mMqi/x2SpQanemrsUY/YZ\n1dP0VyGEEPWv/v6WE0KIOmFh9ivp5a5rzK71Mwtz+371Fg8/eRy7zUJnawN2m4WHnzzOkeND+INR\nmhud9Kxu446b1vDIUydL3vewxePk/o9cya3bVvPSMf0l3opD/6+V69Yv4XN3XEHUJBjmy18rV7h2\nrdUk4A2Phtmsduo+t3FtBwAD/pDpOrx6a+5SisLPKL9RTymi8WTRz0UIIcTCJ5VDIYQwUIt+vW9f\n2crRN/wlH9/U6KSz1W1YHQOIJVLsfvEMoUiCO7erPPr0iSlVpdFgLDcltZxuo1vWdeJtcHJueMIw\nnMbiKa5bv4TXTo3iD0Ro8Sisu6SVD96cmSZa6hrF/LVyhWvXbFYLX/neAd3pvh0tbnbe3E2Dyz5l\nneCG7nbS6TT3fHufaTVwvm7eXvgZ6U1/zU6TzX9uvlVJhRBCzC4Jh0IIMYucdgtWq9VwKuXRN/zY\nymicMxqM8bV/Pkg8UfwFzx09z6tv+glH9dcFHjo2xJjJespWj8LYRHRaE5bdB4yneLZ6FbZffTG3\n3rCaR586wWun/Dx/9DzaKT8b1/rY0N3Bkwf6i45dbwqo3WZh94EzHOobNFwH+o71S2lQHNOCUmFA\nzlYDswE6G5bMmruMjEcY9Ifo6qzfTY+z6/vymQXAbJU0q3Bta5ZesBRCCLHwSDgUQiwIDYqNcDRZ\nk2pfOTxuJxu6O9hz0DgQGQVDj9uO025jJDA1rJTTPMYfMD52LBijxaPgD+rvH3jvXVsIRxNTAkE0\nnuTIceNdgyYice57YD+K0zYlEGdDx7s2L6d3S1euquew61dA9dbKFQaZwvFuXNvBR255e26z+2xQ\nMqsGPnf0PL96Y4SNa33s7O02be6SBr7xyJF5V1kzCoDJVNrwXmarpNkpyVJZFEKIxUHCoRBiQQhF\n63Ot1GgwSu/mLmKxJHuPnjc8zmqFlkYFfyBKs8fJxu4Odt68llAkwX0P7J+VtY9tTS7Wr27jaZ2u\npxu62/E2OHE6bFMqRsX2VswGPaNK6eFjw3z57qu5ddtqRsYj/OzF0/zylfNEYpnXuZw2rrt8ybS1\ncsX2O7z3ri14G5zYbNMDS7ExjwZj7DnYz/EzY9x71xbTBjhGlbVi5qryZjpNVhs0/LnKTuvdfeBM\nSZVFIYQQC4OEQyHEnLJQm7V9xVgsYLNACbM1y9LqddHW5OJD21VePjnEeEi/+2kqBZ/6jfU0uh24\nFTvhaIJEMk04mpi1pjgb13aQNtgkMZ1Os2t337SK0Y6tq2a0t2H+WsI9h/qnBdNILInFYplWlTIL\neGMTUcLRBN4Gp+7zxbbDyDo9EGTXE33svDkTeg5qg9Oqtlmlrj+c6zV9Zp/b6EQMp91KTOeHPrP9\niX1err8UQghROZkTIoSYU1/48CbqYXZaOl39YAgXpkcqDhvqyjbTY62Ta+q+9J0X+KO/28cX/v55\nvvHoEdPXtHqcWCyZtY1mnA4rbV5lSifLHVsv5bBB19Hnj76l27HzsWdOsnGtz/RapuOdXEtYrPFL\nYdfMbMAzO6eR7FYPpTh0bIhEMs3O3rV89vYNht1ksyHXSLb7567dx+a086nZ5wboBkPI/NyGownD\nYOkPRBgcDddth1PpviqEEJWRyqEQYk79zaNHSc1CKJtrLY1Otrytc8r0yEbFuMriclr5xeGzUza2\nHwnEIGBeNfzcHVdgs1p4fP+bPHvkPCmDMux165dw+03dU6Y2DvhDhl/+jaaFHuob4osfvTL362xH\n0lA0Ybp/YdYV3e1Frz08HmFkPMLS9sbcY8X28oPMFhXeZrfuObP3wawaCJl1mNnKpq/FXfbm8vmV\nwuHxKFaDdFmrylsp+0S6nDYaFDujwanNhxLJtOH7dzps/NUPDuMPxOpqHeJcV2qFEGK+k3AohJhT\nI8EYVisLLiBarBaSqTQD/jBtTS4AXj4xYnh8q1cpa1sJgPYmBV+Lm0efPsEvXjJez7ii08POm9di\ns1qndLIsdbplPn8gQjAU59Ztq3lnz1KwWHJjKGWj+mx2LXbt3S+e5s7t66Y8lg14+VtUXNHdTipv\niwpfq5ue1e25cJMfhrMdTL/701fZ96sB3esqThueBkfm1xVsLl/Y/MUorOdPr51td9y0hlAkwXMG\na15j8SR/fOdmnHbrlDWRNiuG7z8SS+b+MaCe1iGW2n1VCCGEPgmHQog5t9CCIWS6hO452M+eg/20\nNymoF7eaNkUJhhIEwvpbThjpWZOpmBlNz7RaYOuGZXzo3Wt1qyZm4cdV0G00q9Wr8Pj+Uxw5MTyl\nMnPbDasmxzLESCCCBf1g9NKxYd5/QxLFYaNnjXEX1yMnRojGk1MCmN5efo8+fYKf541/wB9m94tn\n0E6NEorEp1WPFIeNj77vMvqHQpweCE67biSW5LFnXs8FCb1Amr+tRz6zqbKFnA4bHoM1ktVms1q5\nc7uKdspvWAX1tbh1w+70968wEYnnGgjlm+t1iPN1j0ohhKgnEg6FEGKWDY9Hee7oeZw2C7Gkfikp\nEI4bNgcx8tKxQcaCUcPqWzoN77n6YtPpdEbhJ51O83Od/QgbXI4pU18LKzO3blvNyf4x/vz7h3Wv\nl18x693cZRgO9aaWwtSun5lx64eB/OCXP8ZssPzc7Rv4o797jmh8+v3IDxKlbC6fVawrar5MCD1Z\ns2pWJVVQmB7IY/Ek9z34gu6xtayG6jH7/Od6bEIIMV9IOBRCzLl66Vg625IGnUEB2iYrMuUYCcQY\nCRjvOdjWZN6oBfSrcYrDRjKVwmKxZCqB4xGaPU561rRz1GDqa36gWrW8mfYS1uq1NbkMjwPYfeAM\nd75bBfTXkhWrxhZ69sg5DmoD+AOZ/R31giHoBwm9zeULlTtNt1g1q9rbX5RTBS2Uv2dkueswa8Xs\n85/rsQkhxHwh4VAIMefqPRhagWavg9FAfEZjNdrsHiCZTOhuBj8Tna0uDvUN4nHbOTMQ5I23xnnb\nxW3YbVbe8k8wFogRjMRJpNOkkymsFisrl3ppUJy0eBVIJUmlUjgdFkaDMfYdPU8sof8JDI9HeOPc\nOFjgzECQLl+jfiMTu4W9R/pJpNI0KHYuWeIxDFMvHRtix/WXMjgW5l+e6OPE2UDe9SarsQ4rsRI/\nt/x1cn6TTqPeBgen3wrQPzjB6YEAkWiC7hUtOO02+oeCNLjstHhdRGJJ2rxOfC0NhKMJbFYLK3zG\n76fQ8HiEPQfOcOXbOnE6bLx+bhyP286Sdg+PPXMyF4RbvArqima2X30JbV6FsYkYsXgCLBacdhtu\np40Bf5gWj5PRYIzOVjfJVDoXhgb9odza0J29a9l+5QpeeWOENq9Co8vBuaEJfK0NuQCaDaVuxc7g\nWJjgRIzlPg/hWBLSacPpwD2r24jFk5zsH8uNpavTk9tiJBJLMOAPTQu70XiSwdEwpNNTxgEQCMUy\nP0+dHpwOG4OjYWLxBE6HHV9LpvnQ4GiYUDhOJJZk/ap2nj48fd/ObHU0e77OVjfhaIJ4IoVjcq1l\nOJrIja0wmJcS1Cs9ppJ/BDB7TannK6zCV3q9mY638Bib1cKAPzzlZ6ca15irvUb11MtY5nocc319\noc9itMfVQjQ4GFg8b9bAR7725FwPQQghRA0oDiuJZCr3jxJOeyZMBiPT99p0Oa1cs34JFouFw32D\nmU65VWC1wDJfI91dzbzyup9Bf3jKOtV/3XOCvS+fz4V2l9PKtZcv5dZtq/jaQ4foHwzm1q4WzjCw\nWjIPFq5Z9rjsKE4b/sCF7qu/8c5L+dpDhzgzEDT9B572JoUGl4OJcAx/IEar10mj26m7fjU7XbuU\nDql6x1zR3UGazD+ClNpZ1exaQEmdWvXOcd2G5dxyzfQp6DPt/lrOZ3PgtbfwBy/MnrBaYLnPwxd+\nexNOu3Eto9g16qmDbSVj8fm8DA4GdJ+r5TgW0vWrZTbuTa34fF7D/a8kHC4yEg6FEELUgxWdHt2m\nQAAet51geHqILdVyXyO/95uX5yoS9z243/Balejd0pVbL7prd5/uWs5Sjil27kJm1wKKjqPU8VZy\nbLnjLfWzWdHp4Ysfuaria8z0PVRTJWOZjQAy15/JXF+/WhZqOJw/8VwIIYQQC0b/oHFYm0kwBDg7\nOIFbseemkppdqxKH+oaIxpNFO6QWO8bsdYXMrzXIQU1/e5b885Uy3tKupz/G0sdb+mfTPxgkENKv\nZBe7RiAUm9F7qKaZfp4LZRxzfX1RnIRDIYQQQtSc0R6Q1ZAms/aVyf+t9rWyTYtK6ZBaThfb/NcV\nMjvPSCBqOBU4/3yljLeU6xmNsdTxlvPZpNIX7mW51zgzEJzRe6immX6eC2Ucc319UZyEQyGEEELU\nnNVwUtPMWYCuTg9M/m+1r5XtfprtkFrpMWavK2R2njavQptXv3lL/vlKGW8p1yul+2u1Phur5cK9\nLPcaXZ2eGb2Haprp57lQxjHX1xfFSThcZB78/E1zPQQhhBCC5T79L/yQWXM4E/mdLr0NTtNrVSLb\n/TS7f2Slx5i9rpD5tXxsUjuLnq+U8ZZ2PeO9Mct5fSmfzXKfcdfSYtfwNjhn9B6qaaaf50IZx1xf\nXxRnu//+++d6DDUTCsXun+sx1IN/f/b1uR6CEEIUZbXU/zYnpci+j1Lfj9UKy9obcNgsme0jKqQ4\nrECabN85p92C22knlpi+9YfLaWXrhqWsWtbEWDBW0XX1inNWCyzvbGSz6iMUSRCOJmhrcnHd5Uv4\n7//tMsLRBOeGQySS6clx2HjnFcv49G09vHxihGAolvvMCs9vtYDFCoV99bp8jdzz4c1Tuh5ed/lF\nvHR8mMCEeRfW9iaFjmY3dquFaDxJq1eho8WNw2YhGkvmxn7HTWuwWjIjumxlK+FogrFgjGgsUfIx\n166/iEuXNTE+ETd8XSGza62/tK3oOIzOcfNVF/Ob77x02nVLeW9myvls/OORXNdauFAx/MJvbzLt\nYFnsGjN9D9VUyVgaGxVCBmsuazmOhXT9apmNe1MrjY3KF42ek26li1h+59KvfOwqzgwGOdA3SKPT\nxtB4hPZmF2feCjI0HsZus2C32YnEEiQTCQIyJXxWOO0Q0+nDoLdJvNsG4WRms1Knc/J1KbDbIZ6A\nJKDYwe2AJFbisRSRyb93XXaIJMBpg0QS9HaJc9ogmcycJ8tB5ssY1kz7+EQKbIDNCrFsu3wrOB1g\ns1iIJtOkk+Bw2rjkIi/joSjnBsO5N+RwgNNhIZ22kiZFJJImkc487XJCh9dNa5OCzW5lYCzEeCBK\nKpmmyeNkfCKOzZb5YphMpvE0OFm9vJl4Ms1YIIanwcaQP0LSYsFptbDukmb6B8OMh6JEYkkSicye\ndy1NTtwOB762zAbna5a1cOkyL6+c9HNmOEAolKC1SSEaTzHsj5BIJ1lxURPxZJIzbwVxOaws6Wgk\nEEoQDEZIWyys6PTQ6nURjiZ45c1hLGloalTwtbhZtax5csF9Gu2UH4vNin8sgsflYGmHm7f8EWKJ\nFEta3AwHoviaFdpbGgiF40TjCQLhBOF4iouaXTS6HZwZCDA0GqHZq9DosqE4rKQtVnxNblyKDbvN\nVvE+hyf7/fQPhVm93Euj24k/EMGt2AmGYthtNhrdNlo8Ls4NT3BRayNvW9nK0HhmnU+b18VEOEY0\nkWLN8iZCkSTxRJIXtUFWLvHk9jlc5vPQ3OCk78wYA/4JHHYbF3d6aPEo+CY3ne8fCqK94ef182Os\nWtrMpcuaaHQ7aW505vYXPHZmjLGJKJet9pGIxglFErx5PoDdbkG9uIVQJEmLx8ngaBiX047dkUkV\nFizEEpl99kbGo7icNtSLWxgLxkmm0mXvc9g/OEEylSIQitPd1YzNZsWt2AlHE9P+N5lM8fq5AOrF\nLVP2OVzu807ZW8+t2DPrcCwWmhudM9rnMNug5fVz4ygOK067DYfdWtY+h77WBmLxJK+fHUNR7Djs\nmfP4WtzE4knODAR19zn0Nrs58cbwrO5zeOmyJtO98WSfQ/3jupa1mHZdlH0Oq6ucscxmR8y5/kzm\n+voztVC7lUo4XOTm8w/2Qif3pn7Jvalfcm/ql9yb+iX3pn7Jvalf8/neyFYWQgghhBBCCCFMSTgU\nQgghhBBCCCHhUAghhBBCCCGEhEMhhBBCCCGEEEg4FEIIIYQQQgiBhEMhhBBCCCGEEEg4FEIIIYQQ\nQgiBhEMhhBBCCCGEEEg4FEIIIYQQQgiBhEMhhBBCCCGEEEg4FEIIIYQQQgiBhEMhhBBCCCGEEEg4\nFEIIIYQQQgiBhEMhhBBCCCGEEEg4FEIIIYQQQgiBhEMhhBBCCCGEEEg4FEIIIYQQQgiBhEMhhBBC\nCCGEEEg4FEIIIYQQQgiBhEMhhBBCCCGEEIC9lhdTVdUNPAR0AgHgw5qmDeoctwb4oaZpl0/+vgPY\nBbiBs8DvaJoWUlX1buATQAL4sqZpP6nNOxFCCCGEEEKIhaXWlcPfBV7WNG0r8E/APYUHqKp6J/B9\nwJf38L3ArsnXHQI+oarqEuDTwHXAduDPVFVVZnn8QgghhBBCCLEg1TocXg/81+Svfwr06hzjB7aV\n8LqrgL2apkU1TRsDjgM9VR+xEEIIIYQQQiwCszatVFXVjwKfK3j4LWBs8tcBoLnwddmpoaqq5j/c\npPO6/McMz5evtbUBu91W2htYRHw+71wPQRiQe1O/5N7UL7k39UvuTf2Se1O/5N7Ur4V4b2YtHGqa\n9gDwQP5jqqr+G5D9FL3AaImnG588Ppz3uuxjWUXP5/eHSrzc4uHzeRkcDMz1MIQOuTf1S+5N/ZJ7\nU7/k3tQvuTf1S+5N/ZrP98Ys1NZ6Wule4L2Tv34P8MwMXrcf2KqqqktV1WbgbcDRKo5VCCGEEEII\nIRYNSzqdrtnFVFVtAL4LLAViwE5N086rqvp14BFN0/bnHXte07Qlk7++aPJ1XmBo8nUTk91KP04m\n5H5V07RHa/ZmhBBCCCGEEGIBqWk4FEIIIYQQQghRn2o9rVQIIYQQQgghRB2ScCiEEEIIIYQQQsKh\nEEIIIYQQQggJh0IIIYQQQgghkHAohBBCCCGEEAKwz/UAxOxRVdUK/A2wAYgCH9M07Xje83cDnwAS\nwJc1TfuJqqodwC7ADZwFfkfTtFDNB7/AVXJv8p77LLBE07TP13bUi0OFf24uBh4k899UC/BxTdO0\nmg9+gavw3iwFHgKcwAjwIU3T5ueuxXVshv9N2wY8pGnaitqOenGo8M9NG9DHhf2jf6hp2jdqO/KF\nr8J70wh8C7iUzH/Xfi9/KzhRHRXem78Crpg8ZAkwqmnaO2o78pmTyuHCtgNwaZp2DfB54C+zT6iq\nugT4NHAdsB34M1VVFeBeYJemaVuBQ2R+8EX1lX1vVFV1q6r6z8Cn5mLAi0glf27+FPhrTdNuAL4K\n/FmtB71IVHJv/hD4bt5/0z5W81EvDpXcG1RVXQH834Cj5iNePCq5N5uAf9E07YbJ/5NgODsquTe/\nDxyd/G/a3YBa81EvDmXfG03TPjv5PeBmYIzM/Zl3JBwubNcD/wWgado+YEvec1cBezVNi2qaNgYc\nB3ryXwP8FOit3XAXlUrujQv4LvCVGo91sank3vxP4D8mj7EDkdoNd1Gp5N58Dnho8l+BVwCjtR3y\nolH2vVFV1QX8LfDJWg92kankz81mYLOqqk+rqvqvkxV4UX2V3JvtQExV1ceBPwEer+2QF41K7k3W\n7wE/0zTt5VoNtpokHC5sTWT+5SIrqaqq3eC5ANBc8Hj2MVF9Zd8bTdP8mqb9rFYDXMQquTdDmqbF\nVVVVgb8AvliboS46ldybNGAjMz3uRuDJWgx0Eark75u/Bv5C07T+2gxx0ark3rwG3Ktp2jbgMeCb\ntRjoIlTJvekAWjVN2w78mMzfOaL6Krk3qKrqJDPrbt7eFwmHC9s44M37vVXTtITBc14y/6Ke/3j2\nMVF9ldwbURsV3RtVVW8k8yXqTllvOGsqujeapsU1TbsM+DjwT7UY6CJU7r2JAVuB+1RVfQpoU1X1\n+7UY6CJUyZ+bJ4E9k4/9ENg424NcpCq5N8PAjyYf+zFTK1qieipkyK9OAAAFOklEQVT9ntYL/GKy\nojgvSThc2PYC7wVQVfUdQH55ez+wVVVVl6qqzcDbyPzLeu41wHuAZ2o33EWlknsjaqPsezMZDL8B\n/JqmaS/WesCLSCX35m8m7w9k/nU3VcsBLyLl3pv9mqap2TVtwIimaR+o9aAXiUr+vvkH4NbJY94F\nHKjdcBeVSu7Ns1z4nvZO4JXaDXdRqfR7Wi+ZZVnzliWdTs/1GMQsyeu01EOmg+LvkPlBP65p2o8m\nOy19nMw/EnxV07RHVVW9iMy6Ni8wBOzUNG1iTt7AAlbJvcl77V3AOulWOjsq/HPzEqAA5ydPo2ma\nJs2cqqzCe7OOzLq2NJlg+D80TXt1Tt7AAjaT/6ZNvv68pmlLajzsRaHCPzeXkunAbAEmyHRqPDcn\nb2ABq/DetJEJ70uBOPDbmqa9MRfjX8gq/W+aqqr/AXxB07TDczPymZNwKIQQQgghhBBCppUKIYQQ\nQgghhJBwKIQQQgghhBACCYdCCCGEEEIIIZBwKIQQQgghhBACCYdCCCGEEEIIIZBwKIQQQgghhBAC\nsM/1AIQQQoham9y4+Luapu0o83X3A2iadn+Jx28B/rumaR8r8zorgac0TVtpcoyHzF5068jsw/UV\nTdO+P/nc/wTuJvOPwJ/XNO3fyrm+EEKIxUnCoRBCiMWoFbhiti+iadqLQFnBsAyfB05pmna7qqqd\nwGFVVfcAFwMfIvP+moDnVVV9StO0kVkahxBCiAVCwqEQQojF6P8Ay1RV/SHwQ+CzZKpsB4BPaZoW\nUVV1J3APkAZeIFOJA7hKVdXngOXAP2qadr+qqncBvwa0AauAn2ma9klVVW8A7tc07QZVVa8A/g5o\nAEaA3wLOA98C1gMXARrwmyW+h6cnj0fTtAFVVUeAJcB7gX/TNC0CRFRVfQp4H/BPZX9KQgghFhVZ\ncyiEEGIx+jRwlkz4uxu4VtO0K4AB4H+pqroc+N/AuzVNeztgA3598rUXATcCm4HfV1XVO/n4tcCt\nQA9wi6qqlxdc85+BP9U07XLg+8BnJl8T0zTtGmAN4CYT7orSNO0JTdNOAaiqegegAK8Ay4BzeYee\nA7pKOacQQojFTSqHQgghFrMbgW5gn6qqAE7gIHANsFfTtDMAmqbdCTBZ/fuppmlRIKqq6hCZaiHA\nc5qmBSaPO5n3OKqqdgBLNU37yeT5vpX33LCqqp8is3awG/CU8wZUVX0/8FfAr2na/9/OHcJUFcVx\nHP8yXnBkdDOafsVOcNOgZnVWnIWqjmAkYHIzOBOR4gxWAjPoaDoIBNKpMtjYLDadbz7D/+LeGOIT\nAm73+0n3nXvfuaf+7v9/ThsmmTrmsZ//MqckqZ8Mh5KkPpsG3rbWHsPvQ14GwI3xh5JcHPs5HLse\nUYfBAHz7wzjAjyPzXaAqfFeBZ8ArYBWYPfK/EyV5BDylKpw73fAe1V566DLVgipJ0olsK5Uk9dGQ\nCoEbwL0kl7qK2wq1/3ALmEtyGLJeAndO+7LW2ldgN8ntbugBFQpvUeF0ldp/eJ0KrH+V5C6wCFwb\nC4YA68D9JDNdqL0JvD/t2iVJ/WHlUJLURwfAZ6odcxn4QH0w3QaedwfSPAHeJZkGPlKVvaUzvHMe\nWEnyAvhCBcRZ4E3XGvod+ARcmXC+ZWqP4lrXEguw0FrbTPKaCrgDYKm1tneGdUuSemJqNBqd9xok\nSZIkSefMyqEkSf+pJIvAw2Nu7bfWJjrVVJKkSVk5lCRJkiR5II0kSZIkyXAoSZIkScJwKEmSJEnC\ncChJkiRJwnAoSZIkSQJ+AeafE7TJEvMqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b558eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot(train, 'technical_20', 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NNtkNjSXw/EsnMBLNzTQu7/BiPJ7CSFTNz9g9+dAq7D/QbTgjWU65IGz1XSlOqWEC11Q+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\nlCRJkiRhcShJkiRJwuJQkiRJkgT8BzfPKJf13K+kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095cd278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot(train, 'technical_30', 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/eRJjEzFIDhFAGq+dvYpzF0NldxMtd7+hUxJx4M6Ns35utRqoJjTEkyk0eSXd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\nOdX2AqZP4SwPJIuB4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bVSD9dsPW/O1UqLlUKBqa+L5/UXAqJEMkNvVwtbL+zG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\nERERAf5/KqeUhNS4BDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f47518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_plot(train, 'technical_13', 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_max = train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                2.158000e+03\n",
       "timestamp         1.812000e+03\n",
       "derived_0         3.252527e+03\n",
       "derived_1         1.068448e+16\n",
       "derived_2         3.823001e+03\n",
       "derived_3         1.239737e+03\n",
       "derived_4         6.785965e+04\n",
       "fundamental_0     1.378195e+00\n",
       "fundamental_1     5.203165e+02\n",
       "fundamental_2     7.677125e+01\n",
       "fundamental_3     6.552332e+00\n",
       "fundamental_5     1.535051e+03\n",
       "fundamental_6     1.214948e+02\n",
       "fundamental_7     5.221653e+05\n",
       "fundamental_8     3.943310e+01\n",
       "fundamental_9     3.965863e+01\n",
       "fundamental_10    3.454060e+02\n",
       "fundamental_11    1.167478e+02\n",
       "fundamental_12    9.270403e+04\n",
       "fundamental_13    1.445207e+03\n",
       "fundamental_14    4.058832e+04\n",
       "fundamental_15    2.542647e+02\n",
       "fundamental_16    8.544242e-01\n",
       "fundamental_17    1.040279e+18\n",
       "fundamental_18    4.889408e+01\n",
       "fundamental_19    1.152071e+03\n",
       "fundamental_20    1.782451e+03\n",
       "fundamental_21    3.505455e+00\n",
       "fundamental_22    2.690963e+02\n",
       "fundamental_23    1.438253e+05\n",
       "                      ...     \n",
       "technical_13      7.339227e-03\n",
       "technical_14      0.000000e+00\n",
       "technical_16      1.000000e+00\n",
       "technical_17      0.000000e+00\n",
       "technical_18      0.000000e+00\n",
       "technical_19      6.395316e+01\n",
       "technical_20      6.784815e-02\n",
       "technical_21      1.029848e+03\n",
       "technical_22      5.000000e-01\n",
       "technical_24      4.352512e-01\n",
       "technical_25      1.579077e-01\n",
       "technical_27      2.180798e+01\n",
       "technical_28      1.339475e-01\n",
       "technical_29      0.000000e+00\n",
       "technical_30      7.195023e-02\n",
       "technical_31      2.014349e-01\n",
       "technical_32      0.000000e+00\n",
       "technical_33      1.322735e+00\n",
       "technical_34      5.000000e-01\n",
       "technical_35      1.464272e+01\n",
       "technical_36      4.957758e+01\n",
       "technical_37      0.000000e+00\n",
       "technical_38      0.000000e+00\n",
       "technical_39      0.000000e+00\n",
       "technical_40      1.569265e+00\n",
