Chili-clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Латентно семантический анализ\n",
    "\n",
    "\n",
    "Прежде всего нужна матрица TDM. Принято, что строки  в  матрице TDM с соответствуют  словам, а столбцы — документам. В каждой ячейке матрицы указано какое количество раз слово встречается в соответствующем документе. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import re\n",
    "from pymystem3 import Mystem\n",
    "from yandex_translate import YandexTranslate\n",
    "translate = YandexTranslate('trnsl.1.1.20171021T160722Z.738acb6379ef8012.5214c13767962890654e18a0d677c343d03bba24')\n",
    "import nltk\n",
    "from nltk.corpus import wordnet as wn\n",
    "import numpy as np\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Загружаем русский датасет\n",
    "text_file = open(\"OpenData_MinFin_Russia.csv\", \"r\")\n",
    "lines = text_file.read().split('==Q==')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Загружаем англ датасет\n",
    "text_file = open(\"UK.csv\", \"r\")\n",
    "uk = text_file.read().split('==Q==')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    foo\n",
       "1    fuz\n",
       "2    NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def d(l):\n",
    "    if l =='foo':\n",
    "        return \"fuz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-aad837c8ce17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'abc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'zzz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'abcd'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   2175\u001b[0m             \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2176\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2177\u001b[0;31m             \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m         return self._constructor(new_values,\n",
      "\u001b[0;32mpandas/src/inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:66124)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable"
     ]
    }
   ],
   "source": [
    "s.map(re.sub(r'abc', 'zzz', 'abcd') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        zz\n",
       "1       fuz\n",
       "2    np.nan\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(['foo', 'fuz', 'np.nan'])#.str.replace(regex_pat, 'bar')\n",
    "s.map(lambda x: re.sub('foo', 'zz', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fuz'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "uk = pd.Series(uk)\n",
    "uk = uk.map(lambda x: re.sub('\\s+', ' ', x))\n",
    "uk = uk.map(lambda x: re.sub('[^A-Za-zА-Яа-я]', ' ', x))\n",
    "\n",
    "uk = uk.str.lower()\n",
    "uk.to_csv('uk2.csv', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Загружаем чилийский датасет\n",
    "\n",
    "text_file = open(\"chile.csv\", \"r\")\n",
    "chile = text_file.read().split('==Q==')\n",
    "chile = pd.Series(chile)\n",
    "chile = chile.map(lambda x: re.sub('\\s+', ' ', x))\n",
    "chile = chile.map(lambda x: re.sub('[^A-Za-zА-Яа-я]', ' ', x))\n",
    "\n",
    "chile = chile.str.lower()\n",
    "chile.to_csv('chile2.csv', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Берем заранее лематизированные данные с пощью Mystem - я это делал на своем компьютере\n",
    "ru = pd.Series.empty\n",
    "ru = pd.Series.from_csv('ru2.csv')\n",
    "ru = ru[:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Переводим их на англ.\n",
    "for i in range(len(ru)):\n",
    "    ru[i] = translate.translate(ru[i], 'en')['text'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ru.to_csv('ru_translate.csv', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#СТАРТ\n",
    "ru = chile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Очищаем от запятых\n",
    "ru=ru.map(lambda x: re.sub('\\s+', ' ', x))\n",
    "ru = ru.map(lambda x: re.sub('[^A-Za-zА-Яа-я]', ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Удаляем стопворды\n",
    "text_file = open(\"stop_words.txt\", \"r\")\n",
    "lines = text_file.read().split('\\n')\n",
    "stopwords = lines[1:-2]\n",
    "stopwords = set(stopwords)\n",
    "\n",
    "def replace_stopwords(str_src, stopwords):\n",
    "    for w in stopwords:\n",
    "        str_src = str_src.replace(\" \" + w + \" \", \" \")\n",
    "        str_src = str_src.replace(\"  \",\" \")\n",
    "    return str_src\n",
    " \n",
    "# Удаление стоп слов\n",
    "ru =ru.map(lambda x: replace_stopwords(x, stopwords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ru_old=ru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [3, 2, 1, ..., 0, 1, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Делаем веторайзер\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "\n",
    "X = vectorizer.fit_transform(ru)\n",
    "dtm=X.toarray().transpose()\n",
    "vocabulary = vectorizer.get_feature_names()\n",
    "dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#Подсчет частот\n",
    "d = {'vocabulary': vocabulary,'freq': sum(dtm.T),}\n",
    "freq = pd.DataFrame(data=d)\n",
    "freq=freq.sort('freq',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
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       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"b9c83111-5228-479f-a808-7a481fb514bc\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b9c83111-5228-479f-a808-7a481fb514bc' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"b9c83111-5228-479f-a808-7a481fb514bc\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"b9c83111-5228-479f-a808-7a481fb514bc\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"b9c83111-5228-479f-a808-7a481fb514bc\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b9c83111-5228-479f-a808-7a481fb514bc' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"b9c83111-5228-479f-a808-7a481fb514bc\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook, show\n",
    "from bokeh import plotting\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/bokeh/models/sources.py:137: BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('x', 234), ('y', 235)\n",
      "  \"Current lengths: %s\" % \", \".join(sorted(str((k, len(v))) for k, v in data.items())), BokehUserWarning))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.renderers.GlyphRenderer\">GlyphRenderer</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'6665b4fb-1590-44f4-9daa-2fba1dfd01e6', <span id=\"dadfaf82-c813-41cf-85df-bc4c92284ada\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">data_source&nbsp;=&nbsp;ColumnDataSource(id='4cf2bfc2-60d6-4492-ac36-2aafbd62621f', ...),</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">glyph&nbsp;=&nbsp;Circle(id='6d734130-eb09-4b86-b93d-4919d9999dc6', ...),</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hover_glyph&nbsp;=&nbsp;None,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">level&nbsp;=&nbsp;'glyph',</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted&nbsp;=&nbsp;False,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted_glyph&nbsp;=&nbsp;None,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">nonselection_glyph&nbsp;=&nbsp;Circle(id='daa402be-0a90-4c1d-a0d4-2abc2efba6ec', ...),</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">selection_glyph&nbsp;=&nbsp;None,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">view&nbsp;=&nbsp;CDSView(id='a5533dec-78fa-4f9b-bba0-c480ecbbb3fb', ...),</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range_name&nbsp;=&nbsp;'default',</div></div><div class=\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range_name&nbsp;=&nbsp;'default')</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  var expanded = false;\n",
