mxposed/zzz.ipynb

## zzz.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "В этом домашнем задании на примере [задачки с kaggle](https://www.kaggle.com/c/digit-recognizer), мы потренируемся предобрабатывать данные, применять и анализировать простейшие методы классификации. Общий ход решения будет указан, от вас потребуется заполнить недостающие части.\n",
    "\n",
    "Ниже приведен довольно простой код с расчетом на то, что им будет пользоваться человек малознакомый с Python (и с программированием в целом). Вы можете использовать в решении более сложные конструкции (например, создавать свои классы, использовать итераторы, декораторы и прочее) и переделывать код в соответствии с этим. Главное, чтобы он оставался удобно читаемым.\n",
    "\n",
    "Не забываем импортировать нужные модули:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "# модуль для работы с матрицами\n",
    "import numpy as np\n",
    "\n",
    "# для чтения данных\n",
    "import pandas as pd\n",
    "\n",
    "# инструменты для работы с картинками\n",
    "from scipy.ndimage.interpolation import shift, rotate, zoom\n",
    "from scipy import special\n",
    "\n",
    "# графика\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# импортируем классификаторы\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# модуль для измерения точности классификации\n",
    "from sklearn import metrics\n",
    "\n",
    "# модуль для работы с данными для модели\n",
    "from sklearn import model_selection\n",
    "\n",
    "# библиотека для измерения времени\n",
    "import timeit\n",
    "\n",
    "# установка опций выводы numpy объектов\n",
    "np.set_printoptions(precision=3, threshold=100, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скачайте обучающую и тестовую выборки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте прочитаем данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "digits = []\n",
    "labels = []\n",
    "for digit in data.as_matrix():\n",
    "    digits.append(digit[1:])\n",
    "    labels.append(digit[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь у нас есть два массива: `digits`, где хранятся матрицы в виде векторов с записанными данными о каждом пикселе, и `labels`, где хранятся данные о том, какая цифра изображена на картинке."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь сделаем функцию, которая позволяет нам вывести некоторое множество картинок, и посмотрим на данные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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L7JiGHKZkzu/3i/QvmUyiVCrJjL9UKiUugsFgcK97UUwJGyWmjUYD9XpdPh998f+R2PL7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\nkjdco9vtFpPJBO12Gzc3N8hmsyJjfU7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tXbEFcGdtpVIpJBIJUQD8iK/IIcBM4Ji9rMRj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puh3++j0Wjg8vJSKnIktlQbPCZhynmk/DO9LTqdDi4uLsSUi/B4PA4SxGTxXdA+F5zScB8YIAM4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q6urvDrr7/i6uoKjUbDUbHVILGNxWLIZDIoFotrUmR7n18W+l2ZTCZot9uoVquSuGCfICu2\nOpHo9/uRyWRwfHyMP/zhD/jjH/8o7xsDGlZvec1mszViu6liS3nyJnJr9967oe8VA9Tz83P88ssv\nuLi4kGQ0R8/o+/s9oIlts9mE3+9HMplEPp/fiootyYCu2JIM6LVgxZbENpPJrO17gUBA7hf71y12\nA4xb5vO5xKNMIGsi5fV6EYvFkM/n8e7dO3z8+BGZTAapVArpdBqpVEoMm/RITODuvm3GXoyldDXw\n/Pwcv/32G7rdrhBs7e6riS1/Pyu22widhNBeEVdXV/jtt99QqVQcikH6ARWLRfzwww/48OGDo8gX\nj8cd8SaJrc/nQywWk57rSqUifbdsMWk2mwgGg3IWc67tNuBZnh72wySTSSyXS+mXSyQSSKVSGyu2\ndGijfDidTsuVSqUe/Rm006bW73/J5lr3DbAqy94s0/yK1VdzHhirxLpCbIPx+3FfDy2rs6ZrNR05\nB4OBVGkpG+fFf+50Oo6qPOWv+jnR8vVwOCwOrZQeF4tFscNnsG3xO8w15Cbb7/fRbrfR6XTEFX3T\njGrTBTmfzyOVSjlckG2l9uWwSX5GJQv7L+kmvlwu5fBkb1UqlcKHDx9QKpWQz+eRSCQkoDGz9abM\nWM8XN6uv3OvNZKa59+/6Hmz2ezEpyMuUHjcaDWnn4Bi7TdDrtmmknnmu6j9vIqqsbNJoTqujdKLC\nfFZeM/ju6BE/Wt1l3ls935lnlLn3MXDlLHU9uUGfm/pixZhfzfuvfx/fnU2kR8P8PZRc88+bfgd9\nFLjf53I5aeexuBvmvebzw2Q9x7vwoi8IzdiKxaIQpHg8Lj2WX6qWsoCgW76ogGN73snJCS4vL3F9\nfS0OyD6fTwg0e0SLxaLMo6eh67aM8NoE3YLAJAS9ezjucLVaSaskTUl14YTqJX6PCRZh6D+h3aZ5\nxnq9XjFPbTabUijw+XzSl0vOQtfrtzTr9lmILSuwtPZm1pEExJTMcKPjxT47LY147I015yNS6vCY\nwJgHAQNzViEYmDOrSiJLyVAkEkEoFHLY7tvA6mHQGS5tBDWbzaRyoPtitaxYG5+Y/07PSwZu5cam\nU7Y5M0xLYrPZLNLptFjgf42p2S6BB+J4PBaZHR3DNwW02gVZ99fyXtv352XBcSNMJuk5ptfX1zLW\nbTabSSY5l8uJbH9/fx/Hx8c4ODiQA5fKGZPIkvTohCKDZU1w9bgg7sV6LAVJliW3v0PvrzRp4l5Z\nrVZxc3ODZrMp8+Y1odwEHQBxP6V8OBKJSEWRREpfXGfz85EEct9gmwiTyto87K2sJ2OJwWDgqIBv\nUq48FOFwGMlk0jGCRQfRTBDoS7fqbNqHtVkiYxj9Pm0KrDVZ1gacbDcx19jtdiMajUqvJ41vUqnU\nxl5ii1voPZGmQHSFZ0xKQsuJIKlUSkZnkVBSPfkl8qL7eFl9HQ6HMntVj0mkuRzPAP5uViAPDg5Q\nKpWkMEDfBBodbSvMgoyOW3u9HqbTqRh68bzc399HoVBwkP+HzPfVkyS00oO9+sDvKrpOpyOcij3a\nphzdTGq9djwLsaWEhptkPB53HGjmRqcdaJld0D23X5PFMw9cc+7WQ0BjKJoOsUfTJLas8iUSCSG2\n4XBYyv6bsq0Wd4NZZ2YHeUhqWTENaiiVY/+XeZDrazabSdWea8KZiTRTyGQyMqg8n89LdpMvPd21\nabBgcTe4huPx2CHPv8vBU1dsOTSeMmRbGX95MMDh+8aghlej0RDiSVl5LpfDu3fv8OOPP+KHH34Q\nA5NUKuWoFBBmpUmTWl0J0lVbvs8cI6GdtUlu30LW+bnAe0dfgkajIbJYk9gyuXgfseUVCoVEmZVO\np2Veq75Go5EkLjeN/tPE1hyhp83I+PvfAkhsWYUmsd0UDz0U4XBYWrZyuRyCwaCjGksirY0Uq9Uq\nPB6PJIpNaAM/BrnayHOT0Yz++SQ+vV5Pkhrm38/j8YjjfaFQwN7eHvb396Xf8q2s6UtAn6lMMrJS\ny/vMmJQJYr6LrNqa0wXuIrXapJOjqVhp5CghGkX1ej0pOIxGIyFr9CSh6m1vbw+ZTAbxeFyqg2+B\nOH0ttFJDvxu8X/P53DGze39/H4eHh1LV5n1iRXYTdJJvtVoJ/6IxmFZacO91uVzSe9tqtaR4s1wu\n4fP55CsLhK8dz0ZsSWpjsdhGMw8TJvG8Tzb8UHxLZldLKUlsWfnj4apn5WpiS1nyU/wddgX62aCE\ng1n7wWCAer0uG+nl5aVDcky79Lt67Pjv9Wbq8/mERDHg3tvbE9nMwcEBksmkQ01gmt1Y3A0zQGXi\n4S5iy2xjJBJZI7a2Ov7y0MS20Wg4qrUktlrCyMD73bt3+Kd/+if84Q9/cFSAdGLI7JfVsmcqNkiE\n9PsMQBKiJLYkt1oWq3/HrsI0iyKx1WN9TGKrv38TdG8015w9sYVCAfP5XIgc905tpLTpM1LpMRqN\n4PV6RarLfYOf5S0EXATvN1tmNLH9loptsVjEjz/+iA8fPiCRSIgklYE07z2rtCS1vV4Pbrd7I+lk\nPENSpMe0RKPRtc+hjUH5O5g42dT7S2LL+anHx8dCbMPh8M6/p18C4yLdimWOKSSxjcVia8Q2nU4L\nYXlIDMN31Zy5fHp6ipOTE5ycnOD09NQhQZ/P58hms0JsP3z4gIODA3H1zWQyiEajd5LqbYImtlTG\n6ItxaDKZFHMv3isS24d4uGieY1Zs+S4yFmOScDAYoN1uo9FoSOzM9iGS37eyxz4LsX2rQb/ZQ8aN\nmr0EZo+gliJT185KLSXID91AdhE6wcGDn0Esh44zC9jpdMQinRfXhNd8Pl/rCWJl1uyD5lfKc0hs\nmUHe29sTa/X7eowsfoeZSOAmqi9dcQOc/XmsFoTDYTmUo9Hog4fEWzwtdKKJjo7dblcSTOVy2THa\nZzabOfZCc1RBLpdzeB0ww6wNqRgA8OL4ID1c3nx+GMgxANdSO/vO3sLslePsyWq1iouLC5TLZdTr\ndXQ6HYxGo40tQ9oMTLfhMFmhR1Tk8/m1FpJWq4V6vQ4ADid6QkuRuedyZBurHKwm6H6w1w4ma3jv\nSdTv6jMGbg0weV6ZfeM8p1gVSyQSDhnwaDRyzCyNx+OOgJXmbBr0RqF8lUom/oxIJLL2OVutlkMu\nqQ3ANr17fGc5T1Mrod5KIP2SML1dTNLD/8b3QycTg8Hg2j02iwEsKGj5LBOYNzc3qNVquLq6QqVS\nkYQmE//0HeEECc5g1RJkxsfbhk3vsU4e0pOCPIIyZPZB5/N5FItFqWjf1VNrwtz/2AqaTCaRy+Ww\nXC7R7XZFAUk1x3A4lIQU93Y+I5xMwGLDa99jrXbyC5jNZhgOh9KTyRe50WjIzFM+kMyOeTwemQVl\nktpdyEh9LcxNgI3tWqah5wc3m02RzPHPNN/QRlCmhF33Bum+D15ahqwldDSysL15DwcDZyYoWKWl\nVIrZXG3+ohM/ZpKIh6ANeF4GJukcDocyg+/z58+4urrC9fW1jHSgMyNHE+zv70vvHFtTzD3RNENp\nt9uOIOri4gKnp6e4ubnBaDRaq9hq6SSlduyD38bg6VvA840X506en5/j7OwM1WoV9XpdJMiETjzp\nvZOEJBqNyldNitLptMxQ5KVH5vV6PQyHwzUVF92D+Xu12Uq9XsdsNhNp7Da3+bBXkveVf2dKgtm/\nnkgkHLJBqlt4/2iKSOUL52QeHR2tJS90UMt11WaKui2MiSmqL/j7SIa2vX/yJaBVggAcpkJfGs/z\nJZgzlqlQvMuEs9ls4ubmRsb2Abf93vQmOTo6wrv/nSKh+0V36SxnMrjdbuP6+hrlchmVSgXNZlNm\nvLPYEo/HxceFpPZrW9049zuVSomTPae5MOFENYzX68VgMFjzo0ilUlgul0KSXzsssf0CdHmeo344\nj4vVQd0nrMdYBIPBjcQWgCW3D4B2bGMPrWlQwMw9+3mYVWSiQWeBeSjrg5nZaD0fmf9dfy//rA8L\nu4b3Q8tuKOHXxFa7mur+SBIeXZ1g4Kw3XIvnhSnpJ7GtVCo4PT1FpVIR3wEOh2fQTFkVZxWGw+GN\niT7TJI7z987OznB2doZyuYxarYZ6vS6jI7Skln3ZJLasDHyt6eA2Q0thO50OqtUqyuUyLi4ucHZ2\nhnq9LmZSJDx67/N6vSIhZfJCz8Skg7m+TDLNkRQ0oDGfBV2xZUBGxQ7PBn4fg8JthDYB4kQJuvHz\nfutzTBNbEkpWZdl7y1apbDaLUqkkfbAa/P5NiWHuxSb4u7l2g8FADP8ssX1a8JwEfl8rk9gCj483\ntZ/JZDKREX3soWXyUpt16n5aAAiFQtLKxSotr3w+L0nqTRXjbQTjG5o11Wo1nJ+fC7HlWcb3jbJh\nyrS/hdjqii3P1X6/j5ubG5mhreXNVMRpYst4OhwOv4kRazY6/AJ4CLNqoOefNptNdLtdR8Xgvoqt\nJUMPB4kth9ZT6sgsV6VSkQBYm8joMSCs3jDDrW3tWZHl5sFLS3U0iTJNv+w63g9ztp1JbM3RD7pa\nq03jdMVWy1Ytnhe635UGIq1WC5VKBScnJ7i+vnZIzNnXlcvlUCqV8P79exQKBXE7NR2QgduKLeVR\n7XYblUoFnz9/xi+//IJKpSIyVAYD/Gz8WXTSZiuBrtja5+YWOnFbr9elYsuqeLvdduyxhEls6WJ7\ncHAgxkU03OP5xz2VbSW8gsEger0earXaWuJBm9XohEev15OK7c3NjRC2UCi01TOKtX8HZYUcA5LL\n5aQCS9LJNeJ+ulqtEAwGHckpPWVg0wxL3S+tk45f8pVg3zQVO91uV8iWff+eFpSGer1eLJdL8XN5\nioot5al69vyvv/6KcrnsGCnEnl79DIXDYemZ/vDhg7RysSWBic1daA0x23c6nQ6ur69lznur1ZJK\nqq7YMmGop6l8DXTF1u12yyg3/rnX6zmSw5tG42lybIntG4Ips1sul45Dv1wuizRLm0bpQJyVJUpY\nqY232crHQw+a73Q6Yk7DGZnlchnA+ixGE2b/CeWtTDxomVUsFlube6k3Xj4XD2nat1gnKeaYFjMI\nZSDGjVw7irP3xOLlwICHAQxHlbBPstlsrh2QPFDZX5VKpaTyzp9pVoG1EoOVgouLC5yfn0uWWY+I\n0XC73VKxTaVSQqQtsV2HdsGs1Wqo1WqO5G2/33d8P5O2JKmxWExcO0ulEkqlksjnstms9FCbLQZa\nIqsrTOb5qHuumcDQyTI9t5zJlLcQdD0UmjzSRI+Oqby/mtxu2h95358Tg8HAQa50ItK+f08Lri2T\nhDTJ06SW+7Z5DptzwLkH6xFU3W5X9t+zszOcnJxIUUEXFJhQYay7t7eHw8NDkSDncjkxq4rH4zvT\nFqJJLUeqsceWpHY8HsPlckkiWPfB61E7X/sea+Net9uNfr8vag32QPOZmEwmcLlcjkkuHF2ayWQ2\nnrmvEZbYKphzUhuNhhhpUGpXr9dl9i6DKF7JZBLFYtFx0BcKBSQSCRuUPwLmi6OrMVpSpRMRm142\n7WLNf2bPJ8cG6ZEL7XbbIbPaZLRgzvOyFdynA7OVTDTk83mkUinrgvxKoEnFaDSSqql2ttaBNJUr\n2o2TpjBut1uCLR1wsY+effPs+WTrgXbDNav9AGSsUDQalZnT/L2W2DrBbL0e7UOjqE2VT5/P55Ad\n53I5hxlYoVCQihEAqajTJMl0Sp3P52KeMhwOhZjqa1dBMsuzh47iNOHRY1I4beG1gGZD/X4fnU5H\nJKpfGhNl8X3AObfD4VD2ZnrD6H1bJwtpIsfWu3K5jMvLS9RqNWlLoDKGlUTtTRKPxx0xsDmmb9f2\nYJ04oLs7343pdCouyMlkEvv7+9JXayaGvva+adM5AEgkEjIft9/vw+v1OkxX+VzwHWZ7Ec0a3wIs\nsVXgYtJBsNFoiOMbDUs49oCN3rqHM5vNyoyuo6MjlEol6Xn5mtm7uwizAqszXjrzT+mNy+W600mS\ngbjL5XK4T9LmXM8So7RNE9lAILBW0WX/wabRJLu2YT819JzrdDqNQqFgx/u8IlCepl1pOXqF76Cu\nzDGDr4mtlkky8aRna1KNUalUxGW5Xq9LQpGVhvuctDm2i/1cdvbxOvTcWk1sOerBhN/vRyaTwfHx\nMd69e4fDw0MxhmJvLdee5mJMhHC/1UmJ5XKJRqMhZJqmf7tMaAn2itPfgS6pdErd29sTNdhrJLbc\nJ9rttmOfsMT2eUHSyv2Wjrg6ITmdTh1977VaTebSXl5e4vr6WpKN2hhQV2k5PYKJF63cyGazorra\nRQMxnagjx2BVfLVayTseiUSwv7+PTCbjMNb61ikqug/b4/EIse33+5hOp/B6vahWqwDgkJYzee33\n+x3E9i3sz5bY/i/0LD268bJie3l5KT1HrPQtFgvJqDJwy+VyDmJ7dHQkDfK2Yvs43CUx1hVbytPu\nOiz1QHi+sOwB1KS20+nIxqLXi7brqVRKspR6hqO54Vhi+21wu90OR0BbsX1d4HtEhQN7prUMVM8x\n5dgd7Y6rzYfMyk6n08Hl5aXMQjw9PZVqHt9X/h6zYgvcKjo0sc3lcgiFQrbHbwNoHEIDMI5Rotu0\nCb/fj3Q6jaOjI/zzP/8zPnz4sGbKp0d5MTCisQw9KTTq9boE2nfNs95FaPlxMpkU9YGu2NJQ5rW1\nOengnRVbTiuw6/u8YMWWSX72POsxlbrlq9vt4urqCicnJ/j06RM+f/4sc015rVYrxzifZDKJg4MD\n/PDDD3j//j3ev3/vcEbn+U212y7twWaLIz0GeL+DwaAUv/L5vFRsNbH9VrNSElueyYlEAtlsFpPJ\nREz3gN9JbavVAuBUXXg8HiG2m/rwXyN2mtia5IkZbFqYc0g9qweDwcDxoJrW3Dx0ePAUi8UvmixY\n3A09W0+7TNNEiJu0vu6SMHNDWa1Wsg6UJTNQZ/CrSW0gEMBwOJSKFLOf/G/aGIyfddNGtEsb+beA\nPXwkQhwJwCr512BTdtFUAph/1sqAh6zhrqwvpaR8Z1iF0VU4XbXVhjP8s5akUvamx0ewl+vz58/4\n7bffZPzLJiIL3MjHZtMAACAASURBVPZl893jHEw6xKbTacec1V1Zq4eAWflut4tGoyGSs019VJQe\nJhIJ7O3t4YcffsA//MM/rBntsQrBIJg/m9JytvDw3Wq322i320J87oKWmmtzuU1eCG8BOkG7yf19\nsVhITyLl3zTeyeVyyGazr8IZXu+d/KqrPe12WxIX+rnSe6xp0KjX076vj4NWtJF86h5bPUea7x5j\nXk7/YPvHyckJfvvtN3S7XYcSh9J4yo+z2SwODw/x/v17fPz4ER8/fnTsubu87+reZe03QoUS71Ey\nmRTOoM0On8Ix2uQfs9kMyWRSEhs05Lu5uYHP55PPrFWO9Ea56xx+bXj5nfGFYC7OcrkUd0bdLF+p\nVNDpdNZK8HzBE4kECoWCVGj39vbE9VMf4Lv6Yj8WrIrSuj6bzWK5XMLv9yOZTCKfz8u4JS1x2ySf\n0w7Ji8VCguBNBlE8SEleeUCz6tvtdnF9fe1w+dw0C1dnJ+1YmsfDHCb/FFles29Pz0klydLu2np9\n2Q+qie6uvss6aNIu4bwfOuk3n8/FNfnq6gqpVEoqeVROjMdjx9iIXq+HSqXimIV7nzSVI4Xi8biM\nLjk6OkIulxMzOLMf3uIWX+ppNU2f9J55FwHRztl8BugCWi6XMZvNHIm/fr+/cVauhk6Q8FygiVIm\nk3H0772VNaajdCqVkoSrz+dDPB5HPp/HYDCQ55oEguZc4XD4Ve1B3Ef1fEyayjFhwgohCwJMhDBZ\nkslkkE6nRdJO9ZSeh2txP5jcS6fT2Nvbk6SCVrywKsspA1Su8apWq9JPS2MoGm2ytYRzqWlkViqV\nUCwWZdTUW0w2fQ+QOPLiRAi20tBtmMUxypC/9xgkc983/Spo3snkGg0Y38rIvJ2PurmgJLbValVG\nS7C/q9vtOogtF5ZuYYVCAcfHxzg+PkY2m0UymZS+l10OhL8WLpdLHDO5sUajUeRyOZG1cX6avkxi\na5qBmTI4Xe3RvWEkOS6XSyoamqhqYhwKhSSDTuc/Vn758y0eBtMcjPf7WzP3mnCZBw37rTXZ0kkK\nAHJIA7culLv4Tuv1+ZJrON+z8XiMZrOJcrmMUCgkEjh9MTHF5BSrtyaxvYvcktjSvGhvbw9HR0fI\n5/NCbPmZd3HNHoJNQQ7vt+kob5Jb84wziS3H5V1fX+Py8hKTyQTALWlmVbff7985bkaTaio6NLHl\n/PG3JDUnWeB8SJqs5fN5HB8fYzwei4qIDv4cSReJRF7V39N0SyexbbfbQmxHo5FUiHTVjySMF010\notGoEFuLh8Pj8SAcDiOVSmFvb0/Mf1qtllRtSWxdLpckHHSPLf1Ger2etNzR94LPoNlDy39PYvsU\npkdvHUyi6xFnVBpqYhsKhaTyrQ3hvvd837v2fPb3s53nLU4WsLsGbg9jVmw/f/6M//mf/0G/31/r\n7dLSRL7wPIzYW8ARBtbm/uvBjDYz9JlMRgJgXu12W3rz2u32GnGlbFL3fZnSCrNia1bz9NxO/nz9\n/0SjUZRKJZlDRlMrfp9+ZizuByuCZkX9a++hOcqAh7smsnq8zHA4lEHmANYk0Dyod3FdzcTDpuCF\nhznlqK1WC+VyGavVCtfX1+h2u/LOMmHIy5xxyp/zpYqtx+ORPfjdu3c4Pj6Weaoktt/ao7St2CTH\n19DtIJQ2mjMO76rYct/kPk1iOxqNHD+fvhbs094EPaaNZo3xeBzJZNIRCL7Fii1HYlGNxGefZ5NO\npmqS+1qeZR286/200+lIxZaKN0oZuZYcjUhSRBOyRCIhxNZ6KzwOerZ0sVjEaDSC1+vFfD6XVrpO\npyOktlarydrpaRHa7I2jYvL5PA4ODrC3t4d8Pi/SeKoItEGU3XN/BxWAbN9hn6qOJcPhsEO18NIV\nW/b3awPGtzZZYGeI7aaDW7szsvegVqvh5OQEf/nLX9aIkpYkcqxEMplEoVBAqVTC8fHxWt+PxcNg\nviwktNFoFAAcw+RJTti7xf4tc72m06mQYJ2R5DWZTKQPhWumB9fz/zelPHrTjsViMh6DWXhKyzju\nxPw7voWN4SWg3y0zaCaZvA9m37yZmDDX1JzZ1+v1RIpOh13+HAbOev12aS03VdM3yZGBW6Opdrst\nlblAICBtBM1mE+122/H/3EWuvgSfzycV2+PjY/z0009Ip9PIZrOIRqM2ML4Hd5n0AbdJHE1udULD\nfBfMBBLHjHQ6Hdzc3ODq6grD4fBRn08bkZHUUqLLim04HJb94q28j0zchcPhl/4o3wxdleIoEz1p\nYDAYOL7fNHhjpYqSx0QigVAohEAgYKXIjwR9Xzg7nAmjwWCAZrMpBZzRaISbmxsx4TST9zrG0XEu\nW+60j0w2m137fyxuDWm1YZSeIgBAKracE5tOp8Xj5an2s03eM2ZsxH2bCXu+n5xOYSu2bwR82Vmp\nabfbODs7w83NzdpGTDBzxYsjfbLZrFQXrenB94GuFq1WKwkKSDR9Pt/Giq2u1uoqHTNnZnVQ91vq\nLDSlkuxH0eNOGo0GgsEgVqsV+v2+Q1pFaTLlyaFQyCY87gArPJ1OB/V6Xe5bPB6XCp5ODrhcLsds\nTBJXPdbJnJOqpa866UFp7HA4FDd0yuLY98WKAtsM3low/a2gJDQYDGKxWAjB4L0x+5eZjGDgy3ct\nGo2K2kFLwtnTri8TlPeTbLH/UK/PtxqO7Qo0YQyHwzJyh8+0JqkARDbcbDZRrVal54q96F6vV+Sn\nHNHUaDTQ6/XEgfOxcLvdEmCxslcqlZDP5xGNRh1tJDaofn4weCehZXKQ1b5Na849gKZDrAJyfidJ\n7Wtze34LYCykpd7cCxl3mC0jwK3BEJUBesxhNpvFwcGBrFWhUEA6nXaMULPv3TrY1kZ+QXk3R1AG\nAoG12JD76VPGFaZqzWwPuby8lHF6i8VijWxzvbnfvwXsHLHlRssmeg6irtVqDmK7aUMOBoMi8dDZ\nK0oxNvUcWTwdmL3nn5nt5mZsBsIMqM1eWy1F1rJKj8ezlr0kKSIxorSyXC5jOByi3++j2WwCgJAi\n3XPL4eQkR0/ldLeNYJWv2+2iXq+LOVs2mxV5nnb4M6WM2m1QE1V9mYZjOtnBn6HnyoXDYezv7+Pw\n8BCHh4cibSVp2qXAi6YvDIhMYsvREbw0sWXygRXWSCQih6xWVGhp8iZiy+CLAYDpgJxKpWS2p+3P\n+zL0vQyHw9LzzCrZpp5pjsGr1WoSAFOGGAgEJIC7ublBrVZzBHOPHfXC/T4SiThI0CZzMJtMfhkw\neNdju+4bDaLbi7ieur8wFApJsnmX9tenABN/JE0ktlqmz/eaf2bsw4uyeO3CzSubzUrykIoYq0Tb\njNXq90krJLb065lOpwAgZFZ/ZULnqYktz+DFYrFGbC8uLtBoNNbMwkhsaQpmK7avFFryRmJbrVZx\ndnaGs7MzXFxc3EtsmTH+4YcfUCqVsLe3J3IoBgSW2H4faELDF58v4aZxP7rXy5Rd8KspL9eSDNMm\nfzaboVqtwuPxYDgcolqtYjQaodlsCqnlsPJisSgz/IrFIlarlfRkW2zGcrkUsy722mYyGfR6PUfP\nJXD7LJBMkdCy35qGGdpsrNfrSfCuSa0mXnRF1q6dHz58wHQ6hc/nQyqVcmSpd4k8scLHYIgmPpz1\nzOw0K+mmYRfvK0mp1+sVCTiVGDQXWi6XG+XnumqsR07oii1/zy6tzddA964ysNLVAq4fv5LYsmLL\n/YwJjlAoJBVbTWz7/f6jK7Y8P91uNyKRCHK5HEqlEj58+IBisSjEVrf82DP3+cFYisSW42NY/d+0\n5nriAYkt32E+R9oJ2+Lh0KN+7qvY8qseoeX3+5FKpRxzaWnEl0gkEI/HpVLLfdy+d3dDE1vOCb+v\nYvvUiTpNbNkeYlZsWQi4q2LL3n7rivxKoUd+0Czq5OQEf/vb36Rn80tS5Ewmg6OjI/zjP/4jDg8P\nZUOmFNni+0FXbAFIkA1s7s/T2NRnoH/uXd+vR8LM53Ocn59jNBqhVqvB4/FItUkTLjpB0gWW/Znx\neHxNLm1xC12xZXW2WCwKsWWFXffcsqo+HA5FJnl9fS0XezpJdFlN0FV4YHN/Ib9y3lwymcTh4SHC\n4bBDFr8rYMWWX82KLQ9srqMmt3x/KHGjk22j0RCTNVZ4uK73EVuaCJnEVierLO6GWbHVATCff1Z2\n9LvZaDRETk6vAu6Rd1Vsx+Pxo98V7vckQaVSCR8/fpT11q7XFi8Hc27tfVJks2J7fHwsgTMNiCy+\nDrpiaxJbXbHV68KCDPdVnnMfP37EH/7wBxQKBZlXbY5gMmOnt0B6ngtmxZbE1qzYanKrieNTE1uq\nFTcRW61S5Duoia32WHgLa7wzTMwsxw8GA4fBAWUCHBRPAyBtmKFnyjG7GIlEHLMuLb4P7rq33/ue\n615KnWk+PDxEs9mE2+2WPm2SJm5kDNj1JjEej+WQYfC9rc+N7uFbLpcSPLMaSmKoL0qLXS6XmLmd\nnp7C5/NhMpk4Mvkej0fuOy8SWJqKaVOoXq+H4XDo6MO9y4lVo9fr4fr6GqenpwiHw+h0OiJ7TafT\n4lK67esJwEEYtUR0sVggk8k41mKxWIgRBg9xPfOZBhmTyQTtdvtBg+A595LjfSgR1yZCVjXzMHi9\nXpnDnUgk5F3S8kINju9pNpvw+/2Yz+dotVpStQ2Hw7i+vpb2npubG5lhOp1OHyRFprEQq0Fc61wu\nJ865Oli36/w6oPv47ks2ayUUDfxotkilhsVmaFKqZeC8OBe60WhIy0C1WpXRPT6fT9aIFxOVHDlF\nmTFH3oXDYZGG89y1uB+a2NI3hOaUbrfboZJ5yjZGU6HIFgFOEbm8vMTJyQlqtZoQbW3SxzXnZ6OM\n/S3NJd4pYsvNdDqdivkP5621Wi0xkyGxpSsqL0ox+PJvyoZZbBc0YQGAUCgkcvTpdIpQKIRarYZa\nrSb9C+PxGJ1OB8DvUhRmp5nJptxEB+HbCB6YTBLpDZMHpenIx0otAFFUeL1ejMdj1Ot1xwbLirnu\n0WRwToMv9nDye+gSeVc1YROGwyFubm6kj7vT6QiZ0z0x295fr98D9rhns1ksl0sEg0F5vhlkUalA\nSbff73f0E5HUtlotuN1uqaQz+bgJlModHh7i/fv3ODo6wsHBAbLZLEKh0Fbf/6eGJrapVEoCoLvG\n5jAh3Gw2sVqtMBwOpdJGGR1HOTGQosSNTuP3ga6uPGfZ45XL5ZBKpWS0j+2hfrvgCBSqbNh6Yk4R\nsFiHJqRs3WH8qos02sCNCSa2g+jqnI5zw+GwI7blPv0UY/d2EUw80Bm+Xq9LSxuJLccjmdMFvgWs\nzjJ532g0UKlU5CqXy7i6ukKtVkO/35f2Kz2GSxNb7YL/VrAzJ4OuBrGq1uv15AButVoOYyGdyaKk\ngxVbHrrsNeBDabF9MIN5EtvpdCoz4+gS2+12ZX4n8Dup7fV6SCaTKBaL6Ha7GI1GkgF96Aibtwq+\nP1Q+8LDUPZYEiS2lxTx0q9UqxuMxms0mzs/PZYMlsdVmRZsu3R+tq4Ek1A8BiS3XmNXIQCAgFaTV\narX1+4Due1ytVlKxDQaDIkXWZm2mMYk5l3O1WqHdbqNcLguxZWXvSxXbZDKJg4MD/PTTT3j//r3I\nkEls9We1uBua2CaTSbTbbTHu2XT/SGy5x7VaLQmIuK7amI2jLfgePmSvY8CXTCaRzWbFryCbzSKd\nTiMej9tA+41DE1vGYPTKsK0694PnJPvem80mKpUKqtUqarUabm5uhNDqgg3N+/RotuVy6YhzWa01\nz+pN470svoxN5lE6djErtk91bzmukvtwo9HA5eUlPn36hE+fPqFarYoPCYktE/RUVW1qTXlLSeOd\nIbam/IXW9Lpiqy2xAUgmKxKJIJFIyKWJ7VvMZlg8Drq3l8TW5/PJ87BcLtHtdlGpVOSwmUwm6PV6\ncLvdyGazIosdjUZikEPCt63QBl98j8zNnNlnjhdhtc7lcsl4nmazKcGzNvqiK7J2vdamGKY8zpRx\nPRTD4RCLxQKdTgflclmq7jTZSCaTANZ7wLcR+nBjUiedTjvIqNnDpf8ffUDOZjOUy2VEo1EhtvdV\n0rW5ycePH/Hjjz9KNdgamTwOJLaxWExaa8Lh8J0VW47IG41GjndQKxXMcU2bJKpfgsfjQTgclrmZ\nh4eHUrFNp9NIJBIA8KaCLAsnzIptp9MRGexDWkN2GTpG5Xg8Tmo4OTnB5eWltAGwp1O37nCP1M7I\nJrFlbKurdoBNFj4WJLa6YkuFCxM5rNg+ZZKOxQEW8BqNBi4uLvC3v/0Nf/rTn3B9fe1I9vN3azm6\nXvu3GKO+vU/8QOjDlRsA++5arZaU5Ov1umQtzKDX7/fLDL1cLod8Pi+Dijm3b9vlh7sOc115CJjm\nDHqMjzZcAbBWxZhMJkJqt7liq6vd7J1jRjAej4tsezQaOSqo5tw1Snf0u8afrc29vle2n87LPDC0\nu/JsNsNisXD0C28rNr0LjyHy3IuZjGAlj2u3qYrOd43XwcGBVPA4dkLL0+0+/HAwQx+LxaTPmb3j\nyWTSkTii0uGu+cJPBe4TlCHn83mk02mZTfwWg6xthm7xYsGAlXrtYm9Cz0jm+/89n6u3gE33Sp+D\nq9VKepI5yq5Wq+H8/BwXFxcol8toNBoYj8fiCUNDIj1rmgWdbrfrMOnTEyD0etg99euhvX2m06kk\nCdjPSvXJQ3nEJhNUU5HW6XQkYdTpdHB6eopKpYJWqyUj9dgeRuKqlTc8198yt9naU4IBMV/UTqcj\nZLZSqeDq6grn5+e4ubnBcDh0/L980QOBAOLxuIwa2Nvbk8HU2gDoLS68xddDJ0A2VahM8ACfTCZC\ncPXolG0G3yW6NTKQTqfTUtXm5rwpW29WfDSxNStD3wv6M2gCtqk6ZXE3VquVvAMc69TpdBw9mFxj\ngpVxkq13795hf38f6XRaxoK8NZnUa4HX65WRSavVSswUGRh5vV5ZK3pPAI9TOzwWbrdbqsipVArZ\nbFYctDk72uL1gITInGP7pXE/Fg+HTgQuFgtx/WdFVhu1XV9fYzQawefzSW86CS0JrtfrFdmy2+2W\niQ7sBaXRItdPn7sW3w4m5Bn/saXisYpPs8+a3iKs0OqLEyL4bLB1SpuHsXK8LcrTrSa2LMdTrlGp\nVHBycoKTkxOcn59LBZfEVvc8ulwuBINBJBIJ5PN5HB4eSkAVjUbtzNodx0MILWHK4LnB0C1428H3\ngxWieDyOdDqN0Wgk7+ldI7aA25mmpqQVeNw6fAvMKrJJcp/rc7xlLJdLTCYTaQNpNpsO11xCB1KB\nQADJZBL7+/sy8/Lg4ACpVArBYHAn3MW/F/g+LpdLeL1ecRVnNcflcokJnpl4+l7POSu2NLTK5XJI\nJBIi37N4XdDOvHqOLdUsdj/8erDapws0zWYTl5eXOD09xdnZmTjt8mLrUzabFRdxPU7G6/WKyoXe\nEcDtyCZOeWDVXauo7P767dDEVpPax/CIuwzEyGdoZsqL0yA4kSMSiTh+jsfjWZt1/Nax1cSWG+5g\nMHAQ27/85S84PT0VKRx7HgFnwGxWbIvF4lrF1mK3YFZpH9K3yWdRV2yDweCjnHm3AWbFlqN3hsPh\nnRuqvrd3bfzPSWqBW0mtrdg+DqzY9vt9tFotMTfZ5JrL9Q4EAkin0zg8PMSPP/7o2Id1xdbi8WCf\nF99LjqVgkMz14Jif5zC708Q2nU4jm81K768ltq8T8/lcZMitVuveObYW94P3TccONAI6Pz/HL7/8\ngr/85S9oNpviMTGfzxGPx1EsFpHJZPDhwweUSiXHjGASqdFohHq97pghTi8JPRaPcnKtvLL4etCH\nQxspfkvFlm2WnEtLNerl5SWurq5wdXUlpmGUHAcCAcfPYlvcNnGarSG2JsGgzLHZbIor2OXlJcrl\nsjjHaWLCIIqSjUAggFKphIODA+zt7aFQKPz/7L1nkmRJcjSowTnnEUmK9HTLSEMgAM6DG3x7Cpxh\n9wYQ/AZEFtfAHwzQM02qKmlwzvn+6FVre56RVVk0g7iKuGRNT1ZVVvh77qZmamqOXh8bUJ0mKL0i\nSWUmjLKdXc+ENjzSZmPH/vyY/z6/3y+9c+VyWcggs8Xm/LVd/STPAZ1l1Q6/+lKyPZ4PYSZ8SJC6\n3S5qtZoYnfT7fUku6ky21+sVOWqxWMTZ2Zm0g0Sj0aO6iJ8DbrdbDNm8Xi8SiQSy2azMctcTAcLh\nsLiBUx5ptgYw+NVL93/tquCZZyP/vlgsJhMIwuGwjEizeF6Y1SI9i7bf76Pb7WIwGAgx2nVm01CQ\njtzRaPTopJBPwa77TZNUmrXp2eDspWUMO5lMHGNaeFaWy2WZ8c2KbSgUgtvtxt3dnUMFQYJEvwPG\nNHquuDZItXfc06H9QPSIR+Dh/bgL5nlKvw+u6XTqGOPDZ6PRaIhhKROY8XgciUQCXq/X4Uui49Zj\n2dujIrbaEXUymaDVagmhvbm5wc3NDdrtNqbT6YMHxu12y4iBXC6HXC6Hi4sLIbc0jdKD4S1ODzxY\nRqORJE14kT/m6KgHoPMiP8X5x5wFXSwWJbhhsEoDJjpKM1mwD9AZTco3Ke3i0rb4Fn9Ay7QXiwX6\n/T7q9Tqurq7w5s0bVKtVtNttaQchmeJiUjGXyyGTySCZTCISiVii8wWgAy6Xy4VIJIJMJoP1ei3u\n05yPSYkpFU40U9OJHo/H4wiYWP3RZiamwRsrtHS1TqVSSCQSMnLCnPNo8bzQZjic287e2m63K9JY\nJkd2Be3s7U4kEsjlcigWi0gmk4hGo2KucyrQ5IafJ82hOCam2+3KqtfrqFarGI/Hkoyi/0AymZS2\nuUqlgnw+j0QiIVU6fra8x7iYpOJ9yznkvIs52vBjzQItHlZomfzWxmnvM+Tjc8F3jiZ/XN1u19Fj\n3Ww2RUpOA7FYLCbmt7lcDh6PB5PJRJaeHX8sruRHR2xZ8RmPx2g2m7i+vsbPP/8spNYktoTL5UIi\nkcD5+Tlev36N169fS68CHTh5yZ4aIbH4AxznMxwO5SLnGJ+nEls6KZ/ac8R+SZfLJWNFtPnIZrPB\naDTCcDiUzPU+gMTW7/cjGAxKwM1FkmWJrROm6dZ8PsdgMECj0cDV1RV++eUXuaBpYsKqPgO1crks\n415IbPmZn9K78zXAagKf23A4LKPM4vE48vk8hsOhvJN0YuXX5XIJn88npNTr9T6o0NbrddRqNWw2\nGwyHwwfElmcjq8IcO8SRIzwnLbHdD5h9n5Qgk9gyvnrf7GKa1SSTSeRyORQKBUlknaLcXM9wp6Kl\n2+2i0+kIYeFi5XY8HgtxYXW2UqmIsjCdTiOTySAej4u6yOv1YrPZPCC22q1+vV7vJLbcR/sOfhx0\nP62enMG4h5/5Y/4cupq+XC6F19AEt1qtyqQXPjN6r9hKyer92dkZ3G63kOJer4fhcCj7fSw4OmLL\nF3Q8HqPVauHq6gp//etfcXNz43B4NDMkbrcbiUQCFxcX+PHHH/GP//iPIpFhj4LZ7G1xeuDBryu2\n/X7f4RpqQhNbBmxMkJzSc0RiywA6EAhIFnI8HmO5XMqIkel0+tw/roDElq0KZsU2EAg4JEcWf0Cf\ny2bF9pdffpHqH82jKKnLZDIoFApSsc3n85JgtNWDLwNzHBfPpVgs5vAE4BqPx+KaOhgMxCuAy+/3\nO2Ry8/lcDPIYiJtnJCu2kUgE8Xh8Z8VWj5+weF5oYjufz6ViOxgMJNGrK/bvI7Y05iwWi472jlOC\nmfyjH0ytVkOtVpM+ybu7O9zf32Oz2ThUEslkUmZ6f//99yiVSo47ivJuvutMRumlpc9US2nlFNtE\neA9aPA06cciKLZOIrNhqr45dMN+30WiEZrOJq6sr/Pbbb3j79q3DPGwwGCAcDksBJRqNIp/POwp2\nLpdLjKX4M41GIwCwFdt9g26yn8/nMhC5Xq/LoWA+RLo3yO/3O4yi/vSnP0mGRcu1vjR29VnsMiXS\n36cPKnvZf1mY+2G64DI7zWwqh6CzR5T9arofkxUIBmt0Jzy1YI2BSygUAvD7IcrkQCKRkATBaDT6\npqRF95fwItJ9s7wg+DWbzYp0LhAInGSVYRd2vTs8kxeLBQaDAXq9HlqtFhqNBmq1muP3aSf6XC6H\nSqWCcrkss0zZb2nd6L8MzM/Q4/E4jEXo5M41mUwcsuLZbCYJHgbRlFCyqtTpdKQSt2u/+Hdqs6hk\nMolYLCZ/pr3r9gdsKSChZbWIz8VwOHzwe0x/AvZOJxIJJJNJJBKJZ/iXfHvsOh91Ty1NgJrNJqrV\nKm5vb2U0T6PRQKvVkjiV0wXoPXB5eYlXr16hWCw+8PMwfwa9F4xrtcTcVF2sViup9lp8HJgQZ+KO\ne0PTJ31e8t3RbZX0H9HTXWgOdX19jaurK0dyeLlcwu12iyIim83KRIGzszOcn587WoOovqHc3O12\nH8U+Hw2xXS6X8nAMBgPU63Vx3NTzuHQQpSUalMLofjk9I/Frgz+XloTojI5u8tY9E6fWk/ItwZdf\nHyyNRsORQeVzRhWA3++XPlquly9folgsIpFIiGz1lKv+5lgtPa7lOT4X0xSKCg1+1UY28XhcLohU\nKmXfv/eAyhmOj6GpxWAwkCqA3nP2eabTaRSLRZlZm81mH4xYs/j64N6wSsN7iKZTi8VC7iCea+zX\n4jxFc06xCRpGpVIpFAoFcbxm0siOctovcG87nQ5arZaQrl6v96iUkXciE4MXFxcoFAqIx+MnlxTU\nxQp+lpT3D4fDBxVaKh3S6bSYbbGnNpVKoVQq4eLiAplMRsyhvtb7Yt/Bj4MmmIVCQRL3k8lEVDCN\nRgPxeFx4RygUciQTObKUlfPhcIj7+3vUajWMRiNst1uJURi/aCl6JpNBqVRCqVRCMplEIBAQdZTu\n89VjC48BR0NsF4sFRqMR2u22HLidTkcML7SOnWZRzKYwY0xiqyu13+JiNQ0EdOVZy0RWq5UEf9Fo\nFG632wbWk+vuxgAAIABJREFUXwF6P/hcsReQbq68fGq1mhxW7F9hfxpNyF6+fCkHi362TjlgM91U\n9efxLT8TVmh1r2AqlXIED+z35KIRQzqdtu/fDmgXZJ7J7AvSLsh01dUVBlbuyuUyXrx4IXNM6YJ8\nbO6N+wy+G/w194ptFazkcLlcLgwGA5FUtlotx5ziXZUAJpUZ/HFUSSwWc8gobUJjP8C9bbfbuL+/\nx83NDWq1mlTwd4GmgfQruby8FGJ7auen6RiuDaI4o1aTW/ZmptNpFAoFcYnnnFrtA2Pneu8XqEDi\n2Tafz9FoNLBer0W6TzLL5KHP53P4GJDQahdkFu9Y4WX1nspAElqudDotcYzf7xe5sSW2BwBWbDud\nDqrVqqNiaw6aJkhsmfHgWAEaYehL/WtD23mzSshKoR7QTX2+KRuz+LLQl89wOES73ZZqLS+f+/t7\nNJtNR0Xd5/MhkUigUCg4XLXNiq0N1PCA1D5XEMseaEoqU6kUisWiLF4OXOyTpgGWxUPoigTP5Jub\nG5HumxVbkiNdsb28vEQ6nZbkI8mTxbeBDpB1nxhJLRPE/L7NZgOv1+tIaHyoYktim0qlpN9SV2zt\niJH9Ag2OOp0O7u/vcX19jXq9jl6vJ++0Cb/fL3fi+fk5Li4ukM/nT65iqxPmbNOgWRRbMzSpvbu7\nQyaTQS6XE5l+Pp9HoVAQ3wHKkrm+Zsx6LKTnW8HlcknFlkkfktrlcolerwcA8g6QcLLVYzAYyNmp\nOQBb45gYpmdJsVhEqVRyENpMJoNIJCItI4FAQPxLGLcul0sHsT2Gs/Zgia35kumKba1WeyBFNqWP\npvadPVw00PjUockf+u9mD61+SDebjeju6X5HUyIuBoDBYPCBw6TF+/G+/dFScE1UB4MBOp2Og9TS\nkY6zwmgExUqGDszprp1KpeQiP1XTm8dm/Jrra/7dJpHWbQhc+XxeelLOzs7EkZdLm0UdwyXwJaHP\nNpPY3t3dOYgt+71IXGkuls1mUSgUUKlUEIvFHJ+z/by/HXTFdhfM85T9XaxEkdjummnKfWQyg0ZC\nhUJBRjqd2lzTQ4B27q3Vari5uZF5me8jthw5wjM1m80iFoudFLEF/iATLGCMRiMZ43N3dydxRbvd\nRq/XQyKRQDAYRCaTweXlpbjEc9Gv4lvAnr0fB1Zs4/E41us1fD6ftOS43W6ZrtFut+HxeOTOZPVe\n8xdWVgGIWV8oFJKzk4WUy8vLB4l4Kp1Mg0v9LHJtNhu43e4HHj+7/m37jIMmtiYh1CREj2HRpj7a\nwEbPdioUCnj58iXy+Tyi0ehn/2wmUeJikzelBdo8gBk89gqPRiN5IUieIpHIg35hi4+HDr710nPk\nOAuZVvvsEWy1WiJx93g8Mp6EEtaLiwtcXFxI9kwnTGyg9m3B4JyJqkAgID2zNPTSzq7BYBD5fF7O\nhXw+7zCKsvv3OMxknQ7cWJHodDqShXa73Q+kU99//z0qlQqSyaQdn3QAMI1OZrMZxuOxjH/hjG8m\nl01ztnA4jEgk4uhjp3LqVBOA+wzGMJTQktR+aCoAiwiJREL2WI8/OQVQ+sneyeFwKGZ6HN0ymUyk\nTzIQCODi4kJG+dBvgD2Z3/ousvHmx4GcgwoXAMjlciiXyxiPx5jP5/B6vaIA0+aVVJKaLZFer9fh\n3xKLxSTJUSgU5PngrPenqODMiTKcTEHucog4aGKrswyTyUSIbb1eR7PZlGZtbXjBkR18IM7Pz3F+\nfo6zszMUi0Xk83l5yD7159IBnkmcSFh1RZYkl0GBtu92u92OXj+OT7DE9tOwSw6kre3p0svV6XQc\nq9frod/vYzgcYrlcistjqVSSy0dnVLPZrBgR0eHT4tuBlwtHG/C9L5VKsj+mGVsikXAsLeXRSg67\nlw+hHcTpukhiW61W5dxjWwXfncvLS1xeXop0P5FISN+R/Zz3F3q/NbHlXTwcDoX0aG8LvpOa2JL0\n6NnQFvsFnbDq9Xoyt5bJi11gq0c4HJbkhQ68TwVM/uheyW63i2aziVqthmq1Ki1w6XRaJnQwPi2V\nSuI38BzE1uLj4HK5pHUD+F2dks/nHe2RbN+gI7FWk9Lzh5yF/41xCR3FtQ9IKpXaOerpQ9BnuEls\ntdr1UHCwxJYPBQkjDwpdsR2Px46KLR8ybVZxcXGB7777Dq9evXJUcT6X2OoMiHY446gYLlYGufQ8\nquFwCL/fj0qlgtVqBb/fj2QyKX++xadB7xH7mVmlNftcut2u7AerD9rYKxAIiO3+69ev8erVK4cM\nhAZDDOTsZfRtwcoQySuJ1HfffYfXr1/j/Pxc9karOSj1oZmcOYfO4iG0T4CW2vV6PanY6rPQ5XIh\nFovJfvz4448io0okEvaz3nPs2m+O6+I9N51OZb/1qBG+j5y5qCu2TELZs3L/sN1uHRXbdrvtGBGz\nC+as4lOv2HL+L4ktzU5rtRpSqZSY/aTTaVQqFVnlclkIy3Oov+xZ/HEg5wAg7YOs1FLya7YaamLL\nRKBWmCUSCUf/bDqddvRYh0IhiWV4hn5o30y+wgSMNtzlJItDeQYOltjquVu8UHXFttVqycOyXq9l\no7VlOvsgf/jhB/zwww8PBsF/bEXUlOJph2NadjPIq9frqNVqQmDNIctc0WgUq9UKgUAAqVTKkUWx\nFVsnPvR56P0xjbr4uddqNbx79w6//fYbfv31V6n6c1FSwhecl3WpVMLr16/x448/OqQikUjEBmjP\nCF4U7H/mXv3pT3/C3//93+NPf/qTY+YfyZRpaGWxG2Y2VyccOVCeFdt6ve74vey9I7H9h3/4Bwl4\nmUywn/3+QAc4hL7nzIptt9vdaXbC9zEYDErFlnNNOevR9q/vJ5gM1lLkD0FLK3VV/tRac9j6NJvN\nRGFoVmwDgYA47rOnlqtUKn1ST/Iuj5cPwb57XwaswNPoVZPaQCDgmP88GAwcZJgJQN2qQwMxrmw2\n64hTPjVe0ec4n1MtRT4kUgscMLHVw63NAcXawpovMvt5aE6i+x+1xFBfqGYfL/8bQVK9azQPL3pz\nbpUpbWVVWct5AoEAMpmMyAvYr5nP55FKpWR4PaV6Fr/D3C+zWs5nhf+bc8EoD+eMsLu7O6n480XX\nl7NemUwGf/rTn3B2duZwoGM2+pAOg2OEx+OR1oN4PC7vFaVwlADtuhzs3r0fWta/3W4xm83kou73\n+7i9vcXt7S06nQ5ms5lDFk7TLkrr2FfJqvgpBbyHBH3/rddrqc4ySL++vkaz2cRoNHIQWv4+JjN4\nt+k5xbpVw75/xwPKz5nM0CqYU3rPl8sl+v2+VGc5/mw2m8Hn80kVLpfLORxuY7GYGBZ+LEyDUt32\nxkWixVmq7PnUTrpPrf5ZOKE/r+12K6NF0+k0gN8LI8lkUpSbi8XC0brIKRtM/FF6zPNS78nH7o35\n/doLSJ/bh0ZqgQMntubMV01gzIyDx+NxDEsul8vI5XKORnwzoNWuYSzRa9DVjBkXauc1caLElUsb\nQ41GI8fPvVgsHHLpUCiEbDYrfWfM5MViMQSDQUtsDWhZ3Hq9fuAwbe7FrsWEQ7vdxng8ln1nNT8c\nDjtmyFHOfnFxgVwuJwfOqcms9hWa2DJwYN8s3yHTYMEG1U+Hvvym06m40nO8z83NDdrtNubzOVwu\nl1TOQ6GQSKs4ao0GeTrJaLGfYFZ/OByKzLxarTqIrU4sEwycadhYqVQcDrmnRHROBSS27BUkgTo1\nRQZb0arVKn777Tfc3d1hMBhgNpvJ/HQ90qdcLot0+3OJLeNhk9TOZjOJb/j+6XF2TELYNpzPAz83\nv98v5rR+v9/h70IzWc079NQGuiBHIhExs/yceEX/Xi1H1t5FuuXxkHptD5YZ6YqcWbElsdUVPF2x\n5aFhVmzNh4REiX+muckkQq1WC61WS4aUc/4sJa6azOqfdT6fPxgvw5+HmbtCoYBSqSQVW62pt8TW\nCZ3soMNxt9t12Kf3+330ej0xgdIyY1bWdc+z6aadSCTEdOzi4kIqDhwJE41GZeajJbbPD1bamSXN\nZrNSJWQmGnBWiOxomadDE1uar93c3OC3337D9fW1OCGzYqtNZGh6EY1GxTCIowkswdlfaMd/Etur\nqyu8ffsW9/f3onbRgZFZsc1ms+L2+ljF1uI4QKdXJrW0Qu6U3nPOLq1Wq3jz5g1ubm7k7CThyWQy\nMs+5XC5LxfRTiS3gVDeSQGmCy9hGG6tSRaPVZ5bYfhr0Z0ZJst/vRyQSeWAua1ZLeWfqySja7PJT\nK7a7fo9ZGDLVNoe09wfLjDTp5ItqypE1SGxTqZSQRW2dvouEmFVh85LWMxp5oXNcDL+yV5ZOujoQ\n1A8MFwO8TCaDi4sLVCoV5HI5Wclk0kG0LP6A7rvWBhesKDQaDTSbTbRaLTSbTXQ6HccBzz3WCREe\nHqz8JRIJlEolvHr1Cj/88APOz88dPbXhcNgGZ3sESqx0xZZSZKt6+DyYDuOz2Qztdhu3t7f4+eef\ncXV1JeffbDaD2+12EFuaRGkpsk0GHQbMiu3V1RV+/vlnUbzoiq0GK7aa2FJuaV3jjxO7pMjA6d2P\nlCKzYnt9fY1YLCaLiVdd0HjKuJb3wfSiMeOd2WyGUCjkuCfNiq2d2/5loEmqjv9NVYt5Zu5Skn2J\n/dhVsWUSRBPsQ8TBRnWPlc4fa47X89fo2MjLmSYnJszq3Xq9lr8bAIbDocw2rdfr6PV6jmqt/v2L\nxULGDum+Ph72zMpR2kqLdw6sp+ECJSGHlj17irGT3j9T468b2nlQ615m7iMPb8p+2u22VNTb7baj\ngjsYDBz92ABEJkU5Mc1NuAqFAl68eCEJh1Qq5ZBX2cD86dg1BmI0GslzzkHhwOPPD6vjeoQI946G\na9qAo1gsSo8Kq4MWT4O5BzqpOJvNZMwa37V+vy9n33a7lRl8nOVHt89MJiMJIbsf+w19LtMIR7u7\nDodDx9xagvtKA8dYLOao2GvVlMX+QJOh6XSK+/t71Ot1DAYDLBaLnb/HPJN1q8Ep98/rmFWrCvUo\nSj127lPuJ/OMZksWW+b4nlKtttlsJNmk+3vT6TQikYjIxe3Z/PHY9Xntw2eox1yyVW8ymUiyg8+n\nHffzDNAESOvBHwuA+YL3ej3U63V4PB4MBgOxrI/H4/LnEqPRyOFYpnuG2FNGSWuv18NoNHpgVkQJ\nM3t8tRU3D302h8fjcZGhcNGQaJfZzaE8aOaePJZ40MRV7yn/tymjMavjZp81949L9zbz+wFIgoEy\nuV2LduuZTAaFQkEIEqU6tj/s48EqOF3KuT/9fl+CoMeM2wiO56HETe8X5a65XM4hF+el/SkOkxa/\nY7vdYrFYOFzc7+7uUK/X0W63MRgMxHOA6hmfz4d4PC7JodevXztmPR/KeXbKMDP72uGViV2OsCB0\nYKwnE/DeI+mx5+f+YbFYSIzU6XQeGMLtAveYZzITiac2t3YX+B4whmMSgAlZtjB97lnIGHW9XmM8\nHkuCn8ZVfFepSuO5rFUUmthaHA/obM5zm2rS8XgsPdfmHFs77ucbQV+wTxmBs16vMZlM0Ov14Pf7\nsV6vpZeA0gv+uUSv10O32xVbe52FZmCnezSZ7dAEjQcZDzA9bJmS41wuh3w+L4G3nlNFiRYzebt6\ngQ8F75NbmGMjdN8Bx/JoA67xeCx7w32iMzWJLSU3rKJrYszvNRMNNHDgSqVSjoHYeixFIpFAOByW\nCrqt1n4c2P/K6g1nnuq+HpLax5JWNFvTrsdauk+5Kxff+UgkAr/f/63/yUcB7gOJLaX9dPmkGoIq\nF+6dSWx/+OEHeY8ikcjBnWenBjOZrFs+hsMher2enL96LJoO5jnPUc805bt46qRnH0FiW61WcXt7\ni+vra9zd3b2X2DLZyGSwdqE/9T023wfGhbpK+7lVbVPlxpa5+/t73NzcPCC2nO+ez+fFN4R+IZbY\nHh9YseUs5X6/76jaut1uR88vYMf9fFOYcuT3kVtNbIHfHY0pM2WTPP9Mot1uO3oyKanTF/yuirHZ\n+E1SGggExPGYf2epVMLZ2Zksuh5z8TI4ljEkj5Fb3QdiVrxJUvv9viz2zuo1m80cv0dXf80qMP9u\nXa0NhUIy31hnLjkwPZVKSaWPl5CV6nw6zIrtaDRyVHCYKHifjF0HUel0GqVSCefn5yLl12ZreqQM\nl8XHQSf2OC6r1Wrh/v7+AbEdj8eOd8Pr9T4gtnYvDgumnHI+nzsqtrqNhNB3F4mtrtjqpK3FfoHE\n9v7+Hr/99hvevXuHer3+wYptOByWvlHKzU+9h173R5rV2q9dsaUXzNXVlRBbjvrx+/1IJBJCbKlG\nY8XW4rjAiu10OpWKLZWPNBMzpcjaUPcQ4tyDfWp5MGhDAh3AasdhLlb9gN/7w5hpZgXVDKC73a5I\ncNrttlRsHzN+0hITZqdNSase5RMKhcT9jgO4E4mE/H+ULu87zF5Ys/dZV2K5zN/DTL9JavWsYi0r\n7vf7knDQiQdd6eVLqPeGARQvFJo+sZJHB+pSqYRyuSwV22QyiUQiIaOh9F5bfBo4pDwajYqscTQa\nyYUbDocdruHsg9bgPEwmHvg+VSoVeZ/MviX9jlo8HWYij6qJRqOB29tb3N/fo91uYzgciqSJCb1A\nICCScCaKEomEg/RY7DfoU0EVTKfTkWw/k4omzL53qia0OQ17Me0zsH/Q0wU40oltV7v2G3A6XxeL\nReTzeSSTSTEpOlWY5nnD4VCMmVhBo7qMqjKSXF3U2KV2M/ve+Wf0ej3c3Nzg9vYWd3d3jmrtZrNB\nIBBAOByWRBMVTrrCbt/L4wJj9F0+NVSZmslJXaw7BBwssaWMkX2vlDVRcmhW/1h+n06nAH4/sKfT\nqQS7mkByQzmmh4G2WQ3WmTcuXYEIBoPS20dpq5Yis/9Ey44pwzyki56ZQS1R03PSds2TNZMOmpAy\nW6Ql3UxEaDOvfr8vvbOcAcbvpymDaSqkkwYc/0TiSoLEKm06nRbZ6mMzTy0+HazYmoQ1FAohk8mg\n2+0+GBpugjPd+JXvEkd56b52c/8sPg48Q7mYXKrVari9vUW1WkWn05H3m2ZRPJOLxaKY4QWDQbsP\nBwaqnqiaqVarYhj1mJEQk4d8P/P5vKPn0s4t3m/Q22I0GonpInvxzMkTBKcHFAoFXF5ePujZPFXQ\nZyWRSCCXy2GxWAjR55mZTqel53EymTjuLq/X+2gLF9dkMpHRhhxFeXd3J6vZbEoSyuPxyHtpTnag\nn4t9J48fpjuzuQ4RB3vKkNiSwJDYktzO53PpISDhYYaRg6r1vFEtkeFmamt0PbpAV2u1A6CuHlNu\nValURNZaLpcdlSPOstKLBwqD8EOAmQGiRJFrMBjIGAiuXVJhU1auF/tsubi/zHDSdVpL4bRZSSAQ\ncPR1MdjWRIh9JeZ+cOk9sQf+54PvMAB5H4LBINLpNM7OzjAejx+4ZJvQ+xMMBqW9gJe0duLUAbQl\nVR8PvoM8FweDAdrtNmq1Gm5ubtBsNqUPXhPbbDaLfD7vcHln9caOxjoc6Opdo9GQEXdMLO4C7zgm\ncLPZrKPnUpshWuwfTGJLL4v3EdtAIOCQtpZKJTHsO2Upsia22WzWoX7gudntdh09j1QbMdY0z0kW\nFZhs5Pitu7s7STZS1Ua3ev4+Elsu3pna48Key6eFQyazGgdNbGk4QVc3bRKjK7M0CiKZWiwWD+Qd\n5qBiAA+Ilrnh2hCDkjvKOiKRCBKJBM7OzvD69Wu8evUKL1++dGTf9NIk+9ACb01stXafWUMGv9Vq\nFdVqFbVaTSqsukl9V6ZoV0+zrvJqYmzuD230tVlJNptFJpORCno+n0ehUEA+n0c+n3cYSTHo0sPk\nD2lf9h2s2Pp8Pmw2G6m4amkM8dhhq/eHCSpdpd/1jtv9+zQwgKITLsdp1et13N7eot1uO8ZxMbmX\nyWRwdnYm/Vus2Foyc1hgRajb7aJer+Pu7u5JFVsS20KhILPYWbHVVSH7Xu4fWATQFVvdjrALJLYc\nXZjL5cQkzFZsf/eDYMW23W4LsZ3NZuh2u6JCY2KXvem7oOOuxWKB0WiEer2Ot2/f4tdff3XMEqfv\ngZkMJqElwQ2HwwdXXLH4PDwWcx8qDvaU0fNgvV6vEBeO91gul3C5XCI5pvHTY716GtxQvtz6AtaX\nL40wWBHU1aJIJIJkMikmNlw6AD8U+ZVZqTblL7p6Op1OMRqNpDdZE9tarYZ6vY5arfbAHEonDnRF\n/H3ERDtNa/LJpQ9tSiK14zFl4nSkzuVyD/bjEPbnUKF70S32C7tGdNGjgOZtrAR0Oh3pu+P5RgOZ\nZDIpc2vPzs4kyGVvmcX+wjyP6bDKkXmUnn+o35LJjVKphEKh4DDhO+UK3j7CTCTT30Lf7yb06Bq3\n2+3o16QzPeOkUyZKu6TIi8UC/X5fxhPyXE2lUg4Hf8aVZuGD5m2s+NbrdVxfX+P6+hpXV1e4vr52\ntIUtl0uEQiGJT5PJJAqFgqN1hwUj+26eDsz42uQ6h3ZXH2xEqQ2BgN/7OpgJG41G8j3szXwso2xC\nB3TaVCgWiz3ILtMIQDsr60UzolwuJ4fSIcquzEoqD1NNZNnvyq96dix/PRqNsFqtRFqjSagpQQbw\noKpt/h49v1TPk9U9znSg5sWgK/vxeFxkcfyZLCws/oB+79m/Va/XHVXa0WiE9XotZmBst2C/OlUR\nrNbaUUuHA+0iz9E+DL4bjQa63e5771caCWUyGZTLZYcU3QbO+wftk0EZMtt8dlVoGQdpk7h0Oo14\nPC79msFg0Epb8YeDfyKRkMQ+iwAejwfL5VIcqF0uF8bjsYxM4lez53Y6ncrYw263i2az6Zg1PJ1O\nxRCIcRFNvWiSyRE/yWTSYa5o1WmngV0jqA6d3B40sXW73SLVoGEBM2HMNrJvYVemcRd0ljoSiTiq\neiQ/XKwI0vGYY0oY3AWDQcmKsb/k0GTGhJYCs7+u1+uh3++LSQEXXff00q5rdNvTS1fYtfxGB8ra\npMvr9TrmydJNWvc46yHxZtJBu1Nz30wc2h5ZWHxJmP3NJLb39/e4urrCzc0NWq2W9NSyPYTJJBLb\nXC4nxlFMMh2C2/upwxxpx4o9A+h6vS7yxqc45FYqFanYcv63xX7BNIhjlY/3sgkSJl1VpOM5YyIq\n3k6d2GopMtWD7XZb/AZIbElqm82mYyIDpzLo8ZGUHnPkIRU07IeezWYSb5LYZjIZnJ+f4+XLl3j1\n6hXy+bz0vrNaeyhqQosvBzMm3/UMHMozcbDEFnDOBeOQaWaOOb+r2+3KwfohmNIrGp+cnZ3h8vJS\npDTc8FAo5Dh0SF51Ro1ZTB4Yh9hPpEf4bDYbTKdTcURtNBqO/lnK08x5srqSah6emujzwCexZaCs\nx0LozCMdp3O5nIyR4GIVl3tgZjvNtevffUj7ZGHxJWHKEjWxffPmDer1OlqtlqNiq8dZ6IptsVhE\nsVh0vMMW+w09C1P3WuqKLc37HiO2gUBAiG25XEYul5Oz2RLb/YPu2aR8VRszmtDKNZIwEltdsbUV\nwD+ILfB7wmez2eD+/t5BbPv9PiaTCZrNpqheOB6NEn6dlB8MBjLOhwoamlJx5KU2LI1EIuJ58P33\n3+PHH38Uw6hwOOxoDzjlvTo18N002yQP9Rk4WGJrEkTOw9xsNiLroDS21+s9+c/V5LZQKKBcLuPi\n4gIvX75EOBx2EDL2kHHx/9eS2WMAs/WcNUtjqGazKaRWW8p3u90H8gaSVJJOc7atdjumDT6rOzzM\nTXJcKBQci3Id9tTSmIjL9nJaWDwdupd+tVphNBqJFPnm5kb6K/VMRFYkSGhzuZw4jqdSqef+J1l8\nJLQ0lcaAw+EQvV4PnU5HVDja6E2f/byX2WudTqcl0D6W+/GYwIotDeImk4kYPT4mRWY/Pd/7ZDLp\nqNbatoPfwVYNfl0ulw+q23qe+3q9lpGTjGO1wVM4HEa/35de2uvra/R6PUfFzYy7EokESqWSFGte\nvXr1YLb7oZIZi0+DjtF14eiQFaZHE+lzdEg4HMZ2u0U+n8d8PofX60U8Hv8kclsoFKQPoVAoOAZW\nU4qsZ5we6kPwIcznc0e/LKXH7XYbg8EAs9kMAER6Tbc9LQPmQcy1a3atHufDijgzk7piy/VYNjMU\nCjkqtPawtrD4OGy3WzGOYeWGvVx6zuJ8Psdms5HzkGYklUoF5+fnKBQKSCQSNrg9QGiljk5Cmm72\n2vhPO/zT1FH3WfL/t2fyfoKV+fF4LHf+ZDKR9q5doIkmExisAPp8PrvHCroqBvwxr/38/FxG+9C7\nhF8Zx6xWK4m1RqORJIdGo5GoZlarlUwa4KJJFGOkTCaDV69eoVwuIx6PH0V1zuLzwOQU8MekCial\nzMkSh4KjI7YApDLndrsRi8VQKBQwmUw++GeYUhtKa3gw6IOaDwMPEE2gDu0h+BBmsxn6/b7Izxjc\n0ijKJLaUIfKzY/+rJrrmKCUSWwZP/PO067SWSTBo0i7Uuh+XQZRuhrewsHgaSGwnkwlGo5GM++C7\nz95KVuxYndNjPi4uLpDP5xGPxy2xPUDoMW6a2Gq/BJPY8h5mCwiTjfxvrATYau1+guMRJ5OJmD7q\nBJYJHQeR2MZiMVFL2XvXCT73TASm02mcn58DgBRgGFv1+32JkVhFJzFmdZWxGZ3JdR8v4zCOM9Rf\nmXDUVTmL0wTb/jweD7bbrRSfzGkwh/ScHBWxpcEQeypJat/XA7QLvKRJqLRVvd5Yc26mPrSOCTw8\nKUFkHwgXK6y8zPx+v/TUFYtF5HI5x2cZCAQezKU1gyf27miyaroiaxdk01xKy2psdcDC4uOxXC5l\nJjVdNzWxnc1m0oepDfwKhQIuLy9xcXGBVCplie2BQlds9fnMX9NQSD8DvIeptiGxZcXWdF212C/o\niq1WZrDP1gSD4mAwiFgs5qjY6ikSFs7xhZzqkMlk4Ha7EYlEkM/npXjQbDYRCoUwHA4xHA6lUst3\njnHAhhWLAAAgAElEQVQTVTWUi/PdSyaTyGazKBQKjnGTlUpFCgF6fBB/PovTg9lby954EttD5DVH\nRWy1GUUsFnvGn+a4QFv6TqeDWq2G4XDoqLBut1u53LxeL2KxGC4uLnB+fo6LiwtUKhVHJZVBrg6K\nmJWkxA3Ag/7YQ8wcWVgcCrRiZbPZOObW6nm1HN3FCgETewyoCoUCzs/PcX5+LkoNaxZ1eOC5zP5a\n+h/oiq0JGiZGIhHxOzClyPb83l/QT2MymQip4lSDx6TITGawFYzqKgAij7V3Nx78210uF1KpFEKh\nENLpNMbjMe7v7x1Sbq/Xi/V6LVVz/Q6ae6Krtel0Wkjty5cv8eLFC7x48QIXFxcOgn3K+3Hq0NJ4\n3VurVTaa2B4SjobYWnw9BINBpFIplEolGfuh+6wAOKqloVBIjGPi8bi8II/1u7pcLiHH/N8AbD+W\nhcU3gukIT1f5druN+/t73N7e4v7+Hp1OB5PJBJvNRiSIJK80B4rH4+I9QCXFIV6Op471ei0V+36/\nL54K0+nUYRZF6EkBuVwOuVwO5XIZ6XRaxvvYc/y4wHig3W6LEss0POJ5wGWTXH/A4/HA5/NJFTaZ\nTMroNFZ0tTx5sVg4Yi+zfY6/h4uj1kigLZG1YCsfzR61Ke6x3NOW2Fp8EOyd3W63CAaD0m/D/g8A\nDyTC7E+OxWIO0xAzuCGp1Rbz/DX7Yw91TJKFxSFBKyhWqxXG47FjvE+tVpOZ4ExE0Q01Ho8jm82K\n9JhVOq22sDgs0KF1OByi3W6j3W5LBe8xWSqToMViEefn5yiVSkin04hEIgcpabN4P/QYMADimk1S\n2+12JcmRy+VEjm7xR6zDXmSXy+UgtclkEuPxWFq+6GmgYy8SW8ZRnBvNpf1OOFaI32/fw9ME++Kp\nsAiHwwD+uP+PAZbYWnwQoVAIqVRKCK7uraIURstaTGc+7Sb9vootv/LPtD2yFhbfDvqd1hXbu7s7\nvH37Fr1eD71eTyq2JLbJZFKqA6lUSuZJ05fAVmwPE7pi22630Wq1nlyxLRaLePHihRBbW7E9TpDY\nbrdbTKdTdDodB6llMoTzVJPJ5HP/yHuDXUl8ktpEIoH5fP7AVFObte0iIqZ5G6ty7HO3BQILXbGl\nD8KusW2HDEtsLT4IktNUKvXkjM5TDlCzcmthYfG80E64Wor85s0bzGYz6fPabrcyvzKRSEhFhnMZ\nSWwtDhfr9Rqz2UyIbbvdRr/f/yCxZdvKixcvZH4x5W4WxwVKkUlqXS4Xut0uOp2OJEMWi4WQ2mMJ\nnL8UTPlnKBSSX+tY62MrabtiKxtjWQAPiW04HJYxm4/10R8aLLG1+CCsbMXC4jSgh7UnEgmUy2UM\nBoMHs0yXyyUSiQSy2Syy2SwymQxKpRJyuRxisZgd9XEE4Ei1dDqN+XzuGOmUz+fR7XYffP/r16/x\n+vVrVCoVpNNpxGIxhEIhcbW3z8RxQvfocwyUHuf3WIXxlLHrXbDvh8XXhtfrRTweR6FQwHw+RyQS\ncdzrXq8X5+fnyGQyIlM+NFhia2FhYWEhQZXb7YbP50MqlcL5+Tncbjfi8bhDrrRerx39tbFYDKlU\nCtlsFtFoVIzgLA4XPp8P0WjUMZIknU6jXC6j1+thNBo5vt/j8TjGvLFSS4dXG7RbWFhYPC/8fr8Q\nW4/Hg0wm47jbeY7ncjlLbC0sLCwsDheaeHi9XqRSKbhcLiQSCVQqFTErYZ8X+7jYqkBZUzgctgYx\nRwASW4/Hg0gkgkwmg+l0KmuxWDi+3+VySZKDi2ZB1kDMwsLC4vnh8/mQSCTg8XgQi8Uwm83kTt9s\nNnC73XJ+RyKR5/5xPwmW2FpYWFhYAHCawNHVvFwuiwuulhLy+7TJ27GNDThleL1eRKNRRCIRR+DD\n5MYuWak2wTFnZVpia2FhYfG88Pl8iMfjiEajDmdtfZ7z/Ka52aHBElsLCwuLE8cup3JKki1OEzZB\ncXrwer0IBoNSyZlOp1gul9JfzbE+70MymXQsPfLJtihYWDwvTuFct6eMhYWFhYWFhcWJg/Lz9Xot\niS3dW232Ve9CJBJBJBKRan8ul0OxWEQ8HrfE1sLC4qvDnjIWFhYWFhYWFicOElm3241QKIRYLIZM\nJoNKpYLRaIT5fP7BP0PPUPX7/YhGo2IyZ4mthYXF14Y9ZSwsLCwsLCwsThw+nw8ejwfBYFDmWWsn\n9KfMudR99i6XC16vV5ZtbbCwsPjasMTWwsLCwsLCwuLEQeMvS0AtLCwOFU8ltnkA+Pd//3f89NNP\nX/HHsfgW+M///E8AwL/927/Z/TwS2D09Ltj9PC7Y/Twu2P08Ptg9PS7Y/Twu/O1vf+Mv8x/6Xtcu\ny/4H3+Ry/d8A/s/n/VgWFhYWFhYWFhYWFhYWFh+N/2e73f5f7/uGp1Zs/18A/+df//Vf8ec///nz\nfyyLZ8V//Md/4F/+5V9g9/N4YPf0uGD387hg9/O4YPfz+GD39Lhg9/O48NNPP+Gf//mfgd/56Hvx\nVGLbAIA///nP+Kd/+qfP+NEs9gGUZdj9PB7YPT0u2P08Ltj9PC7Y/Tw+2D09Ltj9PFo0PvQNxz2l\n18LCwsLCwsLCwsLCwuLoYYmthYWFhYWFhYWFhYWFxUHDElsLCwsLCwsLCwsLCwuLg4YlthYWFhYW\nFhYWFhYWFhYHDUtsLSwsLCwsLCwsLCwsLA4althaWFhYWFhYWFhYWFhYHDQssbWwsLCwsLCwsLCw\nsLA4aFhia2FhYWFhYWFhYWFhYXHQsMTWwsLCwsLCwsLCwsLC4qBhia2FhYWFhYWFhYWFhYXFQcMS\nWwsLCwsLCwsLCwsLC4uDhve5fwALCwsLi+PDdrvFZrPBer2Wr/P5HIvFAvP5HMvlEl6v17F8Pp/j\nq9ttc68WFs+F7XYr7/F2u8VyucR4PMZ4PMZoNMJsNoPH44HH44HX64XH40EwGEQgEEAwGEQwGITH\n44HL5YLb7YbL5YLL5Xruf5bFe7DZbBzn9nw+x2QywWQywXg8xmazQTAYRCgUQigUQjAYlL3nOW5h\n8ZywT6CFhYWFxRfHdrvFarXCarXCcrnEfD7HYDBAv9/HYDDAaDSS4IgrHA4jHA4jFArB4/E89z/B\nwuKksd1uHYmp8XiMWq2GWq2GarWKTqeDQCAgKxgMIplMIpVKyVe/3y/klyTXYn+x2WywXC5l9ft9\nNBoNWYvFAplMBtlsFplMBslkUpIYJLkWFs8J+wRaWFhYWHxxMBhmhXY8HqPVakmA1Ol0EI/HEYvF\nEI/HEY/HkUgksNls4PF4EAqFnvufYGFx0mC1lgkqEttffvkFv/zyC25vbxEOhxGJRGSVSiWUy2Vs\nt1sEg0G4XC5st1up2lrsN0hs5/M5ZrMZut0ubm5u8ObNG7x58wbT6RTn5+e4uLjAfD7HdrtFLBYD\nAEtqLfYC9im0sLCwsPjiYMV2sVhgNpthNBqh1Wrh7u4O19fXqFaryGQyyGQySKfTmM1mQmqDwSC2\n2+1z/xMsLE4aup2AMmQS2//6r//Czz//jEQiIUmpRCKByWQipDaVSgnZsQqMwwATGZQgdzod3Nzc\n4H//93/x3//93xiNRuh2u1gsFo4EpNfrRTAYfOaf3sLiiIgt+wJ0b4BeAKQPQEti9NoHLJdLLBYL\nWavVSvpcttst3G63Q/rj9/sf/Bn78m+xsLA4XWw2G0ynU/T7ffT7fbRaLdzf3+Pu7g739/eoVqty\nzi2XS6zXayG1sVhMzm2L58F6vZZKHYkNe6R5N5lgVY79lOZXv98Pv98vdxfJDu8se3ftH3T8QcJD\nctNqtaSyN51OMR6PEYvFkE6npQc3EAjA4/FgvV7bZNUBYL1eSyKy3++j0+mg3W7L4n/n/vIsYNxt\ncdjgO6rfe77fs9kMs9lMznSPxwO32w2fzydneiAQePbK/dEQW168XAyW+GuXyyWN7jQ34AW8T30f\ni8VCAsF+v4/pdOog616vF6lUCqlUCul0+gGx3Zd/h4WFxWljtVpJlbZer6NWq+Hu7g7VahWNRgPt\ndhuAsyoUCAQQi8WwWCxsEPzMWK1WmE6nmE6nmEwmEtByjcfjB7/HNALTpkJerxexWAzJZBLJZBKJ\nREKkqoC9uw4N3K/VaiXB7mazkf75yWQiiRA+D/ad3n9wPweDATqdDjqdDobDIebzOdbrtX1PTwBm\nobDT6aDVaslyu92SpPT5fIjFYtJXn0wmLbH9UthsNiJ542KGYTqdwu12IxaLIRaLSeWTl+0+9X6Q\n2NZqNdTrdfT7fcmar1Yr+P1+nJ2dYbPZIBQKIR6POw4a9rJYWFhYPCfYk9dut3F3d4ebmxvU63XU\n63XpsdWkdrVaIRqNIpPJSO+WxfNhvV47Ku7tdhvValXMg7rdruP7WZHVmXufzycBkN/vRzabRalU\nAgCEQiH4fD5JMPPPsDgcsIo7m82kn34wGGA8HmM6nYr7+Wq1stW8AwETWsPhEJ1OB91uV6qz6/Va\nvs++q8cLmsbxXmaf9bt37/Du3Tt4vV6H6WM2m0W5XIbL5UIkEnl2f4yjIbas2FIOQzv60WiE8XgM\nt9stVQCv1yuVzn0itcAfxLZer+Pt27dot9sOuV4wGBS79XQ6DcBJZu1hY2FhsQ9Yr9dSsb29vcW7\nd+/QarXQbrfRarXQ7/cdpHa5XCKdTkt1wAbCzwsGuP1+H81mE/f393j79q2sWq3m+H6qovTSbqnB\nYBBnZ2cAgHA4jHQ6LQHQPrUDWXwcSFoXiwUmkwmGwyFGo5EUFhaLBQKBgJUiHwgoRR4MBmi321Kx\n1cTWvqvHDVZreS+T2P7000/4n//5H3i9XkSjUcRiMUSjUZyfn8PtdiMajSKXyz33j388xBZwzt/i\naAlKKQA4pBQul0tmcVEj/hwwD3pmP4fDofSw6J7bcDiMwWCA6XS6s8fJ4g88donu6iHQc/q0jF33\njLGfYFev9mMz+uwFYHEq4LvEXzPQ7fV6aDabqNVq6Pf76PV6GI/HjvPY5/MhFAo5ei/tu/N1YZ6P\nPAe5xuMxer0e2u026vU67u/vcXt7i5ubG1xfX+8ktnq+5S5iCwDRaBSpVAq5XE6qupSq2vPzy8Hc\nT8DZA/2UhL52RWbllUSWz4/+8wGI/Jh98/z/Lak9DGhXZLYizOdz8XshzGfJJ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fUYilQq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\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vL/H582d8/PgR7XZbRvw8xAdp9hXoQcja5ViDVUhmuDWpvetoHv1iU1LLqmQul0M6nRZD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elczerZ1dWVEJdYLOZaK2Zy4SX+DtPHYDQaYTabucyjuLfyHlN+bl0/74bXeuV+yUr+YDBwza0d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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d4f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def show_digit_imgs(digits, size=3):\n",
    "    fig, axarr = plt.subplots(size, size, figsize=(1.5 * size, 1.5 * size), sharex=True, sharey=True)\n",
    "\n",
    "    for i in range(size ** 2):\n",
    "        axarr[i // size][i % size].imshow(digits[i].reshape(28, 28), cmap='Greys')\n",
    "\n",
    "    # уберем расстояние между картинками\n",
    "    fig.subplots_adjust(hspace=0, wspace=0)\n",
    "    \n",
    "    # уберем значения координат\n",
    "    plt.setp([a.get_xticklabels() for a in fig.axes], visible=False)\n",
    "    plt.setp([a.get_yticklabels() for a in fig.axes], visible=False)\n",
    "    \n",
    "    # уберем координатные \"насечки\"\n",
    "    [a.xaxis.set_ticks_position('none') for a in fig.axes]\n",