       "technical_41      6.844833e-01\n",
       "technical_42      1.000000e+00\n",
       "technical_43      0.000000e+00\n",
       "technical_44      1.435858e-01\n",
       "y                 9.349781e-02\n",
       "dtype: float32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaled = cols_max * 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                2.158000e+00\n",
       "timestamp         1.812000e+00\n",
       "derived_0         3.252527e+00\n",
       "derived_1         1.068448e+13\n",
       "derived_2         3.823001e+00\n",
       "derived_3         1.239738e+00\n",
       "derived_4         6.785965e+01\n",
       "fundamental_0     1.378195e-03\n",
       "fundamental_1     5.203165e-01\n",
       "fundamental_2     7.677125e-02\n",
       "fundamental_3     6.552332e-03\n",
       "fundamental_5     1.535051e+00\n",
       "fundamental_6     1.214948e-01\n",
       "fundamental_7     5.221653e+02\n",
       "fundamental_8     3.943310e-02\n",
       "fundamental_9     3.965864e-02\n",
       "fundamental_10    3.454060e-01\n",
       "fundamental_11    1.167478e-01\n",
       "fundamental_12    9.270403e+01\n",
       "fundamental_13    1.445207e+00\n",
       "fundamental_14    4.058833e+01\n",
       "fundamental_15    2.542647e-01\n",
       "fundamental_16    8.544243e-04\n",
       "fundamental_17    1.040279e+15\n",
       "fundamental_18    4.889408e-02\n",
       "fundamental_19    1.152071e+00\n",
       "fundamental_20    1.782452e+00\n",
       "fundamental_21    3.505455e-03\n",
       "fundamental_22    2.690963e-01\n",
       "fundamental_23    1.438253e+02\n",
       "                      ...     \n",
       "technical_13      7.339228e-06\n",
       "technical_14      0.000000e+00\n",
       "technical_16      1.000000e-03\n",
       "technical_17      0.000000e+00\n",
       "technical_18      0.000000e+00\n",
       "technical_19      6.395316e-02\n",
       "technical_20      6.784816e-05\n",
       "technical_21      1.029848e+00\n",
       "technical_22      5.000000e-04\n",
       "technical_24      4.352512e-04\n",
       "technical_25      1.579078e-04\n",
       "technical_27      2.180798e-02\n",
       "technical_28      1.339475e-04\n",
       "technical_29      0.000000e+00\n",
       "technical_30      7.195023e-05\n",
       "technical_31      2.014349e-04\n",
       "technical_32      0.000000e+00\n",
       "technical_33      1.322735e-03\n",
       "technical_34      5.000000e-04\n",
       "technical_35      1.464272e-02\n",
       "technical_36      4.957758e-02\n",
       "technical_37      0.000000e+00\n",
       "technical_38      0.000000e+00\n",
       "technical_39      0.000000e+00\n",
       "technical_40      1.569265e-03\n",
       "technical_41      6.844834e-04\n",
       "technical_42      1.000000e-03\n",
       "technical_43      0.000000e+00\n",
       "technical_44      1.435859e-04\n",
       "y                 9.349781e-05\n",
       "dtype: float32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_sm = train[['id', 'technical_20', 'technical_30', 'technical_13']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_sm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 0\n",
       "timestamp          0\n",
       "y                  0\n",
       "technical_20    4764\n",
       "technical_30    4764\n",
       "technical_13    4764\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_sm = train_sm.loc[train.technical_20.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sm_med = train_sm.groupby('id').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>technical_20</th>\n",
       "      <th>technical_30</th>\n",
       "      <th>technical_13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.002420</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.000264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.000422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.000417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.001016</td>\n",
       "      <td>0.000367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.000567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>0.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000610</td>\n",
       "      <td>0.000561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.001772</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.000325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.000340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>0.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.001324</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.000350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.003440</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.003016</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.000166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.000815</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.000459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.000369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.001594</td>\n",
       "      <td>0.000664</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.000854</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.001841</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>0.003830</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>0.001830</td>\n",
       "      <td>0.001641</td>\n",
       "      <td>0.000264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.002138</td>\n",
       "      <td>0.000181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>0.001907</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.000551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.002363</td>\n",
       "      <td>0.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>0.000682</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.000320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>0.002574</td>\n",
       "      <td>0.001159</td>\n",
       "      <td>0.000205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>0.001830</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.000165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>0.001264</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.000269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.001227</td>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>0.000946</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>0.002430</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>0.001813</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.000263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.000374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>0.001830</td>\n",
       "      <td>0.001977</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2148</th>\n",
       "      <td>0.001411</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>0.000764</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.000377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.000396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>0.002125</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.000328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>0.001279</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>0.001104</td>\n",
       "      <td>0.001063</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>0.006097</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1424 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      technical_20  technical_30  technical_13\n",
       "id                                            \n",
       "0         0.001171      0.000679      0.000331\n",
       "6         0.000808      0.000805      0.000414\n",
       "7         0.001357      0.001115      0.000303\n",
       "10        0.002420      0.002482      0.000264\n",
       "11        0.000528      0.000614      0.000422\n",
       "12        0.000807      0.000652      0.000417\n",
       "13        0.000820      0.001016      0.000367\n",
       "14        0.001282      0.000775      0.000567\n",
       "15        0.001692      0.000959      0.000253\n",
       "16        0.003347      0.002351      0.000153\n",
       "17        0.002084      0.000902      0.000323\n",
       "18        0.000222      0.000610      0.000561\n",
       "19        0.001772      0.001751      0.000191\n",
       "20        0.000879      0.000714      0.000325\n",
       "22        0.000964      0.000687      0.000340\n",
       "23        0.001826      0.002171      0.000297\n",
       "24        0.000757      0.000703      0.000376\n",
       "25        0.001324      0.000744      0.000350\n",
       "26        0.000851      0.000655      0.000366\n",
       "27        0.003440      0.000601      0.000240\n",
       "30        0.002241      0.002096      0.000132\n",
       "31        0.003016      0.002058      0.000166\n",
       "32        0.001549      0.000780      0.000485\n",
       "33        0.001587      0.001567      0.000379\n",
       "38        0.000815      0.000603      0.000459\n",
       "39        0.000851      0.000738      0.000369\n",
       "40        0.001594      0.000664      0.000323\n",
       "41        0.000854      0.000679      0.000370\n",
       "43        0.001841      0.001785      0.000243\n",
       "44        0.000697      0.000674      0.000321\n",
       "...            ...           ...           ...\n",
       "2115      0.003830      0.002112      0.000111\n",
       "2117      0.001830      0.001641      0.000264\n",
       "2118      0.001561      0.001690      0.000243\n",
       "2120      0.002058      0.002138      0.000181\n",
       "2121      0.001907      0.000392      0.000551\n",
       "2124      0.000820      0.000670      0.000525\n",
       "2125      0.002959      0.002363      0.000125\n",
       "2126      0.000682      0.000895      0.000385\n",
       "2129      0.001108      0.000879      0.000320\n",
       "2130      0.002574      0.001159      0.000205\n",
       "2131      0.002036      0.001666      0.000228\n",
       "2134      0.001830      0.001898      0.000165\n",
       "2135      0.001264      0.000449      0.000237\n",
       "2136      0.001320      0.001205      0.000269\n",
       "2137      0.001258      0.001227      0.000279\n",
       "2138      0.000946      0.000503      0.000370\n",
       "2139      0.002430      0.001194      0.000244\n",
       "2140      0.001813      0.001172      0.000263\n",
       "2142      0.000871      0.000661      0.000374\n",
       "2145      0.000932      0.000644      0.000360\n",
       "2146      0.001830      0.001977      0.000196\n",
       "2148      0.001411      0.001176      0.000248\n",
       "2149      0.000939      0.000805      0.000306\n",
       "2150      0.000764      0.000634      0.000377\n",
       "2151      0.000756      0.000542      0.000396\n",
       "2152      0.002125      0.000358      0.000457\n",
       "2154      0.001326      0.000743      0.000328\n",
       "2155      0.001279      0.001919      0.000329\n",
       "2156      0.001104      0.001063      0.000332\n",
       "2158      0.006097      0.000874      0.000102\n",
       "\n",
       "[1424 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_med.fillna(sm_med.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    6,    7,   10,   11,   12,   13,   14,   15,   16,\n",
       "            ...\n",
       "            2146, 2148, 2149, 2150, 2151, 2152, 2154, 2155, 2156, 2158],\n",
       "           dtype='int64', name='id', length=1424)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_med.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inert = []\n",
    "for n in range(3,10):\n",
    "    km = KMeans(n_clusters=n, n_jobs=-2, random_state=11, n_init=20)\n",
    "    km.fit(sm_med[['technical_20', 'technical_30', 'technical_13']])\n",
    "    score = silhouette_score(sm_med[['technical_20', 'technical_30', 'technical_13']], km.labels_)\n",
    "    inert.append((n,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 0.54040474),\n",
       " (4, 0.52124578),\n",
       " (5, 0.43464524),\n",
       " (6, 0.43341443),\n",
       " (7, 0.39947894),\n",
       " (8, 0.36665142),\n",
       " (9, 0.35862175)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=30, n_jobs=-2, precompute_distances='auto',\n",
       "    random_state=11, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=5, n_jobs=-2, random_state=11, n_init=30)\n",
    "km.fit(sm_med[['technical_20', 'technical_30', 'technical_13']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 0, 4, 3], dtype=int32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    6,    7,   10,   11,   12,   13,   14,   15,   16,\n",
       "            ...\n",
       "            2146, 2148, 2149, 2150, 2151, 2152, 2154, 2155, 2156, 2158],\n",
       "           dtype='int64', name='id', length=1424)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_med.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "group_id = defaultdict(list)\n",
    "for id_, group in zip(sm_med.index, km.labels_):\n",
    "    group_id[group].append(id_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {0: [10,\n",
       "              17,\n",
       "              19,\n",
       "              23,\n",
       "              27,\n",
       "              30,\n",
       "              31,\n",
       "              33,\n",
       "              43,\n",
       "              49,\n",
       "              52,\n",
       "              55,\n",
       "              56,\n",
       "              58,\n",
       "              59,\n",
       "              62,\n",
       "              66,\n",