       "  var ellipsis = document.getElementById(\"dadfaf82-c813-41cf-85df-bc4c92284ada\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    var rows = document.getElementsByClassName(\"a7637c68-f1fc-4224-99b2-56900dfa3ce7\");\n",
       "    for (var i = 0; i < rows.length; i++) {\n",
       "      var el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "GlyphRenderer(id='6665b4fb-1590-44f4-9daa-2fba1dfd01e6', ...)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, show\n",
    "\n",
    "\n",
    "# prepare some data\n",
    "y = freq['freq'].tolist()\n",
    "x = range(1,235)\n",
    "#x=[ x/10 for x in range(1,100) ]\n",
    "#y0 = [1/i for i in x]\n",
    "#y1 = [10**i for i in x]\n",
    "#y2 = [10**(i**2) for i in x]\n",
    "\n",
    "# output to static HTML file\n",
    "\n",
    "\n",
    "# create a new plot\n",
    "p = figure(\n",
    "   tools=\"pan,box_zoom,reset,save\",\n",
    "   y_axis_type=\"linear\", y_range=[1,120], title=\"Zipf's law illustration\",\n",
    "   x_axis_label='words', y_axis_label='frequency', plot_width = 800, plot_height = 700\n",
    ")\n",
    "\n",
    "# add some renderers\n",
    "#p.line(x, x, legend=\"y=x\")\n",
    "#p.circle(x, x, legend=\"y=x\", fill_color=\"white\", size=8)\n",
    "p.line(x, y, legend=\"Zipf's law curve\", line_width=3)\n",
    "#p.line(x, y1, legend=\"y=10^x\", line_color=\"red\")\n",
    "p.circle(26, 14, legend=\"optimal point\", fill_color=\"red\", line_color=\"red\", size=7)\n",
    "#p.line(x, y2, legend=\"y=10^x^2\", line_color=\"orange\", line_dash=\"4 4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235,)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq['freq'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"803cb2a9-16c7-4ee7-afe7-db29e9e65e7a\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    var docs_json = {\"d383d2b8-f3a9-4db4-b13e-0280a6876e65\":{\"roots\":{\"references\":[{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2ebe7af0-7b13-4094-8f40-5198326ae551\",\"type\":\"PanTool\"},{\"id\":\"6e6981d7-5b19-445a-b0da-b7ddc6567923\",\"type\":\"BoxZoomTool\"},{\"id\":\"6b8cce97-bc23-468e-bdc7-bf6a995bb6e2\",\"type\":\"ResetTool\"},{\"id\":\"e8d8634e-6686-4887-a03d-f1e5b8459029\",\"type\":\"SaveTool\"}]},\"id\":\"e28bfc0e-d897-453b-b4d5-3c56751a0f40\",\"type\":\"Toolbar\"},{\"attributes\":{\"source\":{\"id\":\"e5fc6fff-58eb-475f-98f6-6e1e7a837bc1\",\"type\":\"ColumnDataSource\"}},\"id\":\"071def9f-e84e-4d5c-823a-a48e0952195d\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"4cf2bfc2-60d6-4492-ac36-2aafbd62621f\",\"type\":\"ColumnDataSource\"}},\"id\":\"a5533dec-78fa-4f9b-bba0-c480ecbbb3fb\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"4cf2bfc2-60d6-4492-ac36-2aafbd62621f\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6d734130-eb09-4b86-b93d-4919d9999dc6\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"daa402be-0a90-4c1d-a0d4-2abc2efba6ec\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"a5533dec-78fa-4f9b-bba0-c480ecbbb3fb\",\"type\":\"CDSView\"}},\"id\":\"6665b4fb-1590-44f4-9daa-2fba1dfd01e6\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1f761fe2-02f8-4673-bf8c-e2d1125e27c6\",\"type\":\"LinearScale\"},{\"attributes\":{\"overlay\":{\"id\":\"6c97905e-684d-420f-be00-e12cf526bcaf\",\"type\":\"BoxAnnotation\"}},\"id\":\"6e6981d7-5b19-445a-b0da-b7ddc6567923\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"3ff5514a-f150-4968-9641-d9fe74e2aefd\",\"type\":\"Line\"},{\"attributes\":{\"items\":[{\"id\":\"0d5fd5da-103a-4eda-84d4-bde4b07908c9\",\"type\":\"LegendItem\"},{\"id\":\"245c9304-424c-4ee7-9cfa-92894a3ef8aa\",\"type\":\"LegendItem\"}],\"plot\":{\"id\":\"a5a50e9e-7390-45ca-b396-ede6629f808a\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"8174f661-51b9-4ba5-9bb9-3efc91f531bd\",\"type\":\"Legend\"},{\"attributes\":{\"plot\":null,\"text\":\"Zipf's law illustration\"},\"id\":\"7c66ce27-1b9b-4dd0-8453-a29d40a8a5c1\",\"type\":\"Title\"},{\"attributes\":{\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2fcebc13-72df-4c8f-ba68-590b350c4cbd\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"4a3d5260-f880-4dad-91ba-9154051ceee1\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"6b8cce97-bc23-468e-bdc7-bf6a995bb6e2\",\"type\":\"ResetTool\"},{\"attributes\":{\"label\":{\"value\":\"optimal 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law 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Application\",\"version\":\"0.12.10\"}};\n",
       "    var render_items = [{\"docid\":\"d383d2b8-f3a9-4db4-b13e-0280a6876e65\",\"elementid\":\"803cb2a9-16c7-4ee7-afe7-db29e9e65e7a\",\"modelid\":\"a5a50e9e-7390-45ca-b396-ede6629f808a\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "a5a50e9e-7390-45ca-b396-ede6629f808a"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08a0347e10>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NYQbwfIhflmj/h/h9uyW0vSzR/ibgKOfcGOA3GdZ/E3AKvpj+NsP6TwivxVjg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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08a058cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "major_ticks = np.arange(0, 200, 5)                                        \n",
    "minor_ticks = np.arange(0, 200, 5)      \n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.set_xticks(major_ticks)                                                       \n",
    "ax.set_xticks(minor_ticks, minor=True)                                           \n",
    "ax.set_yticks(major_ticks)                                                       \n",
    "ax.set_yticks(minor_ticks, minor=True)                                           \n",
    "\n",
    "# and a corresponding grid                                                       \n",
    "\n",
    "ax.grid(which='both')                                                            \n",
    "\n",
    "# or if you want differnet settings for the grids:                               \n",
    "ax.grid(which='minor', alpha=0.2)                                                \n",
    "ax.grid(which='major', alpha=0.5)                                                \n",
    "\n",
    "\n",
    "#plt.grid(color='b', linestyle='-', linewidth=1, alpha =0.3)\n",
    "plt.plot(freq['freq'].tolist()[10:200])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ux = []\n",
    "uy = []\n",
    "d = []\n",
    "nomer = []\n",
    "y = freq['freq'].tolist()\n",
    "x = range(1,len(freq['freq'])+1)\n",
    "for i in range(len(x)-2):\n",
    "    d.append(2*(x[i]*(y[i+1]-y[i+2]) + x[i+1]*(y[i+2]-y[i])+x[i+2]*(y[i]-y[i+1])))\n",
    "    if d[i] !=0:\n",
    "        ux.append(((x[i]**2+y[i]**2)*(y[i+1]-y[i+2])+(x[i+1]**2+y[i+1]**2)*(y[i+2]-y[i])+(x[i+2]**2+y[i+2])*(y[i]-y[i+1]))/d[i])\n",
    "        uy.append(((x[i]**2+y[i]**2)*(x[i+2]-x[i+1])+(x[i+1]**2+y[i+1]**2)*(x[i]-x[i+2])+(x[i+2]**2+y[i+2])*(x[i+1]-x[i]))/d[i])\n",
    "        nomer.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 'health'], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq.values[26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    },
    {