    "    [a.yaxis.set_ticks_position('none') for a in fig.axes]\n",
    "\n",
    "    return fig, axarr\n",
    "    \n",
    "show_digit_imgs(digits, size=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Препроцессинг (до 3 баллов)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Почти всегда перед анализом необходимо предобработать данные: убрать из них шум, центрировать или шкалировать.\n",
    "\n",
    "Например, вы можете попровать расположить на каждой картинке цифру по центру, убрать одиночные пиксели, повернуть или масштабировать изображение.\n",
    "\n",
    "По [этой ссылке](http://www.scipy-lectures.org/intro/scipy.html#geometrical-transformations-on-images) вы можете ознакомиться с функциями, которые позволяют проводить геометрические преобразования с картинками.\n",
    "\n",
    "Также вам может пригодиться функция [np.nonzero()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html). Она возвращает координаты всех ненулевых элементов массива."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_preprocessed_digit(digit):\n",
    "    # your code\n",
    "    \n",
    "    return preprocessed_digit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим массив предобратанных данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# map - это такая функция, которая берет функцию, указанную в своем первом аргументе,\n",
    "# и применяет ее ко всем элементам второго аргумента.\n",
    "preprocessed_digits = list(map(get_preprocessed_digit, digits))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрите, что у вас вышло:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N2ImMLFHXPVVma6QrVhxJIJ3L5ZDL5fjI0ev1WFLicDgQCAQQj8c5YqOJtM8d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0wmE0wm00dfn/4kwp7NZlz/JfKlOnImk8FqtUK73cbp6SlOT0/hdDplEjMFciwWC0wmEy7D9ft9\nzjCm0ykf88VisRGewS+OdEejESRJwng85oZHu92GXq+H1Wp97rf4UYgRD5Huu3fvkM/nmQzEZiER\nbK/Xw2KxwHK5xHg8hkajgcfjwWw2e+6PtPEQmzCSJCGfz+P8/BypVApXV1dIpVIystTpdNDr9dDp\ndLBarVwa+Njr058kKRuPx1CpVBx5jcdjTKdTDAYDfh83NzdYLBZc5wWgEO4doGNIDfXBYIDRaMTN\n5+l0yteLQrpfCCIbKpw3m01Uq1XU63V0Oh0mHrpQNuku9zEMBgMuJaTTaWQyGdzc3KBaraLZbKLb\n7cqaNVTLWiwW6PV6qNfrWK1WXFaYTCaw2Wz8fI1Gw9H+XRewWq2GTqf7aJdXo9Gw3GkTamRfA0r1\nZ7MZWq0Wk26hUEClUkGn04HBYIDZbIbRaITdbpc9HA4H7Hb7J3/POum2Wi00m020Wi1ZWYOIdzQa\nIZPJwOv1wmKxwOPxwOv1wuPxcLS9LU3hx8D6zYy+O+rrqFQqLilarVZYLBYeSnlubC3pjkYjDAYD\n1Ot17ugXCgVIkoTpdPrcb/GL0O12USgUcHl5iXQ6jevraxQKBa5LfeymQU0EioIlSUI6nZYpGEwm\nE5PEXWRJGYHVar3zOUajER6PBx6PZ+tJlxow4/EYzWYT+XweqVQKkiSh1+sBAKxWKxMeaW4DgQCs\nVitsNtu9MiiaZqOJNipZVKtVJgtSQYzHYyyXS+TzeRgMBgyHQxwfH+Pk5AROp5NlZK+ZdIH3WQqV\n49LpNNLpNCRJgkajgdls5mEUp9P5yfr7U+H538EXYD6fc+22Xq+zZKxYLHLUso3o9XooFAp4+/Yt\nstkslxWo4fKxJgA1Cnq9HmtIdTqd7MIkaVkgELizDmkymZhQ73oOdYKJnLcZRLqj0QitVotveuJI\nr8Viwe7uLuLxOOLxOJLJJOLxOCwWC+uj7wPSW89mMxSLRS4ZaTQadLtd5PN5ToMnkwny+TyGwyGK\nxSKWyyXcbjcODg4AfHwH12uAWBYaj8eo1+u4vr7G9fU1RqMR1Go1HA4HnE4n3G63rC7+3NhK0hUl\nIpPJBKPRCMPhEKPRCMvlcuPLCHeButN0N6bG2vo8/22gRg2AOyP9wWDABHOXhtdoNKLRaMDhcMBo\nNN76HLPZzBkGSfOA38sORERms5m1wpvcvBQbaRTxDodD6PV6TkkjkQgODg5wfHyMcDiMcDiMYDAo\nmyq7z+8hIqdSF5Vpms0mSqUSbDYbBw3z+Rz9fp8HKxqNBmc7d8nTXhPo+yI5XqPRQKVSQbvdhsVi\nQSAQwN7eHvx+P2dtm1KS2UrSBeQXi/jYZuj1ethsNni9XjSbTRgMhgdtnsxmM64La7XaW5+j1WpR\nr9dhNBrvfA4NZdjtdhnhmEwmHnMNBoNwu93weDwbTbp3gVJTii5PT0/x7bffsp7WbDazRO++IGE+\nZQnL5RI6nQ6FQgE+nw9ut5vHial5NxwOWZUznU63Smv+mKBj0u/30Wq1uEY+HA7h9/sRi8Wwv7+P\nQCAAs9m8MYQLbCnpioS7/vdthk6nY9Itl8swGAwPeqJMp1N0Oh30+/07X5eI4WM1QxL3r09L2e12\nfPvtt3jz5g1H2xaL5V6Npk2D2WyGz+fD3t4ek+53333HU2IajeazL2Sqxa5WKx7XttlsCAaD8Pv9\ncLvd2NnZYbKliJdGiGezmRLp/hvU16HJQGpKDodDWCwWxONxHB8fcyltUwgX2FLSFSGegKJNHo1V\nOhwO2Gw2GI3GjajnfAxUTyXHKurAku6QGiwEUcqkVqt5rFQ8wUjpIfo3UMT00BgMBjL/AKfTubVN\nTRq46ff7WCwWHN1/KdYveq1WywMtbrcb4XAY+/v7yOVy3I2nc1ulUrEkUMHvINJtt9tot9vo9Xpc\nXjQYDHC5XPD5fLDZbB/0Np4bW0+6IjQaDXeUqcu8u7sLj8fD8+6bDJPJBJ/Px3Z/oVAI33zzDQ95\nNJtNWX13NBqx1Ih+xmq1ym4uRBwkS5IkCd1u91HIkFI+mgjq9XpbSbqr1QqDwQC1Wg2TyQQHBwcY\nDocP9vriZJlKpYLT6UQkEmHSkCSJ3wf9qUS3ctC5JkkSR7jz+Rw7OzsyA6hNkYmJeFGkq9VqYbFY\n4PV6EQwGmXg9Hs9n19+eA2azGXq9Hi6XC+FwWNbpzmazyGazMhLrdDpMxiTjcrvdMhkXqRmazSZq\ntRoWi4XMfvAhQfpSIt1+v791ShIiNhodbbfbqNfrD37MxDKO0+nE3t4eNBoNOp0Ocrmc7P1sUpS2\nKRBJt91uYzAYYLFYQKPRyAIQhXQfGeJ4JnXQTSYTjEbjVugaqVa4DiobGAwGGYn1ej20Wi20221O\nqZxOpyyiHwwG/JxGo8F+DqPR6Nb3QPaRlK4R8d/moHUbKGUme8lNb/pQ1Ek1apo4o7IM1cFbrRaq\n1apsE8SXYr0BPBgM0G63UalUeJpSfH8KPgRZbtJ4fKfTwXQ65aEesnHcxOv+xZGu6OAk+pFuUvfy\nc2EymeD1eqHVamXkR/Km4XAIrVbLPgDinX0ymfBzBoMBlyPuikAlSeKoulqtotPpfDAJdxfUajVM\nJhOcTufWlHREwtXr9eynIGpqaVLw+vqavW+/1jZTJPVSqYSzszP89NNPyGazaDabAH4/nynS3dZz\n97Ewm80gSRIKhQLy+TxarRYmkwnMZvNzv7VP4kWRLvA+bRMfmx5tfQomk4lN2dddq2gMWPzc4gUq\nPoceZEV4G0qlEv71r39Bq9VyHfG+9cydnR0Z6Vqt1o0nXTpuWq2WSddsNnN9dZ105/M5DAYD3G73\nV/1e0pkPh0OUSiW8ffsWf/nLXzAYDD7YPnHb3187ptMp2u02k+429Q+2hnRFkiC9aa1WQ61WQ6/X\nw2w2k82krz+2GXeVHb4G5G4lDpdQE6zX67ED1qc8K8Rygtvtht/vRygUQigUgtPp3HiNLg2j6HQ6\nuFwuRCIRbl6S/lOn02G1WnG55b6lFhGitJGGXohgqW5cLpf5/6tUKi4rUY1SFPhv+zn9JRBLMmRw\nQ+Pay+WSfXNNJhP0ev2tQcgmYGtIF3hPvKPRCNVqFalUCul0GvV6HZPJ5Jnf3XZhOp2yoLxWq/FI\naqlU4r+TAuFjzTC1Ws1a3FAohEgkgng8jmg0+tGptk0Bka5KpUIgEMCbN29gMBjYVjOXy7HH7dfW\nqUWJH5V6er0ehsMhJpMJ3+DoPCcSsdlscLlcMJlMG1mjfCqQ/SXpmGlLzHw+59Ka3++H0+nkpvQm\n9hW2inQBcMRRq9VwdXUlI93XejJ+CSaTiczg5e3btzg7O0OlUmFNsGiJdxfUajUbwoTDYUSjUSZd\nitQ2GeImBzJ+DwQCvLF3Op3C5XLBYrFAr9dzc+ZLIPo8EOmSFaFIugSj0QiXywW/3w+XywWz2fzJ\nwZWXDCr3iGP/o9EIi8WCSz4i6ep0uo08VltHusD7OpxoQbhpB3YTQY2byWSCcrmMm5sbXF5e4vLy\nEhcXF0ilUmi1Wp98HWqYGY1GljuFw2EkEglEo1EWpW866JyhdJ1q0DabjQ2EdnZ2WH4oRrzrJZe7\ndLRiHZ0mp5rNJjc0e70eisUiut3uBz9PpQbyYajX68jn8xzVUeT7WpZXiksKJEniKb2dnR1YrVb4\n/X4Eg0E4nU6Wim0iL2wd6apUKhiNRvj9fuzv7/PkEAnZFdwNmnRqtVrIZDI4OzvD2dkZbm5uUKlU\n7n389Ho9/H4/AoEAR7fRaBSRSAShUGhrnceotqtSqRAKhaBWq+HxeGT+uTTdSBAHF27z/xiPxxyR\npdNpzs7ov41GIxQKBVYsiCDns9lsJivV+P1+7O7u8iAN1X1fOmazGd986vX6B9OCwWBQ1kvYRMIF\ntox06SAS6c7nc0ynU9ZPKqT7cayXFM7OzvDzzz+jXC7zepP7gEj36OgIR0dHSCQSiMfjCAQCMBqN\nG1/HvQu0zFOn07FhD22TpofY1BQJlyJakXRJ+dHtdtHpdJBKpfC///u/+Oc//ykz2Kd0eR2j0Yib\nxiRRG4/HbO9osViwWq1eRZQLgK1LRdKdz+fQ6XSw2+0IBAJb0cDdKtIFIDvJybB73ezmNXd4PwaK\nxshEpdvt8gK/zwH5BcTjcRwdHWFvbw97e3tfLaN6TtD5QgSm1Wo/iNjJiHy93j2fz2WRq4hut8u1\n23fv3iGVSuH6+vpWkl7/XXR+A0CtVuNtKES09D14PJ6tvdF9DkibSwMRZDJvMpngcrl4M7bD4dg4\nvwURW0W6RBrD4RDlchkXFxe4vLxEtVrlKR7x4tnUg/5coDFpWku/PkjxOa/jdDq5tLDpkcVDQpzw\no4YjbZ2gcWsRtIByPB4jn8+jWq1+dGX7XRgOh7yQVK1W88h1MpmERqPZ6hvefUHaXNoS0+/3uZFL\nDUfRP3dTsTWkK9o3DgYDVCoVXF5eIpVKcT13nXCVaFcOMhknJ/2vIV2Hw4FwOIxIJMLObq8BtJ49\nn8+z922v1+PNwfl8XvZ8cSiFOu6kBvlc0qXRV4qsJUniuvNrAMkc8/k8CoUCJpMJ1Go1bDYb3G43\n17k33Wdlc98Z5DUzcsvq9/vIZDK8tLFYLPLcNXktOJ1Olths8sF/amg0GhiNRl794vP5sLu7y9GY\nSAgfw2KxQL/fR6PRQKlU4pOcfAsMBgPLdV5aZ73f76NcLuPq6kpWOqA9Z+ukS1itVizWp+bXpyR1\n/7+9K31KI/26B2TfkVVkdQnqmExSM/n/p2o+Tk0lqflZUYNLjAuLrCKyCLwf8p47D4wYsyGNfaq6\nkkoZgab79PPce+45LF+oI8M3Nzcwm82y0mVuWyKRkHM/r7sO2jnyvAP/2rlykvCh0UmPiZlmJNa1\n+v0+qtWqiNU/fPiA3d1dMbogWXAqilpRraYW/CzQ9g4AgsEg0uk0tre3YbVacXFxgXw+/yA3rWaz\nib29PSwsLCCXy0k4o8/nQzgcRjgclpIDyXdecHV1hfPzc+zt7ckql+kFk84dV7Q0LbLZbPB4PKKG\nmARVj6pKzLjKvb29xcnJCcLhMLxeL0KhkBw6ZhczTbrUKfZ6PVSrVeRyObx58wa5XA5nZ2e4uLhA\no9EQYibpptNppNNpBAIBnXQVsDtvNpuFdFutFhYWFjAcDkX7+CVcX19jb28PFxcXYhYdDoexvLyM\n9fV1DIdD0bLS12BeoJIux3iZPXfX7L9auzWZTHA4HHC73eL1HI1GJ74W9aj1eh3FYlG698zNazab\nODk5ETlZt9uF1WrVSXfGMfOkS2+Acrk8Ek9er9dRr9f/I3O6q66r4zPUrT6N3jkAQJydncl46iQD\nkW63K7Kds7MzaWDUajWRQbXbbSGV73XkmiXc3t6i0+mMTJRxq6sGi7J0oBovsezl9/ul0760tDTx\ntWq1mhwWi0UWH8PhEJ1OR1JwP336JO5o4XB4WqdiKuDCi34L9AthWorqr6CVe32mSZcz6kz75DRP\no9G4s/7Y7XZxeXmJo6Mj2O12DIdDuN1uTdR5pg2r1YpAIACDwSBevKurqzg6OsL79+/x/v17XF5e\nfvH3sL5rNBpFipbP5/Hx40e8fPlSTNnnBQwPZfOq0+ng6upqpH6tDlLQ19npdIq8KxgMSpzRfRFA\n19fXYslpsVhwc3ODfD4vHg5sqF1eXsJmsyEWi030SdYqaA7Ubrdl1d9oNNBqtSQ8VWtTqTNNure3\nt+J8pYbPsYM7TrqdTkdI12azwe12IxaLPdK7n22QDJ1OJ0KhEFZWVnBzc4Pd3V0An1e8DyFdbnM7\nnQ7q9Try+TxsNhvi8bjEl88TrFareE10u92Rhg5XXR6PB9FoVEaHmSrMMkwoFJLa7n27ADXbjoS7\nv7+PdrstWnVKyQaDAdbW1n5orNAsgM0zOorV63UxCSLZ6qT7gzE+9KCako+D5Qhuj9vt9jfZ8D0F\ncLJq3PTZYrHg/PxcuvC88SnUV70EOJjCLZ+KZrOJdDqNTCYjhjEulwsOh2Nqn0Xzr08AABB5SURB\nVPFnwOl0IhKJIJPJjKgQeD4XFhYQj8clip4rW3WVGwwGR0j6LoyPFu/u7sLr9cJischKF/g36QOA\n1JbnCf1+H51OR5qIquyOaco0uNGKUmmm3yVPKgBUq1WZre71enfWHRcWFqQzzBl5rXwRswKPx4Ot\nrS0MBgNks1lUq1UJs+RWV7UlnDQ6fHNzg1wuhz/++AOlUgkbGxvIZrNIpVJT/kQ/Fn6/H5lMBiaT\nCclkUspeKokGg0FREfBhw4NOYQ+tQT517TntHFli4ESeqlRKpVKaaprPNCPR8clms+Hq6grLy8uI\nx+NSt6KpNGE0GmG326WmpoXY9VmD1+vF1tYWlpaWUCwWcXp6itPTUxQKBTHLKZfLMBgMd65wiXa7\njVwuJ+Wem5sbLC4uap50fT6fECt146xpkxz50KdrGRtr/JPX5JfIUyXYeTLl/xrQzpGky2k+1XRe\nJ90fiIWFBdjtdthsNmk8+P1+eDweNBoN8Raly9Li4iKCwSAikQjC4bAm4mJmDTSsicViiMfjMr0W\nDAalrl4sFuF2u+FwOFCpVEQuxaQJbn+Z7FGpVBCPx/Hs2TMkk0mpZWptis1gMMDpdIp3Kx863W53\nhBBtNptct18LNXadpQW1cTbuZMZsPK/XK7FO8wTWdNlAY4wSF2R0XPN4PJr57DNNusRdT3yCDSGu\nop49e4b19XXNPf1mEWoum9vtllVdvV5HqVSSdOHLy0sxzqGIX0207fV6yOfz2NnZgdVqlTKRFvWk\n6lbfbDbDYDBI8gSvS3U1+7VQpzDVSUFqqOnzy16Fy+USi81oNDp3Sh2mMdPkptFooNfryeQdyzaz\nGLU+CZogXeKubRWlT6lUCuvr63Kwe66T7rfDYrGIR0M4HJbVVqvVkk5yoVDAwcEBDg4OcHJyAqPR\nKE5chEq6g8EAz58/h8vl0jTp8u8mk2lis/dboK5w2+226NFV0u31erLadblciEajWF9fn0vSVaPW\n8/k86vU6er2e5KBx9JeZaFrATJPuXUmo43UtjrY6nU54vV4sLi4iFAqJjvIp1b8IGgPx5qW4XCUH\nVcQ/iSBokDN+I9NM+vr6GsViEVarVX5Hv9+XoQGCqQm0NPT5fEgkEuL6rxLZrGP82vteqN8JPUYY\nEsr6eblcxvn5OWq1GrrdrsSym81m8ZFdWVlBJBLRRAT510B9yNdqNclE433P61O9BmcdM026D4Ea\nxex2u5FKpUYuzKeIcYMUymzozQp8rt36fD74fL6v3g3QZGQ4HMLv92N5eRn9fh9Go1Hy61QMBgNc\nX1+jVCrBYrGgVCqhVquh2WyKQ5lWbpifAbWkUCwWxUWLZZtyuYyjoyMUCgX0+30xFzKZTGJpyPr7\nU/DVBUYXW/pKd8rodruo1Wro9/twu92oVqtPPkGCW1OmFtD/VVV6cBKKqalfA4PBIDe9wWDA7e0t\nbDYbTCYTCoUCcrncyM9zao0Np2KxiGq1imazKTlfWmmC/Ayou5JisYj379/j3bt3I/VySvSoT7Va\nrWLeHY1GkUgkpLn5FEC3NpLufZrnWYPmSVediLq8vESz2RQtH/A0ywuclKpWqyiVSigUCigWi+j1\neiLm5zZfXf0+FGrN0mg0ioSn3W6La78KegXQ/5QJuO12W2wKnxLuKilwR/Lx40fs7e3h7du3I+UF\nnnNG3nPKbXl5ecRHVmuKkLswfn74UAI+l7yoDmE4p9a8PTRPuhyHBCCSpa8xh55HtFot5PN58Rtm\nE8JsNiMUCs2dKYoWQSLp9/vI5/Oih97Z2cHh4aEsILhrM5vN4pebTqexurqK1dVVZLNZxGIxWCwW\nkVDOA3gPU6fLCTyn0ykj1l6vV5M7pLkhXX45OulC4ox2d3dxfHyM8/NznJ+fw+PxYDgcwuv1PvZb\nfPJQ49ULhQJ2dnbw7t07nJ6e4tOnTyiVSlKXHw6HI7aQqVQKr169wm+//YZAIIBAICB18XnZ2Y1n\nIbI0xsaZTrqPDFVQ/lQJV40zajabyOfzEvmdz+dxcXEhKwR2vn+ESchgMEC320Wr1ZKV2az5XYwr\nOThsQPUED5ZevnaLPn7NTVKPqNE99K7o9Xri7PbmzRtUq1VUKhU0Gg0pJzANhQS7traGra0tvHz5\ncmTSbV6glhRopclkZJJuKBTS7PCT5kmXWkmTyQSr1QqTyTQ3W6yvQb/fl6mwWq2GUqmEi4sLiapm\n11sNpqQPwPeAdprHx8fY29tDPp+fSXtBNhapomCTihN4drsd4XBYphm/BSQLet12Oh2Rf7VaLWmG\nNRoNId5+v4+9vT3kcjlJn+BW2uVyiUUkTXTi8Tiy2SwikYg8OOfteleTkBnPUy6XUa/XZdLP6XRq\nVvWiedKl3tRqtUoqwrxssb4GXBFQ08j8slKphG63i36/L2nAwWDwu9KAVfR6PVxeXuLw8FDSJGbR\nXpB+rOVyGR8+fMD+/j5yuZzI5vx+P7LZLCwWyzeRrir7Up3uuHKtVCooFArI5/Mi/eLBh0C5XJbV\nr8FggMvlQiQSQSwWw/r6ukxb0rGM1/q8Xe8sK1DyqJIuI7ioutGKYkGF5kl3YWEBTqdTYpgdDoc4\ni83bxXgfuDrgVr9er8uFShgMBiwsLIgFocFgkG3vl8xUVFLhtu/29haVSgVnZ2cylVYsFv9DupSY\n0R9D1VVOqw5JRUe5XMbx8TH+97//4c2bN+IGFg6H4XA4EI1GH1weUa0u1YNEW61WUSwWUSqVxDyI\nGlyWHobD4Yh3BfWnXHmnUimsrKxgY2MDm5ub2NjYGLGTnEfQzvH6+hqNRgONRgP1el2uK9a2bTab\nvtKdFtTarcPhQDweRyqVwubmJhKJBJxOpya/jO8B88+Az+fkrq2XaoQ9GAwQi8UwGAzg8XjEG3bS\nyoGNSpI6DeXZcT86OpIxzXFPV6vVikQigWQyiUwmg19//RXxeHyqM/OqcQxrhCw3sLyguoY9BGqG\nGSVxnU5nxHCfpEG9dKVSEfLgQ45ptiz/UHuayWSwsrKCTCaDpaUlWd1qybD7W9DpdFCpVFAsFnF2\ndiYxUGp92+fzjSywtATtvWMFw+EQdrsdy8vLePHiBba2toR05/mivAv0EmaX22Kx/OccjJMuzx/9\nA+4TmKsNM05I8Tg4OMDh4SHy+by4bqmwWCxIJpN4/fo1Xrx4gUQigeXlZRG1T+PGUUNO6Q1B0qVH\nB816HhLOCWBE6qWGVKqTZDwffE3WetV8L7vdLrFSaqIvA1bT6bSQMce25/n6VqdMT09PZfyZAyEs\nB+mkOwXc5TRmtVoRDofF6CYYDMJut8/1RXkXeANzSoeR6HRlYn2sWCxK+i+F5re3t9IVntS5Z0JB\ns9lEoVDA/v4+dnd3cXBwIA5QtVpt5P9QFeBwOLC8vIxffvkFr1+/hsvlmvr0lFo+Ge+KN5tNmM1m\nVCoVaUA+BMfHx/LAYUAlSxh0XVNfX1VK8CFJwyYedGCLxWJIJBJyzDtUBUin00G1WhWN+dXVlVhm\nsoxI0tVruj8RatKqWgt8auT6EPj9fmxsbKBer2N/f19qib1eT6RItA7M5/Pw+XxCAJNWDiQpOl+d\nnZ3h/PwchULhzpICa+0Us3+Lx8OPhNVqhdfrRbfbhd/vl7ryYDBAq9XCcDhELpdDv9//j3cEMe7n\nUSgUZNqPU2UkcZ4PruRNJtOIUoITZfQrvutgj+IpgTVuOtjVajUYjUYEAgGEQiFEIhEEg0FNhxRo\njnR5EatbLJ14R+Hz+bCxsSEpv3/99RcuLy9HYuuvrq5kxcqmFr0U7gIbdWpmVbPZRKvVurOkQNIN\nBoOIRqOS7/VYsNls8Hq9MBgMI8284XAokq7b21uUSiUJ5/wSVDkYG4usG/N8cDdB0qdaIpFIIJVK\nIZlMwuPxwOfzSUoKf/5bjdC1CvZqVNKtVqswGAyyCwiHwwgEAvB6vfcuEmYZmnnHqqvQpEaRjs/w\neDxYWVlBOByGxWJBtVrFycmJeLEyXflngA9DEn4sFkMqlUIwGJQG52PUJC0WC9xutygo/H4/vF7v\niAvb1dWVBHIS44MPLOOoN/tdn4UKAzbFqI9mMCXLYWtra1LPdbvdP+fDawCqMqbT6aDRaKBYLKLR\naMiDKh6PIxKJYHFxEW63+7t8ix8TmiFdh8OBWCwmY5HNZhOnp6eP/bZmElQyDIdDxONx/P7777Db\n7fjw4QOOjo5wfHws0eE/+nX5UAwEAshms8hms1hdXZUb5rFs+FiaAiA9gHq9jmKxiHw+L4ZANGqf\nBJ/PJ+m+3Hndd+OT4EkUJFhOB1L6pMXJqh8JdSCi3W6LXKzX68Hj8SCTyWBtbQ2RSAR2u13T5UXN\nkC5zuxwOB/r9Pk5PT5/U1utrQNI1mUySoJFOp/HPP//gzz//RKlU+mmkyxVdPB7HxsYGXr16hWfP\nngnZsOM87W0hZVZGo1FIFwByuRyMRqOsdnnzT4LP5xPDGTV6fRKi0Sii0SgikYholdns5PEY52PW\noOrMb25uhHTpFbKysoLV1VWEw+ER0tUiNPNN2+12WK1WBINBtFotvH37FhaLRUYmdfwLVfrlcDiw\ntLQE4HPZoVQqYW9vD81mU7bOqh/A+HZ6PPr7rtUFf8bhcCAYDCIWi2FlZQXZbBbb29vY2NiYwqee\nDDXGnEm+/X4fLpcLJpNJaojX19df1OkuLS1hbW0NL168EG3zfbXqeDwuKdY6JoMTaKyRM+nbarXC\n7/cjlUohnU5reiiC0Azp6vh+LC4u4vnz5+h0Osjn87Kdpjzn7OxshHBMJpOUC9h1H98Kq4nNbrcb\n4XAYoVAIsVgMmUwGHo/nMT7qvWBTDfgsheMuihpaNd9tHIz8TiaTskKdVCph0+6pKRC+Be12G8Vi\nURQhNpsN6+vrcLvdSCaT8Pv9sNvt98ZLaQU66T4h+P1+PH/+HKFQSPKmbm5ucHx8jL///lsidAiz\n2Qy32y0GOey+q5EwFotlpCs/LnuaxeaQ1WqVz8GdwObm5ogJzTgoF/N4PGJC85B8t6emQPhWtNtt\nlEolHB4eolgsCukGAgEkk0l5eOmk+0igDR+nqVQDZ63WeaYBEuPm5iba7TaazSaurq6ws7ODRqOB\njx8/jgRKut1uKRdEo1GZlFKJ1Gazyb+zWeRyuWY2q4vKChKhbug+G+h0OqjVajg/P0er1YLH48HS\n0hIikQgSiYQ8JLUUYjoJmiJdnnCfz4ft7W202230+31sbm4imUxqeh572mCzjR4Mr169gslkQqlU\nkp+h7IvyKo/HI1pSwmKxyL9rLSBQx+yA6iRqnFnO4u7JarVqVq0wDk2xE0+43+/H9vY2AoEABoOB\naB85xqrf9F8GE32NRiNisZgoHVSHMAr7VcE+AykJkje78vfVOHXomASSrtPpFO9nRhSpXiLzQLqG\n+5IWDAbD8KkmMfwMGAyGJ5ts8TOgn88fB/1c/lj8//m88wmh7eKIDh06dGgMOunq0KFDxxShk64O\nHTp0TBE66erQoUPHFKGTrg4dOnRMETrp6tChQ8cUoZOuDh06dEwRX9TpTvG96NChQ8fcYJJO917S\n1aFDhw4dPxZ6eUGHDh06pgiddHXo0KFjitBJV4cOHTqmCJ10dejQoWOK0ElXhw4dOqaI/wO2wWdO\n2Egb3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b6437c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "show_digit_imgs(digits, size=4);\n",
    "# show_digit_imgs(preprocessed_digits, size=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если вам кажется это целесообразным, вы можете сделать каждый признак булевым (все пиксели - черные или белые). Это можно сделать, наприпмер, так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bool_digits = [(digit > 50).astype(bool) for digit in digits]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Работа с различными моделями (до трех баллов за анализ каждой модели)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разобьем нашу выборку на две части, тренировочную и тестовую. На первой выборке (или ее части) будем обучать модель, на второй части будем смотреть качество модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_input, test_input, train_output, test_output = model_selection.train_test_split(digits, labels,\n",
    "                                                                      test_size=0.5, train_size=0.5,\n",
    "                                                                      random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию, позволяющую запустить модель на тренировочной и тестовой выборках и выводящая результат работы этой модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_model(model, train_input, train_output, test_input, test_output):\n",
    "    # выводим информацию о модели\n",
    "    print(model, '\\n')\n",
    "    \n",
    "    # обучаем модель\n",
    "    model.fit(train_input, train_output)\n",
    "    \n",
    "    # размечаем с ее помощью тестовую выборку\n",
    "    predicted_output = model.predict(test_input)\n",
    "    \n",
    "    # распечатываем результаты\n",
    "    print(metrics.classification_report(test_output, predicted_output))\n",
    "    print(metrics.confusion_matrix(test_output, predicted_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Приведем пример запуска этой функции."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=3, p=1,\n",
      "           weights='uniform'), '\\n')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.95      0.90      2052\n",
      "          1       0.67      1.00      0.80      2330\n",
      "          2       0.88      0.77      0.82      2096\n",
      "          3       0.68      0.78      0.72      2222\n",
      "          4       0.82      0.63      0.71      2053\n",
      "          5       0.86      0.58      0.69      1833\n",
      "          6       0.91      0.93      0.92      2079\n",
      "          7       0.86      0.86      0.86      2191\n",
      "          8       0.91      0.45      0.60      2062\n",
      "          9       0.64      0.83      0.72      2082\n",
      "\n",
      "avg / total       0.80      0.78      0.78     21000\n",
      "\n",
      "[[1947    6    7    3    7   14   47    8    3   10]\n",
      " [   0 2319    3    2    0    0    2    0    4    0]\n",
      " [  62  233 1611   38   42    5   20   67    9    9]\n",
      " [  27  209   65 1723    3   57   10   34   45   49]\n",
      " [  11  127   17    0 1291    7   33   23    0  544]\n",
      " [  74   87   13  369   28 1065   57   18   30   92]\n",
      " [  36   57   10    2   12   24 1930    3    1    4]\n",
      " [   4  128   11    1   31    0    0 1882    0  134]\n",
      " [ 112  256   81  378   38   69   27   51  929  121]\n",
      " [  25   48   21   25  123    3    3  108    5 1721]]\n"
     ]
    }
   ],
   "source": [
    "# в качестве модели берем классификатор по ближайшим соседям, устанавливаем число соседей, равное трем.\n",
    "model = KNeighborsClassifier(n_neighbors=3, p=1)\n",
    "\n",
    "# берем только размер обучающей выборки равный 300, для того, чтобы модель работала не слишком долго\n",
    "run_model(model, train_input[:300], train_output[:300], test_input, test_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что у нас означают эти таблички?\n",
    "\n",
    "Вначале напечатано название модели и параметры, с которыми она была запущена. Затем вывелась табличка, где в первом столбцы указаны метки классов (названия цифр). Далее идут столбцы:\n",
    "\n",
    "`precision` - доля предсказаний, которые указали правильную метку (подробнее про этот и следующий параметры можно почитать в [википедии](https://en.wikipedia.org/wiki/Precision_and_recall));\n",
    "\n",
    "`recall` - доля элементов класса, для которых предсказание дало правильную метку;\n",
    "\n",
    "`f1-score` - общая мера точности модели для данного класса, см. [вики](https://en.wikipedia.org/wiki/F1_score);\n",
    "\n",
    "`support` - количество вхождений каждого класса в `control_output`.\n",
    "\n",
    "Далее выведена [confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также вы можете замерять время работы вашей модели. Для этого надо немного видоизменить код. Теперь внизу еще будет выводиться время работы вашей модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=3, p=1,\n",
      "           weights='uniform'), '\\n')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.97      0.92      2052\n",
      "          1       0.73      1.00      0.84      2330\n",
      "          2       0.92      0.78      0.84      2096\n",
      "          3       0.84      0.83      0.83      2222\n",
      "          4       0.88      0.82      0.85      2053\n",
      "          5       0.86      0.81      0.83      1833\n",
      "          6       0.96      0.93      0.95      2079\n",
      "          7       0.86      0.90      0.88      2191\n",
      "          8       0.97      0.66      0.78      2062\n",
      "          9       0.79      0.85      0.82      2082\n",
      "\n",
      "avg / total       0.87      0.86      0.86     21000\n",
      "\n",