       "              68,\n",
       "              77,\n",
       "              79,\n",
       "              81,\n",
       "              82,\n",
       "              87,\n",
       "              95,\n",
       "              99,\n",
       "              105,\n",
       "              108,\n",
       "              128,\n",
       "              132,\n",
       "              137,\n",
       "              142,\n",
       "              149,\n",
       "              150,\n",
       "              151,\n",
       "              158,\n",
       "              175,\n",
       "              176,\n",
       "              179,\n",
       "              183,\n",
       "              185,\n",
       "              187,\n",
       "              198,\n",
       "              199,\n",
       "              205,\n",
       "              208,\n",
       "              212,\n",
       "              215,\n",
       "              217,\n",
       "              220,\n",
       "              221,\n",
       "              231,\n",
       "              241,\n",
       "              242,\n",
       "              244,\n",
       "              249,\n",
       "              252,\n",
       "              253,\n",
       "              257,\n",
       "              259,\n",
       "              268,\n",
       "              271,\n",
       "              273,\n",
       "              274,\n",
       "              275,\n",
       "              288,\n",
       "              307,\n",
       "              308,\n",
       "              310,\n",
       "              312,\n",
       "              317,\n",
       "              329,\n",
       "              333,\n",
       "              341,\n",
       "              346,\n",
       "              352,\n",
       "              356,\n",
       "              357,\n",
       "              364,\n",
       "              365,\n",
       "              372,\n",
       "              381,\n",
       "              383,\n",
       "              384,\n",
       "              396,\n",
       "              397,\n",
       "              398,\n",
       "              402,\n",
       "              403,\n",
       "              408,\n",
       "              410,\n",
       "              412,\n",
       "              416,\n",
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       "              437,\n",
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       "              450,\n",
       "              451,\n",
       "              454,\n",
       "              458,\n",
       "              472,\n",
       "              481,\n",
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       "              536,\n",
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       "              555,\n",
       "              561,\n",
       "              570,\n",
       "              571,\n",
       "              578,\n",
       "              582,\n",
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       "              595,\n",
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       "              600,\n",
       "              606,\n",
       "              608,\n",
       "              614,\n",
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       "              622,\n",
       "              629,\n",
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       "              637,\n",
       "              641,\n",
       "              646,\n",
       "              648,\n",
       "              653,\n",
       "              659,\n",
       "              663,\n",
       "              676,\n",
       "              683,\n",
       "              687,\n",
       "              690,\n",
       "              699,\n",
       "              703,\n",
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       "              722,\n",
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       "              761,\n",
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       "              777,\n",
       "              783,\n",
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       "              805,\n",
       "              808,\n",
       "              810,\n",
       "              831,\n",
       "              833,\n",
       "              838,\n",
       "              841,\n",
       "              845,\n",
       "              846,\n",
       "              848,\n",
       "              849,\n",
       "              852,\n",
       "              866,\n",
       "              868,\n",
       "              870,\n",
       "              872,\n",
       "              873,\n",
       "              874,\n",
       "              884,\n",
       "              889,\n",
       "              895,\n",
       "              896,\n",
       "              910,\n",
       "              912,\n",
       "              914,\n",
       "              926,\n",
       "              929,\n",
       "              932,\n",
       "              936,\n",
       "              937,\n",
       "              942,\n",
       "              943,\n",
       "              949,\n",
       "              952,\n",
       "              958,\n",
       "              963,\n",
       "              966,\n",
       "              970,\n",
       "              979,\n",
       "              983,\n",
       "              984,\n",
       "              991,\n",
       "              992,\n",
       "              1002,\n",
       "              1003,\n",
       "              1007,\n",
       "              1012,\n",
       "              1018,\n",
       "              1021,\n",
       "              1025,\n",
       "              1026,\n",
       "              1034,\n",
       "              1035,\n",
       "              1037,\n",
       "              1038,\n",
       "              1039,\n",
       "              1048,\n",
       "              1049,\n",
       "              1063,\n",
       "              1066,\n",
       "              1070,\n",
       "              1071,\n",
       "              1072,\n",
       "              1078,\n",
       "              1086,\n",
       "              1093,\n",
       "              1095,\n",
       "              1105,\n",
       "              1108,\n",
       "              1112,\n",
       "              1113,\n",
       "              1130,\n",
       "              1131,\n",
       "              1134,\n",
       "              1135,\n",
       "              1136,\n",
       "              1137,\n",
       "              1139,\n",
       "              1152,\n",
       "              1161,\n",
       "              1164,\n",
       "              1168,\n",
       "              1170,\n",
       "              1175,\n",
       "              1177,\n",
       "              1178,\n",
       "              1181,\n",
       "              1187,\n",
       "              1192,\n",
       "              1193,\n",
       "              1195,\n",
       "              1196,\n",
       "              1199,\n",
       "              1200,\n",
       "              1207,\n",
       "              1208,\n",
       "              1210,\n",
       "              1213,\n",
       "              1223,\n",
       "              1224,\n",
       "              1226,\n",
       "              1231,\n",
       "              1232,\n",
       "              1235,\n",
       "              1236,\n",
       "              1241,\n",
       "              1244,\n",
       "              1258,\n",
       "              1260,\n",
       "              1266,\n",
       "              1269,\n",
       "              1271,\n",
       "              1274,\n",
       "              1281,\n",
       "              1288,\n",
       "              1290,\n",
       "              1292,\n",
       "              1297,\n",
       "              1303,\n",
       "              1307,\n",
       "              1310,\n",
       "              1313,\n",
       "              1318,\n",
       "              1326,\n",
       "              1331,\n",
       "              1336,\n",
       "              1343,\n",
       "              1348,\n",
       "              1352,\n",
       "              1357,\n",
       "              1359,\n",
       "              1363,\n",
       "              1370,\n",
       "              1375,\n",
       "              1377,\n",
       "              1378,\n",
       "              1379,\n",
       "              1382,\n",
       "              1383,\n",
       "              1387,\n",
       "              1390,\n",
       "              1392,\n",
       "              1402,\n",
       "              1406,\n",
       "              1408,\n",
       "              1413,\n",
       "              1414,\n",
       "              1415,\n",
       "              1427,\n",
       "              1434,\n",
       "              1436,\n",
       "              1438,\n",
       "              1439,\n",
       "              1440,\n",
       "              1451,\n",
       "              1452,\n",
       "              1454,\n",
       "              1457,\n",
       "              1460,\n",
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       "              1474,\n",
       "              1477,\n",
       "              1482,\n",
       "              1483,\n",
       "              1492,\n",
       "              1494,\n",
       "              1497,\n",
       "              1499,\n",
       "              1507,\n",
       "              1514,\n",
       "              1516,\n",
       "              1518,\n",
       "              1519,\n",
       "              1521,\n",
       "              1524,\n",
       "              1530,\n",
       "              1532,\n",
       "              1539,\n",
       "              1541,\n",
       "              1543,\n",
       "              1545,\n",
       "              1546,\n",
       "              1553,\n",
       "              1555,\n",
       "              1561,\n",
       "              1564,\n",
       "              1566,\n",
       "              1570,\n",
       "              1575,\n",
       "              1578,\n",
       "              1583,\n",
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       "              1589,\n",
       "              1599,\n",
       "              1603,\n",
       "              1604,\n",
       "              1607,\n",
       "              1610,\n",
       "              1611,\n",
       "              1614,\n",
       "              1615,\n",
       "              1628,\n",
       "              1629,\n",
       "              1635,\n",
       "              1638,\n",
       "              1653,\n",
       "              1654,\n",
       "              1660,\n",
       "              1663,\n",
       "              1666,\n",
       "              1672,\n",
       "              1681,\n",
       "              1683,\n",
       "              1686,\n",
       "              1701,\n",
       "              1703,\n",
       "              1708,\n",
       "              1726,\n",
       "              1727,\n",
       "              1728,\n",
       "              1737,\n",
       "              1741,\n",
       "              1747,\n",
       "              1756,\n",
       "              1767,\n",
       "              1772,\n",
       "              1774,\n",
       "              1775,\n",
       "              1776,\n",
       "              1780,\n",
       "              1785,\n",
       "              1787,\n",
       "              1791,\n",
       "              1793,\n",
       "              1798,\n",
       "              1808,\n",
       "              1813,\n",
       "              1818,\n",
       "              1821,\n",
       "              1825,\n",
       "              1828,\n",
       "              1836,\n",
       "              1840,\n",
       "              1842,\n",
       "              1843,\n",
       "              1848,\n",
       "              1852,\n",
       "              1860,\n",
       "              1865,\n",
       "              1867,\n",
       "              1872,\n",
       "              1874,\n",
       "              1876,\n",
       "              1877,\n",
       "              1882,\n",
       "              1885,\n",
       "              1888,\n",
       "              1892,\n",
       "              1895,\n",
       "              1903,\n",
       "              1916,\n",
       "              1923,\n",
       "              1936,\n",
       "              1943,\n",
       "              1950,\n",
       "              1957,\n",
       "              1966,\n",
       "              1967,\n",
       "              1972,\n",
       "              1973,\n",
       "              1975,\n",
       "              1976,\n",
       "              1980,\n",
       "              1981,\n",
       "              1998,\n",
       "              2000,\n",
       "              2002,\n",
       "              2009,\n",
       "              2011,\n",
       "              2021,\n",
       "              2027,\n",
       "              2028,\n",
       "              2030,\n",
       "              2031,\n",
       "              2037,\n",
       "              2044,\n",
       "              2047,\n",
       "              2050,\n",
       "              2054,\n",
       "              2058,\n",
       "              2059,\n",
       "              2065,\n",
       "              2069,\n",
       "              2070,\n",
       "              2076,\n",
       "              2077,\n",
       "              2081,\n",