     "data": {
      "image/png": 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BADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCE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\ney8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089eb7b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = []\n",
    "for i in range(len(nomer)):\n",
    "    r.append(((ux[i]*-x[i])**2+(uy[i]*-y[i])**2)**0.5)\n",
    "    \n",
    "min(r)\n",
    "print(np.argmin(r))\n",
    "\n",
    "plt.scatter(range(1+40,len(r)),r[1-1+40:len(r)-1],s=1)\n",
    "#plt.plot((0, 8), (10, 10), 'k-')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#выбираем нужные слова\n",
    "features =freq.reset_index(drop=True)[70:220]\n",
    "#спиоск фич\n",
    "features_list = features.T.iloc[1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Достаем индексы фич\n",
    "index_features=[]\n",
    "for i, j in enumerate(vocabulary):\n",
    "    if j in features_list:\n",
    "        index_features.append(i)\n",
    "#Матрица финальная\n",
    "dtm2 =dtm.T[:, index_features]\n",
    "dtm2=dtm2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clust= 8 silh= 0.407109784983\n"
     ]
    }
   ],
   "source": [
    "#Кластеризация\n",
    "x=[]\n",
    "y=[]\n",
    "clusters=8\n",
    "kmean = KMeans(n_clusters=clusters)\n",
    "rez=kmean.fit_predict(dtm2) \n",
    "silhouette_avg = silhouette_score(dtm2, rez)\n",
    "x.append(clusters)\n",
    "y.append(silhouette_avg)\n",
    "print('clust=',clusters,'silh=',silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \n",
      "  \n",
      "  \n",
      "  \n",
      "  \n",
      "  \n",
      "  \n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Слова в кластеры\n",
    "\n",
    "d = {'rez': rez, 'vocabulary': features_list}\n",
    "df = pd.DataFrame(data=d)\n",
    "#Слова в кластеры\n",
    "chili_words= []\n",
    "result =  df.groupby('rez')['vocabulary'].apply(lambda x: \"%s\" % ', '.join(x))\n",
    "for i in result:\n",
    "    chili_words.append(len(i.split(', ')))\n",
    "    print('  ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[55, 3, 29, 2, 2, 2, 43, 14]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chili_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Расширяем и доваляем синонинмы\n",
    "result_with_sin= result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [[3,3],[13,31]]\n",
    "a[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result_with_syn = []\n",
    "for i, j in enumerate(result.tolist()):\n",
    "    result_with_syn.append(j)\n",
    "#делаем список списков result_with_syn\n",
    "for i,j in enumerate(result_with_syn):\n",
    "    result_with_syn[i] = j.split(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_syns(word):\n",
    "    ad=[]\n",
    "    for synset in wn.synsets(word):\n",
    "        for lemma in synset.lemmas():\n",
    "            ad.append(lemma.name())\n",
    "    return  ', '.join(list(set(ad)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['budgetary',\n",
       " 'consolidated',\n",
       " 'osmium',\n",
       " 'foreign',\n",
       " 'trade',\n",
       " 'complextype',\n",
       " 'municipality',\n",
       " 'real']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_with_syn[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result_with_syn_2=result_with_syn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open('workfile', 'w')\n",
    "for i in [['k','as'],['k','aas']]:\n",
    "    for a in i:\n",
    "        f.write(a+' ')\n",
    "    f.write('\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.read()\n",
    "'This is the entire file.\\n'\n",
    "f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 budgetary\n",
      "0 1 consolidated\n",
      "0 2 osmium\n",
      "0 3 foreign\n",
      "0 4 trade\n",
      "0 5 complextype\n",
      "0 6 municipality\n",
      "0 7 real\n",
      "1 0 sector\n",
      "1 1 document\n",
      "1 2 accounting\n",
      "1 3 inspection\n",
      "1 4 quot\n",
      "1 5 diamond\n",
      "1 6 external\n",
      "1 7 room\n",
      "1 8 position\n",
      "1 9 enter\n",
      "1 10 wastes\n",
      "1 11 anti\n",
      "1 12 law\n",
      "1 13 fax\n",
      "1 14 standardversion\n",
      "1 15 entered\n",
      "2 0 object\n",
      "2 1 responsible\n",
      "2 2 symptom\n",
      "2 3 budgets\n",
      "2 4 planning\n",
      "2 5 obligation\n",
      "2 6 iridium\n",
      "2 7 ruthenium\n",
      "2 8 readable\n",
      "2 9 register\n",
      "2 10 report\n",
      "2 11 spent\n",
      "2 12 receipt\n",
      "3 0 qualification\n",
      "3 1 current\n",
      "3 2 palladium\n",
      "3 3 services\n",
      "3 4 product\n",
      "4 0 set\n",
      "4 1 statistical\n",
      "4 2 address\n",
      "4 3 activity\n",
      "4 4 classification\n",
      "4 5 union\n",
      "4 6 structure\n",
      "4 7 control\n",
      "4 8 work\n",
      "4 9 internal\n",
      "4 10 institution\n",
      "4 11 order\n",
      "4 12 reconstruct\n",
      "4 13 certificate\n",
      "4 14 flow\n",
      "4 15 result\n",
      "4 16 term\n",
      "4 17 tax\n",
      "4 18 payment\n",
      "4 19 functional\n",
      "4 20 incision\n",
      "4 21 activities\n",
      "4 22 loss\n",
      "4 23 compilation\n",
      "4 24 direction\n",
      "4 25 request\n",
      "4 26 target\n",
      "4 27 thous\n",
      "4 28 spend\n",
      "4 29 execution\n",
      "4 30 bank\n",
      "4 31 date\n",
      "4 32 republican\n",
      "4 33 department\n",
      "4 34 synthetic\n",
      "4 35 test\n",
      "4 36 head\n",
      "4 37 supplier\n",
      "4 38 natural\n",
      "4 39 rhodium\n",
      "4 40 сonsecutive\n",
      "4 41 processing\n",
      "4 42 include\n",
      "4 43 contained\n",
      "4 44 support\n",
      "4 45 requirement\n",
      "4 46 procurement\n",
      "4 47 production\n",
      "4 48 size\n",
      "4 49 january\n",
      "4 50 authority\n",
      "4 51 guarantee\n",
      "4 52 purchase\n",
      "4 53 open\n",
      "4 54 registration\n",
      "4 55 entity\n",
      "4 56 mail\n",
      "4 57 http\n",
      "4 58 phone\n",
      "4 59 analytical\n",
      "4 60 gavalec\n",
      "4 61 normative\n",
      "4 62 category\n",
      "4 63 program\n",
      "4 64 loan\n",
      "4 65 shipped\n",
      "4 66 refining\n",
      "4 67 processed\n",
      "4 68 withdrawal\n",
      "4 69 standard\n",
      "4 70 identification\n",
      "4 71 issue\n",
      "4 72 feature\n",
      "4 73 text\n",
      "4 74 statutory\n",
      "4 75 main\n",
      "4 76 value\n",
      "4 77 leader\n",
      "4 78 fund\n",
      "4 79 sequence\n",
      "4 80 planned\n",
      "4 81 corruption\n",
      "4 82 securities\n",
      "4 83 official\n",
      "4 84 carry\n",
      "4 85 item\n",
      "4 86 source\n",
      "4 87 check\n",
      "4 88 executive\n",
      "4 89 month\n",
      "4 90 credit\n",
      "4 91 provision\n",
      "4 92 approve\n",
      "4 93 expertise\n",
      "4 94 opinion\n",
      "4 95 application\n",
      "4 96 opendata\n",
      "4 97 emerald\n",
      "5 0 process\n",
      "5 1 expense\n",
      "5 2 contractor\n",
      "5 3 ship\n",
      "5 4 article\n",
      "5 5 municipal\n",
      "5 6 paragraph\n",
      "5 7 ratio\n",
      "6 0 waste\n",
      "6 1 schedule\n"
     ]
    }
   ],
   "source": [