      "[[1996    7   11    2    4   11   15    1    1    4]\n",
      " [   0 2320    5    2    0    0    0    1    0    2]\n",
      " [  71  217 1635   20   23    3   12   88   10   17]\n",
      " [  25  119   48 1838    1   93    5   34   20   39]\n",
      " [   5   91    0    1 1688    2    7   17    0  242]\n",
      " [  45   53   13  157   14 1480   28    9    5   29]\n",
      " [  55   46    7    0   13   19 1934    4    0    1]\n",
      " [   4   87    9    1   37    2    0 1979    0   72]\n",
      " [  61  218   44  139   28  108   11   42 1354   57]\n",
      " [  14   27   12   23  104    6    2  125    3 1766]]\n",
      "\n",
      "time: 26.8343348503\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3, p=1)\n",
    "\n",
    "time = timeit.timeit(lambda: run_model(model,\n",
    "                                       train_input[:1000], train_output[:1000],\n",
    "                                       test_input, test_output), number=1)\n",
    "print('\\ntime: {}'.format(time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process PoolWorker-10:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-13:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-14:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-15:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-16:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-17:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-18:\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 276, in _bootstrap\n",
      "    traceback.print_exc()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 233, in print_exc\n",
      "    print_exception(etype, value, tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 124, in print_exception\n",
      "    _print(file, 'Traceback (most recent call last):')\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 13, in _print\n",
      "    file.write(str+terminator)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Process PoolWorker-19:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 276, in _bootstrap\n",
      "    traceback.print_exc()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 233, in print_exc\n",
      "    print_exception(etype, value, tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 125, in print_exception\n",
      "    print_tb(tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 67, in print_tb\n",
      "    '  File \"%s\", line %d, in %s' % (filename, lineno, name))\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 13, in _print\n",
      "    file.write(str+terminator)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Process PoolWorker-20:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-21:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 276, in _bootstrap\n",
      "    traceback.print_exc()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 233, in print_exc\n",
      "    print_exception(etype, value, tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 125, in print_exception\n",
      "    print_tb(tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 70, in print_tb\n",
      "    if line: _print(file, '    ' + line.strip())\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 13, in _print\n",
      "    file.write(str+terminator)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Process PoolWorker-22:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-23:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 274, in _bootstrap\n",
      "    sys.stderr.write('Process %s:\\n' % self.name)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Process PoolWorker-25:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-26:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 274, in _bootstrap\n",
      "    sys.stderr.write('Process %s:\\n' % self.name)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Process PoolWorker-28:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "KeyboardInterrupt\n",
      "Process PoolWorker-29:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
      "    task = get()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py\", line 362, in get\n",
      "    return recv()\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 276, in _bootstrap\n",
      "    traceback.print_exc()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 233, in print_exc\n",
      "    print_exception(etype, value, tb, limit, file)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 128, in print_exception\n",
      "    _print(file, line, '')\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/traceback.py\", line 13, in _print\n",
      "    file.write(str+terminator)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "\n",
      "Exception in thread Thread-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 801, in __bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py\", line 754, in run\n",
      "    self.__target(*self.__args, **self.__kwargs)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 326, in _handle_workers\n",
      "    pool._maintain_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 230, in _maintain_pool\n",
      "    self._repopulate_pool()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/pool.py\", line 223, in _repopulate_pool\n",
      "    w.start()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 130, in start\n",
      "    self._popen = Popen(self)\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/forking.py\", line 126, in __init__\n",
      "    code = process_obj._bootstrap()\n",
      "  File \"/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/multiprocessing/process.py\", line 274, in _bootstrap\n",
      "    sys.stderr.write('Process %s:\\n' % self.name)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 377, in write\n",
      "    self.flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 324, in flush\n",
      "    self._flush()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 341, in _flush\n",
      "    parent=self.parent_header, ident=self.topic)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/jupyter_client/session.py\", line 684, in send\n",
      "    stream.send_multipart(to_send, copy=copy)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in send_multipart\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 192, in schedule\n",
      "    f()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 199, in <lambda>\n",
      "    self.schedule(lambda : self._really_send(*args, **kwargs))\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 212, in _really_send\n",
      "    ctx, pipe_out = self._setup_pipe_out()\n",
      "  File \"/usr/local/lib/python2.7/site-packages/ipykernel/iostream.py\", line 143, in _setup_pipe_out\n",
      "    pipe_out = ctx.socket(zmq.PUSH)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/context.py\", line 146, in socket\n",
      "    s = self._socket_class(self, socket_type, **kwargs)\n",
      "  File \"/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.py\", line 61, in __init__\n",
      "    def __init__(self, *a, **kw):\n",
      "KeyboardInterrupt\n",
      "Unhandled exception in thread started by <bound method Thread.__bootstrap of <Thread(Thread-11, stopped daemon 123145330761728)>>\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36m__bootstrap\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    772\u001b[0m         \u001b[0;31m# if a non-daemonic encounters this, something else is wrong.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__bootstrap_inner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__daemonic\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_sys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36m__bootstrap_inner\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    812\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0m_sys\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m                     print>>_sys.stderr, (\"Exception in thread %s:\\n%s\" %\n\u001b[0;32m--> 814\u001b[0;31m                                          (self.name, _format_exc()))\n\u001b[0m\u001b[1;32m    815\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__stderr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m                     \u001b[0;31m# Do the best job possible w/o a huge amt. of code to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m    375\u001b[0m                 \u001b[0;31m# and this helps.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m'\\n'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_schedule_flush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_flush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36m_flush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34mu'name'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'text'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             self.session.send(self.pub_thread, u'stream', content=content,\n\u001b[0;32m--> 341\u001b[0;31m                 parent=self.parent_header, ident=self.topic)\n\u001b[0m\u001b[1;32m    342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0misatty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/jupyter_client/session.