       "              2082,\n",
       "              2092,\n",
       "              2097,\n",
       "              2109,\n",
       "              2117,\n",
       "              2118,\n",
       "              2120,\n",
       "              2130,\n",
       "              2131,\n",
       "              2134,\n",
       "              2139,\n",
       "              2140,\n",
       "              2146,\n",
       "              2155],\n",
       "             1: [93, 1431],\n",
       "             2: [0,\n",
       "              6,\n",
       "              7,\n",
       "              11,\n",
       "              12,\n",
       "              13,\n",
       "              14,\n",
       "              15,\n",
       "              18,\n",
       "              20,\n",
       "              22,\n",
       "              24,\n",
       "              25,\n",
       "              26,\n",
       "              32,\n",
       "              38,\n",
       "              39,\n",
       "              40,\n",
       "              41,\n",
       "              44,\n",
       "              46,\n",
       "              47,\n",
       "              48,\n",
       "              54,\n",
       "              60,\n",
       "              64,\n",
       "              65,\n",
       "              69,\n",
       "              70,\n",
       "              72,\n",
       "              76,\n",
       "              80,\n",
       "              83,\n",
       "              85,\n",
       "              88,\n",
       "              90,\n",
       "              91,\n",
       "              97,\n",
       "              98,\n",
       "              101,\n",
       "              103,\n",
       "              104,\n",
       "              106,\n",
       "              111,\n",
       "              112,\n",
       "              113,\n",
       "              118,\n",
       "              119,\n",
       "              121,\n",
       "              124,\n",
       "              126,\n",
       "              127,\n",
       "              130,\n",
       "              131,\n",
       "              133,\n",
       "              134,\n",
       "              140,\n",
       "              141,\n",
       "              152,\n",
       "              154,\n",
       "              156,\n",
       "              159,\n",
       "              160,\n",
       "              161,\n",
       "              163,\n",
       "              166,\n",
       "              169,\n",
       "              171,\n",
       "              172,\n",
       "              173,\n",
       "              174,\n",
       "              177,\n",
       "              178,\n",
       "              184,\n",
       "              186,\n",
       "              188,\n",
       "              191,\n",
       "              195,\n",
       "              203,\n",
       "              206,\n",
       "              214,\n",
       "              216,\n",
       "              218,\n",
       "              219,\n",
       "              222,\n",
       "              223,\n",
       "              224,\n",
       "              225,\n",
       "              226,\n",
       "              227,\n",
       "              229,\n",
       "              232,\n",
       "              233,\n",
       "              235,\n",
       "              239,\n",
       "              240,\n",
       "              245,\n",
       "              248,\n",
       "              255,\n",
       "              258,\n",
       "              261,\n",
       "              262,\n",
       "              264,\n",
       "              265,\n",
       "              266,\n",
       "              269,\n",
       "              270,\n",
       "              277,\n",
       "              278,\n",
       "              279,\n",
       "              280,\n",
       "              281,\n",
       "              284,\n",
       "              285,\n",
       "              286,\n",
       "              287,\n",
       "              290,\n",
       "              294,\n",
       "              295,\n",
       "              296,\n",
       "              298,\n",
       "              300,\n",
       "              302,\n",
       "              303,\n",
       "              304,\n",
       "              305,\n",
       "              306,\n",
       "              318,\n",
       "              319,\n",
       "              321,\n",
       "              324,\n",
       "              325,\n",
       "              330,\n",
       "              334,\n",
       "              338,\n",
       "              340,\n",
       "              344,\n",
       "              347,\n",
       "              348,\n",
       "              349,\n",
       "              351,\n",
       "              355,\n",
       "              358,\n",
       "              362,\n",
       "              363,\n",
       "              366,\n",
       "              377,\n",
       "              378,\n",
       "              379,\n",
       "              380,\n",
       "              382,\n",
       "              386,\n",
       "              388,\n",
       "              389,\n",
       "              391,\n",
       "              392,\n",
       "              393,\n",
       "              395,\n",
       "              399,\n",
       "              400,\n",
       "              404,\n",
       "              407,\n",
       "              411,\n",
       "              413,\n",
       "              414,\n",
       "              417,\n",
       "              423,\n",
       "              424,\n",
       "              425,\n",
       "              429,\n",
       "              430,\n",
       "              431,\n",
       "              435,\n",
       "              438,\n",
       "              441,\n",
       "              445,\n",
       "              448,\n",
       "              452,\n",
       "              453,\n",
       "              456,\n",
       "              459,\n",
       "              461,\n",
       "              462,\n",
       "              467,\n",
       "              468,\n",
       "              469,\n",
       "              473,\n",
       "              474,\n",
       "              479,\n",
       "              480,\n",
       "              483,\n",
       "              484,\n",
       "              490,\n",
       "              492,\n",
       "              494,\n",
       "              500,\n",
       "              501,\n",
       "              505,\n",
       "              506,\n",
       "              507,\n",
       "              509,\n",
       "              510,\n",
       "              512,\n",
       "              513,\n",
       "              517,\n",
       "              520,\n",
       "              521,\n",
       "              527,\n",
       "              529,\n",
       "              530,\n",
       "              531,\n",
       "              532,\n",
       "              539,\n",
       "              540,\n",
       "              542,\n",
       "              543,\n",
       "              545,\n",
       "              548,\n",
       "              550,\n",
       "              554,\n",
       "              556,\n",
       "              557,\n",
       "              558,\n",
       "              559,\n",
       "              565,\n",
       "              567,\n",
       "              568,\n",
       "              569,\n",
       "              572,\n",
       "              573,\n",
       "              575,\n",
       "              576,\n",
       "              577,\n",
       "              579,\n",
       "              580,\n",
       "              584,\n",
       "              585,\n",
       "              586,\n",
       "              588,\n",
       "              591,\n",
       "              592,\n",
       "              593,\n",
       "              601,\n",
       "              602,\n",
       "              604,\n",
       "              605,\n",
       "              609,\n",
       "              610,\n",
       "              611,\n",
       "              613,\n",
       "              615,\n",
       "              617,\n",
       "              619,\n",
       "              620,\n",
       "              621,\n",
       "              624,\n",
       "              625,\n",
       "              631,\n",
       "              633,\n",
       "              638,\n",
       "              639,\n",
       "              640,\n",
       "              642,\n",
       "              644,\n",
       "              649,\n",
       "              650,\n",
       "              651,\n",
       "              654,\n",
       "              656,\n",
       "              657,\n",
       "              662,\n",
       "              667,\n",
       "              669,\n",
       "              672,\n",
       "              677,\n",
       "              679,\n",
       "              684,\n",
       "              686,\n",
       "              689,\n",
       "              691,\n",
       "              693,\n",
       "              694,\n",
       "              695,\n",
       "              697,\n",
       "              698,\n",
       "              700,\n",
       "              701,\n",
       "              704,\n",
       "              707,\n",
       "              708,\n",
       "              709,\n",
       "              711,\n",
       "              713,\n",
       "              715,\n",
       "              719,\n",
       "              720,\n",
       "              723,\n",
       "              727,\n",
       "              728,\n",
       "              729,\n",
       "              736,\n",
       "              740,\n",
       "              743,\n",
       "              748,\n",
       "              751,\n",
       "              752,\n",
       "              753,\n",
       "              757,\n",
       "              758,\n",
       "              762,\n",
       "              763,\n",
       "              765,\n",
       "              773,\n",
       "              776,\n",
       "              780,\n",
       "              788,\n",
       "              789,\n",
       "              791,\n",
       "              792,\n",
       "              794,\n",
       "              796,\n",
       "              797,\n",
       "              798,\n",
       "              799,\n",
       "              800,\n",
       "              804,\n",
       "              806,\n",
       "              809,\n",
       "              811,\n",
       "              815,\n",
       "              816,\n",
       "              817,\n",
       "              820,\n",
       "              828,\n",
       "              829,\n",
       "              830,\n",
       "              832,\n",
       "              839,\n",
       "              842,\n",
       "              844,\n",
       "              853,\n",
       "              855,\n",
       "              856,\n",
       "              858,\n",
       "              859,\n",
       "              860,\n",
       "              861,\n",
       "              863,\n",
       "              864,\n",
       "              865,\n",
       "              867,\n",
       "              869,\n",
       "              871,\n",
       "              880,\n",
       "              881,\n",
       "              885,\n",
       "              888,\n",
       "              890,\n",
       "              892,\n",
       "              897,\n",
       "              898,\n",
       "              901,\n",
       "              906,\n",
       "              909,\n",
       "              911,\n",
       "              913,\n",
       "              915,\n",
       "              916,\n",
       "              918,\n",
       "              920,\n",
       "              924,\n",
       "              925,\n",
       "              934,\n",
       "              935,\n",
       "              941,\n",
       "              944,\n",
       "              946,\n",
       "              953,\n",
       "              954,\n",
       "              955,\n",
       "              959,\n",
       "              961,\n",
       "              964,\n",
       "              967,\n",
       "              968,\n",
       "              972,\n",
       "              973,\n",
       "              975,\n",
       "              976,\n",
       "              977,\n",
       "              978,\n",
       "              980,\n",
       "              982,\n",
       "              985,\n",
       "              988,\n",
       "              990,\n",
       "              993,\n",
       "              994,\n",
       "              999,\n",
       "              1000,\n",
       "              1004,\n",
       "              1008,\n",
       "              1009,\n",
       "              1011,\n",
       "              1013,\n",
       "              1015,\n",
       "              1016,\n",
       "              1017,\n",
       "              1023,\n",
       "              1024,\n",
       "              1030,\n",
       "              1033,\n",
       "              1036,\n",
       "              1042,\n",
       "              1043,\n",
       "              1045,\n",
       "              1046,\n",
       "              1047,\n",
       "              1050,\n",
       "              1053,\n",
       "              1056,\n",
       "              1057,\n",
       "              1059,\n",
       "              1060,\n",
       "              1064,\n",