    "for i,j in enumerate(result_with_syn):\n",
    "    #print(i,j)\n",
    "    for k,l in enumerate(j):\n",
    "        #result_with_syn[i].append(j) #апендим синонимы\n",
    "        #print(i,k,l)\n",
    "        result_with_syn[i][k]=get_syns(l)\n",
    "#result_with_syn_2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'real, rattling, really, tangible, veridical, material, substantial, very, genuine, literal, existent, real_number, actual'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_syns('real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['budgetary',\n",
       "  'amalgamate, fused, consolidate, consolidated, amalgamated, coalesced',\n",
       "  'osmium, atomic_number_76, Os',\n",
       "  'foreign, strange, alien, extraneous',\n",
       "  'switch, swap, swop, merchandise, patronage, craft, business_deal, trade, trade_in, trade_wind, deal, barter, sell',\n",
       "  '',\n",
       "  'municipality',\n",
       "  'real, rattling, really, tangible, veridical, material, substantial, very, genuine, literal, existent, real_number, actual'],\n",
       " ['sector, sphere',\n",
       "  'written_document, document, text_file, papers',\n",
       "  'describe, answer_for, accounting, accountancy, method_of_accounting, account_statement, accounting_system, report, calculate, account',\n",
       "  'inspection, review',\n",
       "  '',\n",
       "  'baseball_field, infield, diamond, ball_field, rhombus, baseball_diamond, rhomb, adamant',\n",
       "  'external, outside, international, extraneous',\n",
       "  'elbow_room, room, way, board',\n",
       "  'put, perspective, attitude, billet, place, stead, post, positioning, set, berth, view, situation, placement, office, locating, military_position, posture, emplacement, spatial_relation, location, status, side, stance, pose, position, lieu, lay, spot',\n",
       "  'recruit, come_in, introduce, accede, move_into, record, insert, go_in, put_down, enrol, get_into, go_into, enroll, enter, inscribe, figure, embark, get_in, participate, infix',\n",
       "  'wastefulness, consume, languish, macerate, knock_off, neutralize, barren, run_off, neutralise, scourge, squander, liquidate, wasteland, lay_waste_to, ravage, thriftlessness, rot, blow, waste_product, waste_material, waste_matter, waste, dissipation, pine_away, ware, permissive_waste, emaciate, do_in, devastate, desolate',\n",
       "  'anti',\n",
       "  'legal_philosophy, law, police_force, jurisprudence, constabulary, practice_of_law, natural_law, police, law_of_nature',\n",
       "  'fax, telefax, facsimile, facsimile_machine',\n",
       "  '',\n",
       "  'recruit, come_in, introduce, accede, move_into, record, insert, go_in, put_down, enrol, get_into, go_into, enroll, enter, inscribe, figure, embark, get_in, participate, infix'],\n",
       " ['target, aim, physical_object, object, objective',\n",
       "  'responsible_for, responsible, creditworthy',\n",
       "  'symptom',\n",
       "  'budget',\n",
       "  'design, be_after, preparation, planning, project, plan, contrive, provision',\n",
       "  'responsibility, debt_instrument, duty, obligation, indebtedness, certificate_of_indebtedness',\n",
       "  'iridium, Ir, atomic_number_77',\n",
       "  'atomic_number_44, ruthenium, Ru',\n",
       "  'decipherable, readable, clear',\n",
       "  'file, read, cash_register, record, register, registry, cross-file, show',\n",
       "  'study, news_report, describe, written_report, story, paper, report, theme, reputation, write_up, cover, report_card, composition, account',\n",
       "  'pass, spent, worn_out, washed-out, dog-tired, expend, spend, drop, played_out, worn-out, fatigued, exhausted, fagged',\n",
       "  'acknowledge, reception, receipt'],\n",
       " ['reservation, qualification, making',\n",
       "  'stream, current, flow, electric_current',\n",
       "  'atomic_number_46, palladium, Pd',\n",
       "  'avail, Robert_William_Service, services, religious_service, divine_service, overhaul, inspection_and_repair, serving, service, table_service, servicing, military_service, help, serve, Service, armed_service, service_of_process',\n",
       "  'intersection, ware, production, merchandise, Cartesian_product, mathematical_product, product'],\n",
       " ['plant, put, hardened, situated, limit, lay_out, adjust, countersink, do, place, set_up, readiness, fit, Set, stage_set, jell, hardening, rig, circle, localise, coif, laid, fructify, dictated, specify, placed, primed, band, mark, fix, lot, go_under, go_down, define, bent, gear_up, fixed, determined, solidification, prepare, coiffure, determine, rigid, localize, coiffe, ready, sic, congeal, dress, solidifying, arrange, typeset, Seth, correct, exercise_set, curing, pose, position, located, lay, set',\n",
       "  'statistical',\n",
       "  'address, computer_address, cover, destination, direct, treat, accost, handle, speech, turn_to, plow, come_up_to, speak, deal, reference, name_and_address, savoir-faire, call',\n",
       "  'bodily_process, activity, natural_process, natural_action, body_process, bodily_function, activeness, action',\n",
       "  'classification, categorization, categorisation, sorting, compartmentalisation, compartmentalization, assortment',\n",
       "  'marriage, conglutination, Federal, coupling, matrimony, North, trade_union, trades_union, mating, sexual_union, pairing, wedlock, Union, labor_union, union, join, brotherhood, spousal_relationship, jointure, sum, uniting, conjugation, unification',\n",
       "  'structure, social_system, bodily_structure, complex_body_part, body_structure, social_organization, anatomical_structure, construction, social_structure, social_organisation',\n",
       "  'hold, ascendance, moderate, see_to_it, hold_in, ascendence, operate, insure, master, keep_in_line, manipulate, ascendancy, controller, verify, ascertain, assure, restraint, see, control, check, ensure, command, mastery, control_condition, curb, dominance, contain, ascendency',\n",
       "  'piece_of_work, run, workplace, oeuvre, play, do_work, wreak, operate, function, bring, knead, forge, make_for, solve, act, work_on, mould, make, figure_out, puzzle_out, employment, study, work_out, influence, ferment, sour, process, exercise, turn, crop, body_of_work, lick, work, act_upon, exploit, put_to_work, cultivate, shape, go, form, mold',\n",
       "  'intimate, national, interior, internal, home, inner, intragroup',\n",
       "  'mental_hospital, introduction, creation, asylum, founding, initiation, foundation, origination, mental_institution, instauration, innovation, insane_asylum, establishment, psychiatric_hospital, institution, mental_home',\n",
       "  'put, tell, rules_of_order, set_up, place, club, society, edict, consecrate, regulate, ordination, regularize, prescribe, range, enjoin, fiat, guild, rate, social_club, parliamentary_law, govern, dictate, gild, regularise, lodge, rescript, monastic_order, say, purchase_order, Order, order, decree, rank, parliamentary_procedure, grade, ordering, ordain, ordinate, Holy_Order, arrange, order_of_magnitude, orderliness',\n",
       "  'remodel, restore, construct, reconstruct, redo, rebuild, retrace',\n",
       "  'credential, security, certificate, credentials, certification',\n",