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, stream, msg_or_type, content, parent, ident, buffers, track, header, metadata)\u001b[0m\n\u001b[1;32m    682\u001b[0m             \u001b[0;31m# use dummy tracker, which will be done immediately\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m             \u001b[0mtracker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDONE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 684\u001b[0;31m             \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_send\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36msend_multipart\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0mIf\u001b[0m \u001b[0mmy\u001b[0m \u001b[0mthread\u001b[0m \u001b[0misn\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mt\u001b[0m \u001b[0mrunning\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mforked\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msend\u001b[0m \u001b[0mimmediately\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \"\"\"\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_really_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_really_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mschedule\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    190\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msend_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0mIf\u001b[0m \u001b[0mmy\u001b[0m \u001b[0mthread\u001b[0m \u001b[0misn\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mt\u001b[0m \u001b[0mrunning\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mforked\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msend\u001b[0m \u001b[0mimmediately\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \"\"\"\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_really_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_really_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36m_really_send\u001b[0;34m(self, msg, *args, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0;31m# new context/socket for every pipe-out\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0;31m# since forks don't teardown politely, use ctx.term to ensure send has completed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m             \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpipe_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_pipe_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m             \u001b[0mpipe_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pipe_uuid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m             \u001b[0mpipe_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36m_setup_pipe_out\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# must be new context after fork\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mpipe_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPUSH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m         \u001b[0mpipe_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3000\u001b[0m \u001b[0;31m# 3s timeout for pipe_out sends before discarding the message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0mpipe_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tcp://127.0.0.1:%i\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pipe_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/zmq/sugar/context.pyc\u001b[0m in \u001b[0;36msocket\u001b[0;34m(self, socket_type, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mZMQError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mENOTSUP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_socket_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msockopts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/zmq/sugar/socket.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *a, **kw)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0m_monitor_socket\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSocket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'shadow'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "params = {'penalty':['l2', 'l1'], 'C': np.logspace(0.00001, 100, num=10)}\n",
    "clf = GridSearchCV(LogisticRegression(), params, n_jobs=-1)\n",
    "clf.fit(train_input[:1000], train_output[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "means = filter(lambda x: x[0]['p'] == 2 and x[0]['weights'] == 'distance', clf.grid_scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f138d90>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kn08kFccBh1ZMKhJGAv8DtK7wglANSVb5SGr69NDbYOJEJRWFZNAg+PLL8Oir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\n4tDFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100fb950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([x[0]['n_neighbors'] for x in means], [x[1] for x in means])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=4, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=4, n_jobs=-1, p=2)\n",
    "clf.fit(train_input[:100], train_output[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = clf.predict(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions_df = pd.DataFrame(predictions, columns=['Label'])\n",
    "predictions_df.index.name = 'ImageId'\n",
    "predictions_df.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': (0.1, 0.03, 0.01)}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = model_selection.GridSearchCV(LogisticRegression(), {'C': (0.1, 0.03, 0.01)})\n",
    "clf.fit(train_input, train_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90785714285714281"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(test_output, clf.predict(test_input), average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean_fit_time',\n",
       " 'mean_score_time',\n",
       " 'mean_test_score',\n",
       " 'mean_train_score',\n",
       " 'param_C',\n",
       " 'param_penalty',\n",
       " 'params',\n",
       " 'rank_test_score',\n",
       " 'split0_test_score',\n",
       " 'split0_train_score',\n",
       " 'split1_test_score',\n",
       " 'split1_train_score',\n",
       " 'split2_test_score',\n",
       " 'split2_train_score',\n",
       " 'std_fit_time',\n",
       " 'std_score_time',\n",
       " 'std_test_score',\n",
       " 'std_train_score']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# NB!!!\n",
    "# тут важное: вместо np.linspace(0.00001, 1, num=1000) я взял [10**i for i in range(-10, 2)]\n",
    "# тут не 1000 вариантов, а 12, но они охватывают широкий диапазон параметра C\n",
    "# можешь в пустой ячейке выполнить оба выражения и посмотреть, что они дают\n",
    "parameters = {'C':[10**i for i in range(-10, 2)],'penalty':('l1', 'l2')}\n",
    "svr = LogisticRegression()\n",
    "clf = GridSearchCV(svr, parameters, n_jobs=-1)\n",
    "clf.fit(train_input[:5000], train_output[:5000])\n",
    "sorted(clf.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TiIiILJsKiwI2YAAsWACXXho7iYiIyPJRYVGg5s6F/v2hQwdo2jR2GhERkeWj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\nWIiIiEhi/h+4Y71kjhoALgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d71a750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NB!!!\n",
    "# я научился по-другому немного работать с результатами работы GridSearchCV\n",
    "\n",
    "# первое: я беру маску для параметров, где параметр penalty был равен 'l2'\n",
    "# это даёт массив с True на тех местах, где параметр был l2 и False там, где не был\n",
    "mask = clf.cv_results_['param_penalty'] == 'l2'\n",
    "\n",
    "# второе: я по этой маске достаю значения параметра C и сохраняю в переменную\n",
    "param_C = list(clf.cv_results_['param_C'][mask])\n",
    "\n",
    "# третье: по этой же маске я достаю качество на тестовой выборке\n",
    "score = list(clf.cv_results_['mean_test_score'][mask])\n",
    "\n",
    "# строю график\n",
    "plt.plot(param_C, score)\n",
    "\n",
    "# и, оч важно для наглядности: ставлю ось x в логарифмическую шкалу\n",
    "# так видны изменения качества при небольших изменениях параметра C\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clf.cv_results_['mean_test_score'][mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'C': 1e-10, 'penalty': 'l1'},\n",
       " {'C': 1e-10, 'penalty': 'l2'},\n",
       " {'C': 1e-09, 'penalty': 'l1'},\n",
       " {'C': 1e-09, 'penalty': 'l2'},\n",
       " {'C': 1e-08, 'penalty': 'l1'},\n",
       " {'C': 1e-08, 'penalty': 'l2'},\n",
       " {'C': 1e-07, 'penalty': 'l1'},\n",
       " {'C': 1e-07, 'penalty': 'l2'},\n",
       " {'C': 1e-06, 'penalty': 'l1'},\n",
       " {'C': 1e-06, 'penalty': 'l2'},\n",
       " {'C': 1e-05, 'penalty': 'l1'},\n",
       " {'C': 1e-05, 'penalty': 'l2'},\n",
       " {'C': 0.0001, 'penalty': 'l1'},\n",
       " {'C': 0.0001, 'penalty': 'l2'},\n",
       " {'C': 0.001, 'penalty': 'l1'},\n",
       " {'C': 0.001, 'penalty': 'l2'},\n",
       " {'C': 0.01, 'penalty': 'l1'},\n",
       " {'C': 0.01, 'penalty': 'l2'},\n",
       " {'C': 0.1, 'penalty': 'l1'},\n",
       " {'C': 0.1, 'penalty': 'l2'},\n",
       " {'C': 1, 'penalty': 'l1'},\n",
       " {'C': 1, 'penalty': 'l2'},\n",
       " {'C': 10, 'penalty': 'l1'},\n",
       " {'C': 10, 'penalty': 'l2'})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.cv_results_['params']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуйте поэкспериментировать на моделях: `LogisticRegression`, `GaussianNB`, `KNeighborsClassifier`, `DecisionTreeClassifier`, `RandomForestClassifier`, `MLPClassifier` запуская эти модели на разных параметрах.\n",
    "\n",
    "Так как в `Jupyter Notebook` не очень удобно работать с таблицами, то скопируйте вот [эту таблицу](https://docs.google.com/spreadsheets/d/1FZq-tlRWPshldQV7-W3njIHPHoaVhCHaYG8n829Dut4/edit?usp=sharing) и заполните ее.\n",
    "\n",
    "**По окончанию выберите 5 наилучших результатов и для каждого их них сделайте submission в `kaggle`.** Для этого надо запустить вашу модель на данных из `test.csv`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отчет должен содержать вышеуказанную табличку, выводы о том, почему некоторые модели справляются с данной задачей классификации лучше, почему некоторые модели долго работают. Также нужно обосновать выбор ключевых параметров модели. Не забудьте указать результат 5 лучших классификаторов.\n",
    "\n",
    "Баллы за коллективные работы будут снижаться пропорционально количеству участников!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}