       "              1065,\n",
       "              1067,\n",
       "              1069,\n",
       "              1073,\n",
       "              1075,\n",
       "              1076,\n",
       "              1083,\n",
       "              1085,\n",
       "              1087,\n",
       "              1088,\n",
       "              1090,\n",
       "              1094,\n",
       "              1098,\n",
       "              1101,\n",
       "              1103,\n",
       "              1107,\n",
       "              1114,\n",
       "              1116,\n",
       "              1120,\n",
       "              1125,\n",
       "              1126,\n",
       "              1128,\n",
       "              1129,\n",
       "              1133,\n",
       "              1138,\n",
       "              1140,\n",
       "              1142,\n",
       "              1143,\n",
       "              1144,\n",
       "              1146,\n",
       "              1150,\n",
       "              1153,\n",
       "              1155,\n",
       "              1157,\n",
       "              1159,\n",
       "              1165,\n",
       "              1166,\n",
       "              1172,\n",
       "              1174,\n",
       "              1176,\n",
       "              1186,\n",
       "              1194,\n",
       "              1197,\n",
       "              1201,\n",
       "              1204,\n",
       "              1205,\n",
       "              1209,\n",
       "              1212,\n",
       "              1215,\n",
       "              1218,\n",
       "              1220,\n",
       "              1221,\n",
       "              1225,\n",
       "              1227,\n",
       "              1228,\n",
       "              1229,\n",
       "              1230,\n",
       "              1238,\n",
       "              1239,\n",
       "              1240,\n",
       "              1243,\n",
       "              1246,\n",
       "              1247,\n",
       "              1249,\n",
       "              1253,\n",
       "              1255,\n",
       "              1256,\n",
       "              1257,\n",
       "              1259,\n",
       "              1261,\n",
       "              1263,\n",
       "              1264,\n",
       "              1265,\n",
       "              1267,\n",
       "              1270,\n",
       "              1272,\n",
       "              1277,\n",
       "              1279,\n",
       "              1285,\n",
       "              1286,\n",
       "              1287,\n",
       "              1291,\n",
       "              1294,\n",
       "              1295,\n",
       "              1296,\n",
       "              1298,\n",
       "              1301,\n",
       "              1304,\n",
       "              1306,\n",
       "              1308,\n",
       "              1311,\n",
       "              1314,\n",
       "              1316,\n",
       "              1319,\n",
       "              1320,\n",
       "              1322,\n",
       "              1323,\n",
       "              1324,\n",
       "              1328,\n",
       "              1329,\n",
       "              1332,\n",
       "              1334,\n",
       "              1335,\n",
       "              1339,\n",
       "              1340,\n",
       "              1342,\n",
       "              1346,\n",
       "              1349,\n",
       "              1350,\n",
       "              1354,\n",
       "              1365,\n",
       "              1367,\n",
       "              1371,\n",
       "              1373,\n",
       "              1374,\n",
       "              1376,\n",
       "              1380,\n",
       "              1381,\n",
       "              1386,\n",
       "              1393,\n",
       "              1395,\n",
       "              1396,\n",
       "              1398,\n",
       "              1401,\n",
       "              1404,\n",
       "              1407,\n",
       "              1409,\n",
       "              1410,\n",
       "              1412,\n",
       "              1416,\n",
       "              1419,\n",
       "              1421,\n",
       "              1422,\n",
       "              1424,\n",
       "              1426,\n",
       "              1441,\n",
       "              1442,\n",
       "              1443,\n",
       "              1444,\n",
       "              1445,\n",
       "              1447,\n",
       "              1448,\n",
       "              1450,\n",
       "              1459,\n",
       "              1462,\n",
       "              1464,\n",
       "              1465,\n",
       "              1466,\n",
       "              1473,\n",
       "              1476,\n",
       "              1478,\n",
       "              1479,\n",
       "              1481,\n",
       "              1485,\n",
       "              1489,\n",
       "              1491,\n",
       "              1493,\n",
       "              1495,\n",
       "              1498,\n",
       "              1500,\n",
       "              1501,\n",
       "              1502,\n",
       "              1505,\n",
       "              1506,\n",
       "              1508,\n",
       "              1511,\n",
       "              1513,\n",
       "              1515,\n",
       "              1522,\n",
       "              1523,\n",
       "              1527,\n",
       "              1534,\n",
       "              1536,\n",
       "              1537,\n",
       "              1548,\n",
       "              1550,\n",
       "              1554,\n",
       "              1560,\n",
       "              1562,\n",
       "              1565,\n",
       "              1569,\n",
       "              1573,\n",
       "              1577,\n",
       "              1579,\n",
       "              1585,\n",
       "              1588,\n",
       "              1591,\n",
       "              1593,\n",
       "              1598,\n",
       "              1602,\n",
       "              1606,\n",
       "              1612,\n",
       "              1613,\n",
       "              1616,\n",
       "              1618,\n",
       "              1619,\n",
       "              1620,\n",
       "              1624,\n",
       "              1625,\n",
       "              1630,\n",
       "              1636,\n",
       "              1639,\n",
       "              1646,\n",
       "              1647,\n",
       "              1650,\n",
       "              1652,\n",
       "              1655,\n",
       "              1657,\n",
       "              1664,\n",
       "              1667,\n",
       "              1668,\n",
       "              1669,\n",
       "              1671,\n",
       "              1676,\n",
       "              1677,\n",
       "              1679,\n",
       "              1680,\n",
       "              1685,\n",
       "              1687,\n",
       "              1690,\n",
       "              1695,\n",
       "              1696,\n",
       "              1697,\n",
       "              1699,\n",
       "              1704,\n",
       "              1705,\n",
       "              1706,\n",
       "              1711,\n",
       "              1712,\n",
       "              1714,\n",
       "              1716,\n",
       "              1717,\n",
       "              1718,\n",
       "              1724,\n",
       "              1725,\n",
       "              1729,\n",
       "              1730,\n",
       "              1731,\n",
       "              1733,\n",
       "              1734,\n",
       "              1738,\n",
       "              1739,\n",
       "              1742,\n",
       "              1743,\n",
       "              1744,\n",
       "              1745,\n",
       "              1750,\n",
       "              1752,\n",
       "              1758,\n",
       "              1759,\n",
       "              1764,\n",
       "              1766,\n",
       "              1769,\n",
       "              1782,\n",
       "              1783,\n",
       "              1784,\n",
       "              1788,\n",
       "              1789,\n",
       "              1790,\n",
       "              1792,\n",
       "              1794,\n",
       "              1795,\n",
       "              1796,\n",
       "              1802,\n",
       "              1803,\n",
       "              1806,\n",
       "              1810,\n",
       "              1812,\n",
       "              1814,\n",
       "              1815,\n",
       "              1817,\n",
       "              1827,\n",
       "              1830,\n",
       "              1831,\n",
       "              1832,\n",
       "              1838,\n",
       "              1839,\n",
       "              1845,\n",
       "              1849,\n",
       "              1851,\n",
       "              1853,\n",
       "              1854,\n",
       "              1858,\n",
       "              1861,\n",
       "              1862,\n",
       "              1863,\n",
       "              1864,\n",
       "              1866,\n",
       "              1869,\n",
       "              1870,\n",
       "              1873,\n",
       "              1878,\n",
       "              1883,\n",
       "              1884,\n",
       "              1887,\n",
       "              1890,\n",
       "              1891,\n",
       "              1893,\n",
       "              1896,\n",
       "              1897,\n",
       "              1898,\n",
       "              1899,\n",
       "              1901,\n",
       "              1902,\n",
       "              1905,\n",
       "              1906,\n",
       "              1907,\n",
       "              1910,\n",
       "              1911,\n",
       "              1912,\n",
       "              1915,\n",
       "              1918,\n",
       "              1921,\n",
       "              1925,\n",
       "              1926,\n",
       "              1927,\n",
       "              1928,\n",
       "              1931,\n",
       "              1933,\n",
       "              1935,\n",
       "              1941,\n",
       "              1945,\n",
       "              1949,\n",
       "              1951,\n",
       "              1952,\n",
       "              1954,\n",
       "              1956,\n",
       "              1962,\n",
       "              1963,\n",
       "              1965,\n",
       "              1968,\n",
       "              1969,\n",
       "              1970,\n",
       "              1971,\n",
       "              1977,\n",
       "              1979,\n",
       "              1985,\n",
       "              1987,\n",
       "              1992,\n",
       "              2001,\n",
       "              2004,\n",
       "              2006,\n",
       "              2008,\n",
       "              2013,\n",
       "              2015,\n",
       "              2018,\n",
       "              2023,\n",
       "              2024,\n",
       "              2029,\n",
       "              2033,\n",
       "              2035,\n",
       "              2036,\n",
       "              2043,\n",
       "              2055,\n",
       "              2056,\n",
       "              2057,\n",
       "              2061,\n",
       "              2062,\n",
       "              2063,\n",
       "              2064,\n",
       "              2066,\n",
       "              2068,\n",
       "              2071,\n",
       "              2074,\n",
       "              2075,\n",
       "              2078,\n",
       "              2079,\n",
       "              2080,\n",
       "              2083,\n",
       "              2087,\n",
       "              2088,\n",
       "              2089,\n",
       "              2090,\n",
       "              2095,\n",
       "              2098,\n",
       "              2100,\n",
       "              2101,\n",
       "              2102,\n",
       "              2104,\n",
       "              2107,\n",
       "              2108,\n",
       "              2111,\n",
       "              2112,\n",
       "              2114,\n",
       "              2121,\n",
       "              2124,\n",
       "              2126,\n",
       "              2129,\n",
       "              2135,\n",
       "              2136,\n",
       "              2137,\n",
       "              2138,\n",
       "              2142,\n",
       "              2145,\n",
       "              2148,\n",
       "              2149,\n",
       "              2150,\n",
       "              2151,\n",
       "              2152,\n",
       "              2154,\n",
       "              2156],\n",
       "             3: [16,\n",
       "              63,\n",
       "              100,\n",
       "              102,\n",
       "              115,\n",
       "              122,\n",
       "              170,\n",
       "              181,\n",
       "              256,\n",
       "              289,\n",
       "              297,\n",
       "              316,\n",
       "              339,\n",
       "              343,\n",
       "              353,\n",
       "              361,\n",
       "              369,\n",
       "              374,\n",
       "              406,\n",
       "              439,\n",
       "              440,\n",
       "              449,\n",
       "              470,\n",
       "              508,\n",
       "              523,\n",
       "              538,\n",