       "  'flowing, rate_of_flow, flux, period, run, menstruum, catamenia, hang, feed, menses, menstruate, flow, menstruation, flow_rate, stream, current, course, fall',\n",
       "  'outcome, resultant, issue, termination, effect, leave, consequence, solvent, answer, ensue, result, lead, resultant_role, solution, final_result, event, resolution, upshot',\n",
       "  'full_term, terminal_figure, term, condition, terminus',\n",
       "  'revenue_enhancement, assess, tax, taxation, task',\n",
       "  'defrayal, defrayment, requital, payment',\n",
       "  'usable, running, operative, functional, working, useable, operational, operable',\n",
       "  'dent, prick, surgical_incision, incision, scratch, section, slit',\n",
       "  'bodily_process, activity, natural_process, natural_action, body_process, bodily_function, activeness, action',\n",
       "  'red_ink, personnel_casualty, expiration, departure, loss, red, deprivation, going, release, exit, passing',\n",
       "  'compiling, compilation, digest',\n",
       "  'focussing, charge, focal_point, centering, counsel, steering, direction, guidance, focus, counselling, counseling, instruction, way, focusing, commission, management',\n",
       "  'request, asking, bespeak, postulation, petition, call_for, quest',\n",
       "  'point, target, aim, quarry, butt, direct, prey, place, mark, object, objective, target_area, fair_game',\n",
       "  'thousand, yard, chiliad, G, one_thousand, K, thou, grand, M, 1000',\n",
       "  'expend, spend, pass, drop',\n",
       "  'performance, death_penalty, capital_punishment, writ_of_execution, carrying_into_action, slaying, execution, murder, instruction_execution, carrying_out, execution_of_instrument, implementation, executing',\n",
       "  'banking_company, trust, rely, money_box, swear, deposit, banking_concern, bank, depository_financial_institution, bank_building, camber, coin_bank, cant, savings_bank',\n",
       "  'date, go_out, see, escort, appointment, day_of_the_month, engagement, particular_date, date_stamp, go_steady',\n",
       "  'republican, Republican_River, Republican',\n",
       "  'section, department',\n",
       "  'synthetic, synthetical, man-made, semisynthetic, synthetic_substance, celluloid',\n",
       "  'examination, trial_run, examine, mental_testing, essay, quiz, run, screen, psychometric_test, exam, prove, try, trial, tryout, mental_test, try_out, test',\n",
       "  'headway, head_up, chief, channelise, direct, lead, headland, heading, point, channelize, question, header, head, top_dog, capitulum, fountainhead, drumhead, manoeuver, psyche, forefront, nous, head_word, guide, oral_sex, foreland, straits, brain, maneuver, caput, school_principal, headspring, steer, read/write_head, promontory, mind, head_teacher, principal, manoeuvre, pass',\n",
       "  'provider, supplier',\n",
       "  'rude, instinctive, raw, innate, natural, lifelike, cancel, born',\n",
       "  'atomic_number_45, Rh, rhodium',\n",
       "  '',\n",
       "  'swear_out, work, sue, treat, action, processing, process, march, work_on, litigate, serve',\n",
       "  'admit, include, let_in',\n",
       "  'incorporate, stop, comprise, carry, hold, control, moderate, check, turn_back, hold_in, hold_back, curb, contain, take, contained, arrest, bear',\n",
       "  'sustain, financial_support, corroborate, underpin, hold, digest, sustenance, stick_out, stomach, abide, keep_going, fend_for, back, financial_backing, support, tolerate, endure, hold_up, supporting, defend, patronise, reinforcement, put_up, living, livelihood, documentation, patronize, substantiate, plunk_for, keep, back_up, stand, musical_accompaniment, funding, bread_and_butter, bear, accompaniment, suffer, affirm, indorse, bear_out, brook, reenforcement, patronage, endorse, plump_for, confirm, backup, backing, subscribe',\n",
       "  'necessity, prerequisite, requirement, requisite, necessary, demand, essential',\n",
       "  'procural, procurement, procurance',\n",
       "  'production, output, product, yield',\n",
       "  'size_of_it, sizing, size',\n",
       "  'Jan, January',\n",
       "  'confidence, sureness, sanction, authorization, government_agency, self-confidence, potency, federal_agency, bureau, authority, dominance, self-assurance, office, agency, authorisation, say-so, assurance',\n",
       "  'assure, warrant, secure, insure, warranty, guarantee, warrantee, ensure, undertake, vouch, guaranty',\n",
       "  'purchase, buy, leverage',\n",
       "  'candid, undefended, undecided, subject, outdoors, unresolved, open_up, surface, clear, undetermined, capable, heart-to-heart, unfastened, assailable, overt, exposed, opened, undefendable, out-of-doors, open, spread, receptive, unfold, loose, spread_out, afford, give, open_air',\n",
       "  'readjustment, enrolment, registration, adjustment, enrollment',\n",
       "  'entity',\n",
       "  'ring_armor, mail, get_off, postal_service, ring_armour, post, ring_mail, chain_mail, mail_service, chain_armour, chain_armor, send',\n",
       "  'HTTP, hypertext_transfer_protocol',\n",
       "  'call_up, telephone_set, phone, sound, ring, speech_sound, earpiece, earphone, headphone, telephone, call',\n",
       "  'analytical, analytic',\n",
       "  '',\n",
       "  'normative, prescriptive',\n",
       "  'class, family, category',\n",
       "  'course_of_study, broadcast, curriculum, computer_program, platform, political_platform, computer_programme, syllabus, political_program, plan, program, programme',\n",
       "  'loanword, lend, loan',\n",
       "  'embark, transport, send, ship',\n",
       "  'refinement, rarify, refine, refining, polish, fine-tune, rectify, elaborate, down, purification, complicate',\n",
       "  'swear_out, work, refined, sue, treat, action, process, march, work_on, litigate, serve, processed',\n",
       "  'pulling_out, climb-down, coitus_interruptus, backdown, withdrawal_method, onanism, secession, detachment, withdrawal, drug_withdrawal',\n",
       "  'standard, stock, banner, received, touchstone, monetary_standard, criterion, measure',\n",
       "  'designation, identification, recognition',\n",
       "  'bring_out, consequence, cut, subject, government_issue, put_out, upshot, supply, emergence, way_out, exit, military_issue, publication, number, payoff, takings, make_out, progeny, issue, offspring, effect, yield, write_out, release, come_forth, egress, proceeds, go_forth, topic, event, outcome, come_out, outlet, issuance, result, take, issuing, emerge, matter, publish, return',\n",
       "  'feature_of_speech, characteristic, boast, have, feature, lineament, sport, feature_film, feature_article',\n",
       "  'textual_matter, text, schoolbook, text_edition, textbook, school_text',\n",
       "  'statutory',\n",
       "  'chief, principal, master, main, independent, primary, briny',\n",
       "  'appreciate, value, prise, note_value, rate, economic_value, assess, esteem, respect, valuate, appraise, prize, treasure, evaluate, measure, time_value',\n",
       "  'drawing_card, loss_leader, leader',\n",
       "  'investment_trust, stock, investment_firm, investment_company, monetary_fund, store, fund',\n",
       "  'episode, chronological_succession, successiveness, succession, chronological_sequence, sequence',\n",
       "  'planned, design, aforethought, be_after, project, plotted, plan, contrive',\n",