       "              551,\n",
       "              594,\n",
       "              652,\n",
       "              670,\n",
       "              682,\n",
       "              692,\n",
       "              696,\n",
       "              706,\n",
       "              731,\n",
       "              745,\n",
       "              766,\n",
       "              767,\n",
       "              785,\n",
       "              793,\n",
       "              812,\n",
       "              819,\n",
       "              821,\n",
       "              823,\n",
       "              824,\n",
       "              843,\n",
       "              875,\n",
       "              891,\n",
       "              907,\n",
       "              938,\n",
       "              940,\n",
       "              960,\n",
       "              986,\n",
       "              989,\n",
       "              995,\n",
       "              1010,\n",
       "              1019,\n",
       "              1022,\n",
       "              1041,\n",
       "              1062,\n",
       "              1089,\n",
       "              1100,\n",
       "              1124,\n",
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       "              1312,\n",
       "              1317,\n",
       "              1325,\n",
       "              1327,\n",
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       "              1429,\n",
       "              1432,\n",
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       "              1688,\n",
       "              1732,\n",
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       "              1929,\n",
       "              1939,\n",
       "              1990,\n",
       "              1991,\n",
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       "              2086,\n",
       "              2093,\n",
       "              2096,\n",
       "              2115,\n",
       "              2125,\n",
       "              2158],\n",
       "             4: [129,\n",
       "              807,\n",
       "              882,\n",
       "              1118,\n",
       "              1151,\n",
       "              1276,\n",
       "              1538,\n",
       "              1552,\n",
       "              1637,\n",
       "              1692,\n",
       "              1778,\n",
       "              1986]})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1424,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_id[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def label(id):\n",
    "    if id in group_id[0]:\n",
    "        return 'a'\n",
    "    elif id in group_id[1]:\n",
    "        return 'b'\n",
    "    elif id in group_id[2]:\n",
    "        return 'c'\n",
    "    elif id in group_id[3]:\n",
    "        return 'd'\n",
    "    elif id in group_id[4]:\n",
    "        return 'e'\n",
    "    elif id in group_id[5]:\n",
    "        return 'f'\n",
    "    elif id in group_id[6]:\n",
    "        return 'g'\n",
    "    elif id in group_id[7]:\n",
    "        return 'h'\n",
    "    elif id in group_id[8]:\n",
    "        return 'i'\n",
    "    else:\n",
    "        return 'n'\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label(221)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nan_cols = list(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d_mean= train.median(axis=0)\n",
    "n = train.isnull().sum(axis=1)\n",
    "for c in train.columns:\n",
    "    train[c + '_nan_'] = pd.isnull(train[c])\n",
    "    d_mean[c + '_nan_'] = 0\n",
    "train = train.fillna(d_mean)\n",
    "train['znull'] = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>timestamp</th>\n",
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       "      <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",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>28</td>\n",
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       "      <td>25</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>38</td>\n",
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       "      <td>21</td>\n",
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       "<p>5 rows × 223 columns</p>\n",
       "</div>"
      ],
      "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  -0.000837   0.005523   0.021095   0.002476   0.011752   \n",
       "4  26          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2  ...    technical_37_nan_  \\\n",
       "0      -0.335633       0.113292       1.621238  ...                 True   \n",
       "1       0.004413       0.114285      -0.210185  ...                 True   \n",
       "2      -0.155937       1.219439      -0.764516  ...                 True   \n",
       "3       0.178495      -0.007395      -0.007262  ...                 True   \n",
       "4       0.139445      -0.125687      -0.018707  ...                 True   \n",
       "\n",
       "   technical_38_nan_  technical_39_nan_  technical_40_nan_  technical_41_nan_  \\\n",
       "0               True               True              False               True   \n",
       "1               True               True              False               True   \n",
       "2               True               True              False               True   \n",
       "3               True               True              False               True   \n",
       "4               True               True              False               True   \n",
       "\n",
       "   technical_42_nan_  technical_43_nan_  technical_44_nan_  y_nan_  znull  \n",
       "0               True              False               True   False     24  \n",
       "1               True              False               True   False     22  \n",
       "2               True              False               True   False     28  \n",
       "3               True              False               True   False     38  \n",
       "4               True              False               True   False     21  \n",
       "\n",
       "[5 rows x 223 columns]"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "label = train.id.map(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'timestamp', 'derived_0', 'derived_1', 'derived_2', 'derived_3',\n",
       "       'derived_4', 'fundamental_0', 'fundamental_1', 'fundamental_2',\n",
       "       ...\n",
       "       'technical_37_nan_', 'technical_38_nan_', 'technical_39_nan_',\n",
       "       'technical_40_nan_', 'technical_41_nan_', 'technical_42_nan_',\n",
       "       'technical_43_nan_', 'technical_44_nan_', 'y_nan_', 'znull'],\n",
       "      dtype='object', length=223)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "label_dummy = pd.get_dummies(label, prefix='Label')\n",
    "train.drop(['id', 'y_nan_', 'id_nan_', 'timestamp_nan_'], axis=1, inplace=True)\n",
    "train = pd.concat([train, label_dummy], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "   timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
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       "2          0  -0.010622  -0.050577   3.379575  -0.157525  -0.068550   \n",
       "3          0  -0.000837   0.005523   0.021095   0.002476   0.011752   \n",
       "4          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2  fundamental_3   ...     \\\n",
       "0      -0.335633       0.113292       1.621238      -0.179404   ...      \n",
       "1       0.004413       0.114285      -0.210185       0.216281   ...      \n",
       "2      -0.155937       1.219439      -0.764516      -0.040183   ...      \n",
       "3       0.178495      -0.007395      -0.007262      -0.097903   ...      \n",
       "4       0.139445      -0.125687      -0.018707       0.196391   ...      \n",
       "\n",
       "   technical_41_nan_  technical_42_nan_  technical_43_nan_  technical_44_nan_  \\\n",
       "0               True               True              False               True   \n",
       "1               True               True              False               True   \n",
       "2               True               True              False               True   \n",
       "3               True               True              False               True   \n",
       "4               True               True              False               True   \n",
       "\n",
       "   znull  Label_a  Label_b  Label_c  Label_d  Label_e  \n",
       "0     24        1        0        0        0        0  \n",
       "1     22        0        0        1        0        0  \n",
       "2     28        0        0        1        0        0  \n",
       "3     38        0        0        1        0        0  \n",
       "4     21        0        0        1        0        0  \n",
       "\n",
       "[5 rows x 224 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp            0\n",
       "derived_0            0\n",
       "derived_1            0\n",
       "derived_2            0\n",
       "derived_3            0\n",
       "derived_4            0\n",
       "fundamental_0        0\n",
       "fundamental_1        0\n",
       "fundamental_2        0\n",
       "fundamental_3        0\n",
       "fundamental_5        0\n",
       "fundamental_6        0\n",
       "fundamental_7        0\n",
       "fundamental_8        0\n",
       "fundamental_9        0\n",
       "fundamental_10       0\n",
       "fundamental_11       0\n",
       "fundamental_12       0\n",
       "fundamental_13       0\n",
       "fundamental_14       0\n",
       "fundamental_15       0\n",
       "fundamental_16       0\n",
       "fundamental_17       0\n",
       "fundamental_18       0\n",
       "fundamental_19       0\n",
       "fundamental_20       0\n",
       "fundamental_21       0\n",
       "fundamental_22       0\n",
       "fundamental_23       0\n",
       "fundamental_24       0\n",
       "                    ..\n",
       "technical_19_nan_    0\n",
       "technical_20_nan_    0\n",
       "technical_21_nan_    0\n",
       "technical_22_nan_    0\n",
       "technical_24_nan_    0\n",
       "technical_25_nan_    0\n",
       "technical_27_nan_    0\n",
       "technical_28_nan_    0\n",
       "technical_29_nan_    0\n",
       "technical_30_nan_    0\n",
       "technical_31_nan_    0\n",
       "technical_32_nan_    0\n",
       "technical_33_nan_    0\n",
       "technical_34_nan_    0\n",
       "technical_35_nan_    0\n",
       "technical_36_nan_    0\n",
       "technical_37_nan_    0\n",
       "technical_38_nan_    0\n",
       "technical_39_nan_    0\n",
       "technical_40_nan_    0\n",
       "technical_41_nan_    0\n",
       "technical_42_nan_    0\n",
       "technical_43_nan_    0\n",
       "technical_44_nan_    0\n",
       "znull                0\n",
       "Label__a             0\n",
       "Label__b             0\n",
       "Label__c             0\n",
       "Label__d             0\n",
       "Label__e             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = train.y\n",
    "X_train = train.drop(['y','timestamp'], axis=1)\n",
    "# X_train['period'] = X_train.timestamp % 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>fundamental_3</th>\n",
       "      <th>fundamental_5</th>\n",
       "      <th>...</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",
       "      <th>znull</th>\n",
       "      <th>Label_a</th>\n",
       "      <th>Label_b</th>\n",
       "      <th>Label_c</th>\n",
       "      <th>Label_d</th>\n",
       "      <th>Label_e</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.210185</td>\n",
       "      <td>0.216281</td>\n",
       "      <td>0.096750</td>\n",
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       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
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       "      <td>-0.764516</td>\n",
       "      <td>-0.040183</td>\n",
       "      <td>0.033375</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000837</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>0.021095</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.011752</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>-0.007395</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>-0.097903</td>\n",
       "      <td>0.033375</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.196391</td>\n",
       "      <td>0.033375</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 222 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   derived_0  derived_1  derived_2  derived_3  derived_4  fundamental_0  \\\n",
       "0   0.370326  -0.006316   0.222831  -0.213030   0.729277      -0.335633   \n",
       "1   0.014765  -0.038064  -0.017425   0.320652  -0.034134       0.004413   \n",
       "2  -0.010622  -0.050577   3.379575  -0.157525  -0.068550      -0.155937   \n",
       "3  -0.000837   0.005523   0.021095   0.002476   0.011752       0.178495   \n",
       "4   0.176693  -0.025284  -0.057680   0.015100   0.180894       0.139445   \n",
       "\n",
       "   fundamental_1  fundamental_2  fundamental_3  fundamental_5   ...     \\\n",
       "0       0.113292       1.621238      -0.179404       0.033375   ...      \n",
       "1       0.114285      -0.210185       0.216281       0.096750   ...      \n",
       "2       1.219439      -0.764516      -0.040183       0.033375   ...      \n",
       "3      -0.007395      -0.007262      -0.097903       0.033375   ...      \n",
       "4      -0.125687      -0.018707       0.196391       0.033375   ...      \n",
       "\n",
       "   technical_41_nan_  technical_42_nan_  technical_43_nan_  technical_44_nan_  \\\n",