       "  'degeneracy, depravity, putrescence, rottenness, putrefaction, subversion, corruption, putridness, corruptness, depravation',\n",
       "  'security_system, surety, security_measures, security, protection, security_department, security_measure, certificate',\n",
       "  'functionary, official, prescribed',\n",
       "  'stock, run, hold, persuade, gestate, express, post, acquit, transport, extend, behave, dribble, expect, conduct, channel, transmit, stockpile, pack, impart, comport, have_a_bun_in_the_oven, bear, convey, deport, take, contain, sway, carry',\n",
       "  'item, particular, token, point, detail',\n",
       "  'germ, seed, informant, origin, reservoir, source, author, rootage, beginning, generator, root, reference',\n",
       "  'confirmation, check_into, check_out, chequer, look_into, hold, moderate, fit, balk, see_to_it, hold_in, chink, check-out_procedure, go_over, jibe, chit, hinderance, check_off, correspond, baulk, deterrent, insure, stay, bank_check, handicap, mark, condition, check_up_on, bridle, check_mark, hindrance, ascertain, assure, assay, cheque, checker, watch, tab, see, suss_out, control, turn_back, check, hold_back, halt, stoppage, tick, ensure, tick_off, retard, arrest, delay, gibe, tally, determine, crack, stop, checkout, substantiation, verification, chip, hitch, check_over, agree, discipline, curb, contain, mark_off, match, train, learn, break, find_out, impediment',\n",
       "  'executive, administrator, executive_director',\n",
       "  'month, calendar_month',\n",
       "  'mention, course_credit, acknowledgment, credit_entry, credit, credit_rating, quotation, accredit, reference, deferred_payment, cite, citation, recognition',\n",
       "  'preparation, proviso, planning, purvey, supplying, provision, supply',\n",
       "  'sanction, okay, approve, O.K.',\n",
       "  'expertise, expertness',\n",
       "  'legal_opinion, ruling, feeling, popular_opinion, thought, persuasion, view, sentiment, vox_populi, judgement, judgment, belief, notion, opinion, public_opinion, impression',\n",
       "  'application_program, covering, applications_programme, lotion, practical_application, diligence, application, coating',\n",
       "  '',\n",
       "  'emerald'],\n",
       " ['swear_out, physical_process, work, litigate, sue, procedure, treat, action, process, cognitive_operation, mental_process, summons, march, work_on, appendage, operation, cognitive_process, outgrowth, serve, unconscious_process',\n",
       "  'disbursal, disbursement, expense, write_off, write_down',\n",
       "  'contractile_organ, declarer, contractor',\n",
       "  'embark, transport, send, ship',\n",
       "  'clause, article',\n",
       "  'municipal',\n",
       "  'paragraph',\n",
       "  'proportion, ratio'],\n",
       " ['wastefulness, consume, godforsaken, languish, macerate, knock_off, neutralize, barren, run_off, neutralise, wild, scourge, squander, liquidate, wasteland, lay_waste_to, ravage, thriftlessness, rot, blow, waste_product, waste_material, waste_matter, waste, dissipation, pine_away, ware, permissive_waste, emaciate, do_in, devastate, desolate',\n",
       "  'schedule, agenda, docket']]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_with_syn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "f = open('workfile', 'r')\n",
    "print(f.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open('workfile', 'w')\n",
    "for i in result_with_syn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-214-8a6ef8b839ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0maaa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maaa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mflattened_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "aaa =[[1,3],[3,41,1]]\n",
    "aaa[0][1]=[9,2]\n",
    "aaa\n",
    "aaa[0]\n",
    "for x in aaa[0]:\n",
    "    for y in x:\n",
    "        flattened_list.append(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "nltk.__version__\n",
    "\n",
    "from nltk.corpus import wordnet as wn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'budgetary'"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Разбиваем большой кластер\n",
    "split_big_cluster = result[0].split(', ')\n",
    "#split_big_cluster=result[0].map(lambda x: re.sub('\\s+', ' ', x))\n",
    "\n",
    "#features_list = features.T.iloc[1].tolist()\n",
    "split_big_cluster[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Достаем индексы фич\n",
    "index_split_big_cluster=[]\n",
    "for i, j in enumerate(vocabulary):\n",
    "    if j in split_big_cluster:\n",
    "        index_split_big_cluster.append(i)\n",
    "#Матрица финальная\n",
    "dtm_big_cluster =dtm.T[:, index_split_big_cluster]\n",
    "dtm_big_cluster=dtm_big_cluster.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Функция которая по вводимому списку слов создает матрицу терм-док\n",
    "result_with_syn[0]\n",
    "def list_to_matrix(list_words):\n",
    "    global vocabulary\n",
    "    global dtm\n",
    "    indexes=[]\n",
    "    for i, j in enumerate(vocabulary):\n",
    "        if j in list_words:\n",
    "            indexes.append(i)\n",
    "    #Матрица финальная\n",
    "    result_matrix =dtm.T[:, indexes]\n",
    "    result_matrix=result_matrix.T\n",
    "    return result_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello-world'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [\"Hello\", \"world\"]\n",
    "\"-\".join(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['l', 'l', 'l', 'l', 'l', 'l', 'l', 'l']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_list = []\n",
    "for sublist in result_with_syn[0]:\n",
    "    for item in sublist:\n",
    "        flat_list.append(item)\n",
    "flat_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'budgetary, amalgamate, fused, consolidate, consolidated, amalgamated, coalesced, osmium, atomic_number_76, Os, foreign, strange, alien, extraneous, switch, swap, swop, merchandise, patronage, craft, business_deal, trade, trade_in, trade_wind, deal, barter, sell, , municipality, real, rattling, really, tangible, veridical, material, substantial, very, genuine, literal, existent, real_number, actual'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\", \".join(result_with_syn[0]).split(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_to_matrix(\", \".join(result_with_syn[0]).split(', '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clust= 6 silh= 0.161324313737\n",
      "metals, statistical, expense, address, object, activity, control, article, order, certificate, internal, flow, result, term, functional, direction, compilation, target, bank, execution, spend, date, department, current, natural, readable, ruthenium, contained, include, report, procurement, foreign, size, authority, external, program, schedule, phone, loan, paragraph, text, wastes, feature, anti, corruption, complextype, securities, carry, planned, credit, sum, opendata\n",
      "  \n",
      "contractor, ship, payment, document, thous, obligation, inspection, osmium, processing, diamond, withdrawal, real\n",
      "  \n",
      "budgetary, incision, product, check\n",
      "  \n",
      "request, synthetic, support, purchase, value, standard, main, expertise, fax, body\n",
      "  \n",
      "structure\n",
      "  \n",