       "0               True               True              False               True   \n",
       "1               True               True              False               True   \n",
       "2               True               True              False               True   \n",
       "3               True               True              False               True   \n",
       "4               True               True              False               True   \n",
       "\n",
       "   znull  Label_a  Label_b  Label_c  Label_d  Label_e  \n",
       "0     24        1        0        0        0        0  \n",
       "1     22        0        0        1        0        0  \n",
       "2     28        0        0        1        0        0  \n",
       "3     38        0        0        1        0        0  \n",
       "4     21        0        0        1        0        0  \n",
       "\n",
       "[5 rows x 222 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "etr =  ExtraTreesRegressor(n_estimators=60, n_jobs=-1, random_state=17, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "          max_features='auto', max_leaf_nodes=None,\n",
       "          min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "          min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "          n_estimators=60, n_jobs=-1, oob_score=False, random_state=17,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr.fit(X_train[489999:799999], y[489999:799999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imp = pd.DataFrame({'feature':X_train.columns, 'important':etr.feature_importances_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>technical_30</td>\n",
       "      <td>5.236827e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>technical_33</td>\n",
       "      <td>4.692793e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>technical_41</td>\n",
       "      <td>4.312483e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>technical_24</td>\n",
       "      <td>4.170072e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>technical_20</td>\n",
       "      <td>4.015349e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>technical_3</td>\n",
       "      <td>3.980889e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>technical_1</td>\n",
       "      <td>3.899048e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>technical_31</td>\n",
       "      <td>3.667957e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>technical_5</td>\n",
       "      <td>3.651197e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>technical_25</td>\n",
       "      <td>3.616646e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>technical_44</td>\n",
       "      <td>3.491835e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>technical_28</td>\n",
       "      <td>3.470507e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>technical_13</td>\n",
       "      <td>3.069191e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>technical_21</td>\n",
       "      <td>1.348004e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>technical_17</td>\n",
       "      <td>1.302374e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>technical_2</td>\n",
       "      <td>1.205679e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>technical_11</td>\n",
       "      <td>1.144886e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>technical_12</td>\n",
       "      <td>1.087828e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>technical_19</td>\n",
       "      <td>1.043576e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>fundamental_41</td>\n",
       "      <td>1.033024e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>fundamental_62</td>\n",
       "      <td>1.012053e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>technical_0</td>\n",
       "      <td>9.598179e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>technical_6</td>\n",
       "      <td>9.595127e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>technical_27</td>\n",
       "      <td>9.094214e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>technical_37</td>\n",
       "      <td>8.735460e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>technical_38</td>\n",
       "      <td>8.507448e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>technical_36</td>\n",
       "      <td>8.341153e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>fundamental_42</td>\n",
       "      <td>8.192210e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>technical_35</td>\n",
       "      <td>8.055088e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>technical_32</td>\n",
       "      <td>7.373654e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>fundamental_29_nan_</td>\n",
       "      <td>5.507619e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>fundamental_48_nan_</td>\n",
       "      <td>4.830714e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>fundamental_36_nan_</td>\n",
       "      <td>4.459860e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>fundamental_18_nan_</td>\n",
       "      <td>4.174143e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>technical_14_nan_</td>\n",
       "      <td>3.984598e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>technical_43_nan_</td>\n",
       "      <td>1.221915e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>technical_18_nan_</td>\n",
       "      <td>1.185152e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>technical_2_nan_</td>\n",
       "      <td>1.171837e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>technical_6_nan_</td>\n",
       "      <td>1.084509e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>technical_16_nan_</td>\n",
       "      <td>1.081928e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>technical_39_nan_</td>\n",
       "      <td>9.987821e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>technical_42_nan_</td>\n",
       "      <td>9.695531e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>technical_0_nan_</td>\n",
       "      <td>7.945453e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>technical_11_nan_</td>\n",
       "      <td>6.540501e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>technical_38_nan_</td>\n",
       "      <td>5.105070e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>technical_17_nan_</td>\n",
       "      <td>4.998847e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>technical_37_nan_</td>\n",
       "      <td>4.228036e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>technical_12_nan_</td>\n",
       "      <td>3.986665e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>technical_9_nan_</td>\n",
       "      <td>3.488788e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>technical_32_nan_</td>\n",
       "      <td>3.061838e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>technical_7_nan_</td>\n",
       "      <td>3.798368e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>technical_27_nan_</td>\n",
       "      <td>9.966971e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>technical_36_nan_</td>\n",
       "      <td>7.885980e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>technical_21_nan_</td>\n",
       "      <td>1.603277e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>technical_40_nan_</td>\n",
       "      <td>1.253695e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>technical_35_nan_</td>\n",
       "      <td>5.241626e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>technical_19_nan_</td>\n",
       "      <td>9.763483e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>technical_34_nan_</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Label_b</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>technical_22_nan_</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 feature     important\n",
       "93          technical_30  5.236827e-02\n",
       "96          technical_33  4.692793e-02\n",
       "104         technical_41  4.312483e-02\n",
       "88          technical_24  4.170072e-02\n",
       "85          technical_20  4.015349e-02\n",
       "71           technical_3  3.980889e-02\n",
       "69           technical_1  3.899048e-02\n",
       "94          technical_31  3.667957e-02\n",
       "72           technical_5  3.651197e-02\n",
       "89          technical_25  3.616646e-02\n",
       "107         technical_44  3.491835e-02\n",
       "91          technical_28  3.470507e-02\n",
       "79          technical_13  3.069191e-02\n",
       "86          technical_21  1.348004e-02\n",
       "82          technical_17  1.302374e-02\n",
       "70           technical_2  1.205679e-02\n",
       "77          technical_11  1.144886e-02\n",
       "78          technical_12  1.087828e-02\n",
       "84          technical_19  1.043576e-02\n",
       "45        fundamental_41  1.033024e-02\n",
       "66        fundamental_62  1.012053e-02\n",
       "68           technical_0  9.598179e-03\n",
       "73           technical_6  9.595127e-03\n",
       "90          technical_27  9.094214e-03\n",
       "100         technical_37  8.735460e-03\n",
       "101         technical_38  8.507448e-03\n",
       "99          technical_36  8.341153e-03\n",
       "46        fundamental_42  8.192210e-03\n",
       "98          technical_35  8.055088e-03\n",
       "95          technical_32  7.373654e-03\n",
       "..                   ...           ...\n",
       "141  fundamental_29_nan_  5.507619e-05\n",
       "160  fundamental_48_nan_  4.830714e-05\n",
       "148  fundamental_36_nan_  4.459860e-05\n",
       "130  fundamental_18_nan_  4.174143e-05\n",
       "188    technical_14_nan_  3.984598e-05\n",
       "214    technical_43_nan_  1.221915e-05\n",
       "191    technical_18_nan_  1.185152e-05\n",
       "178     technical_2_nan_  1.171837e-05\n",
       "181     technical_6_nan_  1.084509e-05\n",
       "189    technical_16_nan_  1.081928e-05\n",
       "210    technical_39_nan_  9.987821e-06\n",
       "213    technical_42_nan_  9.695531e-06\n",
       "176     technical_0_nan_  7.945453e-06\n",
       "185    technical_11_nan_  6.540501e-06\n",
       "209    technical_38_nan_  5.105070e-06\n",
       "190    technical_17_nan_  4.998847e-06\n",
       "208    technical_37_nan_  4.228036e-06\n",
       "186    technical_12_nan_  3.986665e-06\n",
       "183     technical_9_nan_  3.488788e-06\n",
       "203    technical_32_nan_  3.061838e-06\n",
       "182     technical_7_nan_  3.798368e-07\n",
       "198    technical_27_nan_  9.966971e-08\n",
       "207    technical_36_nan_  7.885980e-08\n",
       "194    technical_21_nan_  1.603277e-08\n",
       "211    technical_40_nan_  1.253695e-08\n",
       "206    technical_35_nan_  5.241626e-09\n",
       "192    technical_19_nan_  9.763483e-11\n",
       "205    technical_34_nan_  0.000000e+00\n",
       "218              Label_b  0.000000e+00\n",
       "195    technical_22_nan_  0.000000e+00\n",
       "\n",
       "[222 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.sort_values('important', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134    fundamental_22_nan_\n",
       "220                Label_d\n",
       "215      technical_44_nan_\n",
       "197      technical_25_nan_\n",
       "164    fundamental_52_nan_\n",
       "180       technical_5_nan_\n",
       "169    fundamental_57_nan_\n",
       "202      technical_31_nan_\n",
       "138    fundamental_26_nan_\n",
       "173    fundamental_61_nan_\n",
       "114     fundamental_1_nan_\n",
       "146    fundamental_34_nan_\n",
       "137    fundamental_25_nan_\n",
       "170    fundamental_58_nan_\n",
       "196      technical_24_nan_\n",
       "122    fundamental_10_nan_\n",
       "163    fundamental_51_nan_\n",
       "174    fundamental_62_nan_\n",
       "109         derived_1_nan_\n",
       "177       technical_1_nan_\n",
       "131    fundamental_19_nan_\n",
       "151    fundamental_39_nan_\n",
       "124    fundamental_12_nan_\n",
       "179       technical_3_nan_\n",
       "108         derived_0_nan_\n",
       "159    fundamental_47_nan_\n",
       "200      technical_29_nan_\n",
       "112         derived_4_nan_\n",
       "147    fundamental_35_nan_\n",
       "132    fundamental_20_nan_\n",
       "              ...         \n",
       "141    fundamental_29_nan_\n",
       "160    fundamental_48_nan_\n",
       "148    fundamental_36_nan_\n",
       "130    fundamental_18_nan_\n",
       "188      technical_14_nan_\n",
       "214      technical_43_nan_\n",
       "191      technical_18_nan_\n",
       "178       technical_2_nan_\n",
       "181       technical_6_nan_\n",
       "189      technical_16_nan_\n",
       "210      technical_39_nan_\n",
       "213      technical_42_nan_\n",
       "176       technical_0_nan_\n",
       "185      technical_11_nan_\n",
       "209      technical_38_nan_\n",
       "190      technical_17_nan_\n",
       "208      technical_37_nan_\n",
       "186      technical_12_nan_\n",
       "183       technical_9_nan_\n",
       "203      technical_32_nan_\n",
       "182       technical_7_nan_\n",
       "198      technical_27_nan_\n",
       "207      technical_36_nan_\n",
       "194      technical_21_nan_\n",
       "211      technical_40_nan_\n",
       "206      technical_35_nan_\n",
       "192      technical_19_nan_\n",
       "195      technical_22_nan_\n",
       "218                Label_b\n",
       "205      technical_34_nan_\n",
       "Name: feature, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.loc[imp.important < imp.mean().important / 10].sort_values('important', ascending=False).feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>important</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>derived_0</td>\n",
       "      <td>4.139880e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>derived_1</td>\n",
       "      <td>4.365267e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>derived_2</td>\n",
       "      <td>4.093007e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>derived_3</td>\n",
       "      <td>5.815219e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>derived_4</td>\n",
       "      <td>3.725371e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fundamental_0</td>\n",
       "      <td>5.101779e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fundamental_1</td>\n",
       "      <td>1.975685e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fundamental_2</td>\n",
       "      <td>3.893213e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fundamental_3</td>\n",
       "      <td>2.602457e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fundamental_5</td>\n",
       "      <td>2.370876e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fundamental_6</td>\n",
       "      <td>2.257239e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fundamental_7</td>\n",
       "      <td>4.645183e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fundamental_8</td>\n",
       "      <td>3.243798e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fundamental_9</td>\n",
       "      <td>2.636833e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fundamental_10</td>\n",
       "      <td>6.374257e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fundamental_11</td>\n",
       "      <td>4.061992e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fundamental_12</td>\n",
       "      <td>3.296572e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fundamental_13</td>\n",
       "      <td>2.819670e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>fundamental_14</td>\n",
       "      <td>3.201378e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fundamental_15</td>\n",
       "      <td>3.926173e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>fundamental_16</td>\n",
       "      <td>2.755622e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fundamental_17</td>\n",
       "      <td>4.005967e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fundamental_18</td>\n",
       "      <td>4.726348e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fundamental_19</td>\n",
       "      <td>3.670883e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>fundamental_20</td>\n",
       "      <td>3.475807e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fundamental_21</td>\n",
       "      <td>4.930999e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>fundamental_22</td>\n",
       "      <td>2.886690e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fundamental_23</td>\n",
       "      <td>3.688235e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fundamental_24</td>\n",
       "      <td>2.640344e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>fundamental_25</td>\n",
       "      <td>3.118165e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>technical_19_nan_</td>\n",
       "      <td>9.763483e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>technical_20_nan_</td>\n",
       "      <td>8.732996e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>technical_21_nan_</td>\n",
       "      <td>1.603277e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>technical_22_nan_</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>technical_24_nan_</td>\n",
       "      <td>2.900155e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>technical_25_nan_</td>\n",
       "      <td>3.456184e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>technical_27_nan_</td>\n",
       "      <td>9.966971e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>technical_28_nan_</td>\n",
       "      <td>4.910064e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>technical_29_nan_</td>\n",
       "      <td>2.457151e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>technical_30_nan_</td>\n",
       "      <td>8.364412e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>technical_31_nan_</td>\n",
       "      <td>3.238888e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>technical_32_nan_</td>\n",
       "      <td>3.061838e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>technical_33_nan_</td>\n",
       "      <td>2.123349e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>technical_34_nan_</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>technical_35_nan_</td>\n",
       "      <td>5.241626e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>technical_36_nan_</td>\n",
       "      <td>7.885980e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>technical_37_nan_</td>\n",
       "      <td>4.228036e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>technical_38_nan_</td>\n",
       "      <td>5.105070e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>technical_39_nan_</td>\n",
       "      <td>9.987821e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>technical_40_nan_</td>\n",
       "      <td>1.253695e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>technical_41_nan_</td>\n",
       "      <td>1.753865e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>technical_42_nan_</td>\n",
       "      <td>9.695531e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>technical_43_nan_</td>\n",
       "      <td>1.221915e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>technical_44_nan_</td>\n",
       "      <td>3.777373e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>znull</td>\n",
       "      <td>1.307710e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Label_a</td>\n",
       "      <td>8.543135e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Label_b</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>Label_c</td>\n",
       "      <td>7.080019e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Label_d</td>\n",
       "      <td>4.293023e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Label_e</td>\n",
       "      <td>1.006373e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature     important\n",
       "0            derived_0  4.139880e-03\n",
       "1            derived_1  4.365267e-03\n",
       "2            derived_2  4.093007e-03\n",
       "3            derived_3  5.815219e-03\n",
       "4            derived_4  3.725371e-03\n",
       "5        fundamental_0  5.101779e-03\n",
       "6        fundamental_1  1.975685e-03\n",
       "7        fundamental_2  3.893213e-03\n",
       "8        fundamental_3  2.602457e-03\n",
       "9        fundamental_5  2.370876e-03\n",
       "10       fundamental_6  2.257239e-03\n",
       "11       fundamental_7  4.645183e-03\n",
       "12       fundamental_8  3.243798e-03\n",
       "13       fundamental_9  2.636833e-03\n",
       "14      fundamental_10  6.374257e-03\n",
       "15      fundamental_11  4.061992e-03\n",
       "16      fundamental_12  3.296572e-03\n",
       "17      fundamental_13  2.819670e-03\n",
       "18      fundamental_14  3.201378e-03\n",
       "19      fundamental_15  3.926173e-03\n",
       "20      fundamental_16  2.755622e-03\n",
       "21      fundamental_17  4.005967e-03\n",
       "22      fundamental_18  4.726348e-03\n",
       "23      fundamental_19  3.670883e-03\n",
       "24      fundamental_20  3.475807e-03\n",
       "25      fundamental_21  4.930999e-03\n",
       "26      fundamental_22  2.886690e-03\n",
       "27      fundamental_23  3.688235e-03\n",
       "28      fundamental_24  2.640344e-03\n",
       "29      fundamental_25  3.118165e-03\n",
       "..                 ...           ...\n",
       "192  technical_19_nan_  9.763483e-11\n",
       "193  technical_20_nan_  8.732996e-05\n",
       "194  technical_21_nan_  1.603277e-08\n",
       "195  technical_22_nan_  0.000000e+00\n",
       "196  technical_24_nan_  2.900155e-04\n",
       "197  technical_25_nan_  3.456184e-04\n",
       "198  technical_27_nan_  9.966971e-08\n",
       "199  technical_28_nan_  4.910064e-04\n",
       "200  technical_29_nan_  2.457151e-04\n",
       "201  technical_30_nan_  8.364412e-05\n",
       "202  technical_31_nan_  3.238888e-04\n",
       "203  technical_32_nan_  3.061838e-06\n",
       "204  technical_33_nan_  2.123349e-04\n",
       "205  technical_34_nan_  0.000000e+00\n",
       "206  technical_35_nan_  5.241626e-09\n",
       "207  technical_36_nan_  7.885980e-08\n",
       "208  technical_37_nan_  4.228036e-06\n",
       "209  technical_38_nan_  5.105070e-06\n",
       "210  technical_39_nan_  9.987821e-06\n",
       "211  technical_40_nan_  1.253695e-08\n",
       "212  technical_41_nan_  1.753865e-04\n",
       "213  technical_42_nan_  9.695531e-06\n",
       "214  technical_43_nan_  1.221915e-05\n",
       "215  technical_44_nan_  3.777373e-04\n",
       "216              znull  1.307710e-03\n",
       "217            Label_a  8.543135e-04\n",
       "218            Label_b  0.000000e+00\n",
       "219            Label_c  7.080019e-04\n",
       "220            Label_d  4.293023e-04\n",
       "221            Label_e  1.006373e-04\n",
       "\n",
       "[222 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp"
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[93, 1431]"
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
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       " 1676,\n",
       " 1677,\n",
       " 1679,\n",
       " 1680,\n",
       " 1685,\n",
       " 1687,\n",
       " 1690,\n",
       " 1695,\n",
       " 1696,\n",
       " 1697,\n",
       " 1699,\n",
       " 1704,\n",
       " 1705,\n",
       " 1706,\n",
       " 1711,\n",
       " 1712,\n",
       " 1714,\n",
       " 1716,\n",
       " 1717,\n",
       " 1718,\n",
       " 1724,\n",
       " 1725,\n",
       " 1729,\n",
       " 1730,\n",
       " 1731,\n",
       " 1733,\n",
       " 1734,\n",
       " 1738,\n",
       " 1739,\n",
       " 1742,\n",
       " 1743,\n",
       " 1744,\n",
       " 1745,\n",
       " 1750,\n",
       " 1752,\n",
       " 1758,\n",
       " 1759,\n",
       " 1764,\n",
       " 1766,\n",
       " 1769,\n",
       " 1782,\n",
       " 1783,\n",
       " 1784,\n",
       " 1788,\n",
       " 1789,\n",
       " 1790,\n",
       " 1792,\n",
       " 1794,\n",
       " 1795,\n",
       " 1796,\n",
       " 1802,\n",
       " 1803,\n",
       " 1806,\n",
       " 1810,\n",
       " 1812,\n",
       " 1814,\n",
       " 1815,\n",
       " 1817,\n",
       " 1827,\n",
       " 1830,\n",
       " 1831,\n",
       " 1832,\n",
       " 1838,\n",
       " 1839,\n",
       " 1845,\n",
       " 1849,\n",
       " 1851,\n",
       " 1853,\n",
       " 1854,\n",
       " 1858,\n",
       " 1861,\n",
       " 1862,\n",
       " 1863,\n",
       " 1864,\n",
       " 1866,\n",
       " 1869,\n",
       " 1870,\n",
       " 1873,\n",
       " 1878,\n",
       " 1883,\n",
       " 1884,\n",
       " 1887,\n",
       " 1890,\n",
       " 1891,\n",
       " 1893,\n",
       " 1896,\n",
       " 1897,\n",
       " 1898,\n",
       " 1899,\n",
       " 1901,\n",
       " 1902,\n",
       " 1905,\n",
       " 1906,\n",
       " 1907,\n",
       " 1910,\n",
       " 1911,\n",
       " 1912,\n",
       " 1915,\n",
       " 1918,\n",
       " 1921,\n",
       " 1925,\n",
       " 1926,\n",
       " 1927,\n",
       " 1928,\n",
       " 1931,\n",
       " 1933,\n",
       " 1935,\n",
       " 1941,\n",
       " 1945,\n",
       " 1949,\n",
       " 1951,\n",
       " 1952,\n",
       " 1954,\n",
       " 1956,\n",
       " 1962,\n",
       " 1963,\n",
       " 1965,\n",
       " 1968,\n",
       " 1969,\n",
       " 1970,\n",
       " 1971,\n",
       " 1977,\n",
       " 1979,\n",
       " 1985,\n",
       " 1987,\n",
       " 1992,\n",
       " 2001,\n",
       " 2004,\n",
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       " 2013,\n",
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       " 2056,\n",
       " 2057,\n",
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       " 2063,\n",
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       " 2066,\n",
       " 2068,\n",
       " 2071,\n",
       " 2074,\n",
       " 2075,\n",
       " 2078,\n",
       " 2079,\n",
       " 2080,\n",
       " 2083,\n",
       " 2087,\n",
       " 2088,\n",
       " 2089,\n",
       " 2090,\n",
       " 2095,\n",
       " 2098,\n",
       " 2100,\n",
       " 2101,\n",
       " 2102,\n",
       " 2104,\n",
       " 2107,\n",
       " 2108,\n",
       " 2111,\n",
       " 2112,\n",
       " 2114,\n",
       " 2121,\n",
       " 2124,\n",
       " 2126,\n",
       " 2129,\n",
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       " 2136,\n",
       " 2137,\n",
       " 2138,\n",
       " 2142,\n",
       " 2145,\n",
       " 2148,\n",
       " 2149,\n",
       " 2150,\n",
       " 2151,\n",
       " 2152,\n",
       " 2154,\n",
       " 2156]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_id[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "536"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group_id[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group_id[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group_id[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train.drop('period', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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