      "classification, waste, institution, responsible, activities, municipal, quot, open, registration, analytical, gavalec, shipped, services, identification, trade, enter, leader, fund\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Разбивка большого кластера\n",
    "matrix = dtm_big_cluster\n",
    "x=[]\n",
    "y=[]\n",
    "clusters=6\n",
    "kmean = KMeans(n_clusters=clusters)\n",
    "rez=kmean.fit_predict(matrix) \n",
    "silhouette_avg = silhouette_score(matrix, rez)\n",
    "x.append(clusters)\n",
    "y.append(silhouette_avg)\n",
    "print('clust=',clusters,'silh=',silhouette_avg)\n",
    "\n",
    "#Делаем панду\n",
    "d = {'rez': rez, 'vocabulary': split_big_cluster}\n",
    "df = pd.DataFrame(data=d)\n",
    "#Слова в кластеры\n",
    "\n",
    "result =  df.groupby('rez')['vocabulary'].apply(lambda x: \"%s\" % ', '.join(x))\n",
    "for i in result:\n",
    "    print(i)\n",
    "    print('  ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.0\n",
      "10.0\n",
      "7.0\n",
      "120.0\n",
      "5.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'output  department  benefits  production  regions  producer  sectors  detailed'"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_file = open(\"words_uk.txt\", \"r\")\n",
    "uk = text_file.read().split('\\n')\n",
    "uk = pd.Series(uk)\n",
    "uk = uk.map(lambda x: re.sub('\\s+', ' ', x))\n",
    "uk = uk.map(lambda x: re.sub('[^A-Za-zА-Яа-я]', ' ', x))\n",
    "\n",
    "uk = uk.str.lower()\n",
    "lens_uk = []\n",
    "for i in uk:\n",
    "    print((len(i.split('\\n')[0].split(' '))+1)/2)\n",
    "   # lens_uk.append((i.split('\\n')))\n",
    "#lens_uk[1][0]\n",
    "uk[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Удаляем стопворды\n",
    "text_file = open(\"stop_words.txt\", \"r\")\n",
    "lines = text_file.read().split('\\n')\n",
    "stopwords = lines[1:-2]\n",
    "stopwords = set(stopwords)\n",
    "\n",
    "def replace_stopwords(str_src, stopwords):\n",
    "    for w in stopwords:\n",
    "        str_src = str_src.replace(\" \" + w + \" \", \" \")\n",
    "        str_src = str_src.replace(\"  \",\" \")\n",
    "    return str_src\n",
    " \n",
    "# Удаление стоп слов\n",
    "ru =ru.map(lambda x: replace_stopwords(x, stopwords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[125, 4, 5, 5, 3, 2, 6]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_clstrs = []\n",
    "result =  df.groupby('rez')['vocabulary'].apply(lambda x: \"%s\" % ', '.join(x))\n",
    "for i in result:\n",
    "    #print(len(i.split(', ')))\n",
    "    words_in_clstrs.append(len(i.split(', ')))\n",
    "words_in_clstrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chili_words = [35, 7, 29, 9,  43, 14, 35, 35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVdusFWO3z9Pb201y8hbefvtPN0x5EmxeNlUilWBx+vr66O8foKxsJ4qicOnS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\npgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f089f679ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "from math import pi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "words_in_clstrs = chili_words\n",
    "words_in_clstrs2 = chili_words\n",
    "# Set data\n",
    "cat = ['1','2','3','4','5','6','7','8']\n",
    "values = words_in_clstrs\n",
    "\n",
    "N = len(cat)\n",
    "\n",
    "x_as = [n / float(N) * 2 * pi for n in range(N)]\n",
    "\n",
    "# Because our chart will be circular we need to append a copy of the first \n",
    "# value of each list at the end of each list with data\n",
    "values += values[:1]\n",
    "x_as += x_as[:1]\n",
    "\n",
    "\n",
    "# Set color of axes\n",
    "plt.rc('axes', linewidth=0.5, edgecolor=\"#888888\")\n",
    "\n",
    "\n",
    "# Create polar plot\n",
    "ax = plt.subplot(111, polar=True)\n",
    "\n",
    "\n",
    "# Set clockwise rotation. That is:\n",
    "ax.set_theta_offset(pi / 2)\n",
    "ax.set_theta_direction(-1)\n",
    "\n",
    "\n",
    "# Set position of y-labels\n",
    "ax.set_rlabel_position(0)\n",
    "\n",
    "\n",
    "# Set color and linestyle of grid\n",
    "ax.xaxis.grid(True, color=\"#888888\", linestyle='solid', linewidth=0.5)\n",
    "ax.yaxis.grid(True, color=\"#888888\", linestyle='solid', linewidth=0.5)\n",
    "\n",
    "\n",
    "# Set number of radial axes and remove labels\n",
    "plt.xticks(x_as[:-1], [])\n",
    "\n",
    "# Set yticks\n",
    "plt.yticks([0,  10, 20,30,40], [\"0\", \"10\", \"20\",'30','40'])\n",
    "\n",
    "\n",
    "# Plot data\n",
    "ax.plot(x_as, values, linewidth=0, linestyle='solid', zorder=3)\n",
    "\n",
    "# Fill area\n",
    "ax.fill(x_as, values, 'b', alpha=0.3)\n",
    "\n",
    "size_of_radar =50\n",
    "# Set axes limits\n",
    "plt.ylim(0, size_of_radar)\n",
    "\n",
    "# Draw ytick labels to make sure they fit properly\n",
    "for i in range(N):\n",
    "    angle_rad = i / float(N) * 2 * pi\n",
    "\n",
    "    if angle_rad == 0:\n",
    "        ha, distance_ax = \"center\", 1\n",
    "    elif 0 < angle_rad < pi:\n",
    "        ha, distance_ax = \"left\", 1\n",
    "    elif angle_rad == pi:\n",
    "        ha, distance_ax = \"center\", 1\n",
    "    else:\n",
    "        ha, distance_ax = \"right\", 1\n",
    "\n",
    "    ax.text(angle_rad, 52 + distance_ax, 'N'+str(cat[i])+' '+str(words_in_clstrs2[i])+' words', \n",
    "            size=13, horizontalalignment=ha, verticalalignment=\"center\")\n",
    "\n",
    "# Draw ytick labels to make sure they fit properly\n",
    "plt.savefig('filename_chile.png', dpi = 140)\n",
    "\n",
    "# Show polar plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Визуализация\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 3\n",
      "-0.223409407245\n"
     ]
    }
   ],
   "source": [
    "#Попытка исопльзования DBSCAN\n",
    "db = DBSCAN(eps=0.0001, min_samples=3).fit(dtm2)\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "rez=db.fit_predict(dtm2) \n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print(silhouette_score(dtm2, rez))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "8.0\n",
    "10.0\n",
    "7.0\n",
    "120.0\n",
    "5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LMHQ6HVarjfj4eOLjE4mLi8dgEMu/I8HtXuTixXPcdNMRUlJS1A5H0CiRRLe5urp6QC/W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17902b2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "from math import pi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "words_in_clstrs = [8, 10, 7, 20, 5]\n",
    "words_in_clstrs2 = [8, 10, 7, 120, 5]\n",
    "# Set data\n",
    "cat = ['1','2','3','4','5']\n",
    "values = words_in_clstrs\n",
    "\n",
    "N = len(cat)\n",
    "\n",
    "x_as = [n / float(N) * 2 * pi for n in range(N)]\n",
    "\n",
    "# Because our chart will be circular we need to append a copy of the first \n",
    "# value of each list at the end of each list with data\n",
    "values += values[:1]\n",
    "x_as += x_as[:1]\n",
    "\n",
    "\n",
    "# Set color of axes\n",
    "plt.rc('axes', linewidth=0.5, edgecolor=\"#888888\")\n",
    "\n",
    "\n",
    "# Create polar plot\n",
    "ax = plt.subplot(111, polar=True)\n",
    "\n",
    "\n",
    "# Set clockwise rotation. That is:\n",
    "ax.set_theta_offset(pi / 2)\n",
    "ax.set_theta_direction(-1)\n",
    "\n",
    "\n",
    "# Set position of y-labels\n",
    "ax.set_rlabel_position(0)\n",
    "\n",
    "\n",
    "# Set color and linestyle of grid\n",
    "ax.xaxis.grid(True, color=\"#888888\", linestyle='solid', linewidth=0.5)\n",
    "ax.yaxis.grid(True, color=\"#888888\", linestyle='solid', linewidth=0.5)\n",
    "\n",
    "\n",
    "# Set number of radial axes and remove labels\n",
    "plt.xticks(x_as[:-1], [])\n",
    "\n",
    "# Set yticks\n",
    "plt.yticks([0, 5, 10, 15], [\"0\", '5', \"10\", \"15\"])\n",
    "\n",
    "\n",
    "# Plot data\n",
    "ax.plot(x_as, values, linewidth=0, linestyle='solid', zorder=3)\n",
    "\n",
    "# Fill area\n",
    "ax.fill(x_as, values, 'b', alpha=0.3)\n",
    "\n",
    "size_of_radar =20\n",
    "# Set axes limits\n",
    "plt.ylim(0, size_of_radar)\n",
    "\n",
    "# Draw ytick labels to make sure they fit properly\n",
    "for i in range(N):\n",
    "    angle_rad = i / float(N) * 2 * pi\n",
    "\n",
    "    if angle_rad == 0:\n",
    "        ha, distance_ax = \"center\", 1\n",
    "    elif 0 < angle_rad < pi:\n",
    "        ha, distance_ax = \"left\", 1\n",
    "    elif angle_rad == pi:\n",
    "        ha, distance_ax = \"center\", 1\n",
    "    else:\n",
    "        ha, distance_ax = \"right\", 1\n",
    "\n",
    "    ax.text(angle_rad, 21 + distance_ax, 'N'+str(cat[i])+' '+str(words_in_clstrs2[i])+' words', size=13, horizontalalignment=ha, verticalalignment=\"center\")\n",
    "\n",
    "# Draw ytick labels to make sure they fit properly\n",
    "plt.savefig('filename_uk.png', dpi = 140)\n",
    "\n",
    "# Show polar plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['budgetary',\n",
       " 'amalgamate, fused, consolidate, consolidated, amalgamated, coalesced',\n",
       " 'osmium, atomic_number_76, Os',\n",
       " 'foreign, strange, alien, extraneous',\n",
       " 'switch, swap, swop, merchandise, patronage, craft, business_deal, trade, trade_in, trade_wind, deal, barter, sell',\n",
       " '',\n",
       " 'municipality',\n",
       " 'real, rattling, really, tangible, veridical, material, substantial, very, genuine, literal, existent, real_number, actual']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_with_syn[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'budgetary'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-2f8b9a075e40>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mnc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mkmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0mrez\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkmean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m             \u001b[0msilhouette_avg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrez\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/cluster/k_means_.py\u001b[0m in \u001b[0;36mfit_predict\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \"\"\"\n\u001b[0;32m--> 898\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/cluster/k_means_.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    878\u001b[0m         \"\"\"\n\u001b[1;32m    879\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_fit_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minertia_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter_\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/cluster/k_means_.py\u001b[0m in \u001b[0;36m_check_fit_data\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    852\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_fit_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m         \u001b[0;34m\"\"\"Verify that the number of samples given is larger than k\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 854\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\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    855\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m             raise ValueError(\"n_samples=%d should be >= n_clusters=%d\" % (\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    380\u001b[0m                                       force_all_finite)\n\u001b[1;32m    381\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\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    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'budgetary'"
     ]
    }
   ],
   "source": [
    "#Распределение кластеров\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import numpy as np\n",
    "\n",
    "x= []\n",
    "y= []\n",
    "averg=[]\n",
    "mm = 0\n",
    "matrix = result_with_syn[0]\n",
    "for _ in range(20):\n",
    "    for nc in range(10, 3,-1):\n",
    "            kmean = KMeans(n_clusters=nc)\n",
    "            rez=kmean.fit_predict(matrix) \n",
    "            silhouette_avg = silhouette_score(matrix, rez)\n",
    "            x.append(nc)\n",
    "            y.append(silhouette_avg)\n",
    "            print('clust=',nc,'silh=',silhouette_avg)\n",
    "\n",
    "        \n",
    "        \n",
    "#    df['clst'] = x\n",
    "#    df['coef'] = y\n",
    "#    x= []\n",
    "#    y= []\n",
    "#    averg.append(df.loc[df['coef'].idxmax()]['clst'])\n",
    "#sum(averg)/len(averg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 1361)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtm = dtm.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clust= 10 silh= 0.0732165802112\n"
     ]
    }
   ],
   "source": [
    "kmean = KMeans(n_clusters=10)\n",
    "rez=kmean.fit_predict(dtm) \n",
    "print('clust=',10,'silh=',silhouette_score(dtm, rez))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean = KMeans(n_clusters=10).fit(dtm)\n",
    "kmean.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf82702588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ux,uy, marker='o', color='g', ls='')\n",
    "plt.plot(x,y, marker='o', color='r', ls='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = []\n",
    "for i in range(len(nomer)):\n",
    "    r.append(((ux[i]*-x[i])**2+(uy[i]*-y[i])**2)**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Токенизация\n",
    "ru_ls= []\n",
    "for i in range(len(ru)):\n",
    "    ru_ls.append(ru[i].split(' '))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'organization', 'to', 'be', 'under', 'the', 'jurisdiction', 'of', 'the', 'Ministry', 'of', 'Finance', 'Russia', 'list', '', 'and', 'the', 'function', 'of', 'the', 'organization', 'to', 'be', 'under', 'the', 'jurisdiction', 'of', 'the', 'Ministry', 'of', 'Finance', '', 'Russia', 'field', 'name', 'english', 'description', 'English', 'description', 'format', 'name', 'of', 'the', 'organization', 'Name', 'of', 'the', 'organization', 'name', 'of', 'the', 'organization', 'to', 'be', 'under', 'the', 'jurisdiction', 'of', 'the', 'Ministry', 'of', 'Finance', 'Russia', 'string', 'head', 'organization', 'of', 'the', 'CEO', '', 'the', 'head', 'of', 'the', 'string', 'legal', 'address', 'Legal', 'address', 'legal', 'address', 'legal', 'address', 'string', 'code', 'kladr', 'KLADR', 'Legal', 'address', 'the', 'legal', 'address', 'of', 'the', 'string', 'kladr', 'code', 'postal', 'address', 'Postal', 'address', 'postal', 'address', 'postal', 'address', 'string', 'code', 'kladr', 'KLADR', 'Postal', 'address', 'postal', 'address', 'code', 'kladr', 'string', 'the', 'latitude', 'coordinate', 'the', 'Latitude', 'coordinate', 'of', 'the', 'latitude', 'string', 'longitude', 'coordinate', 'the', 'Longitude', 'coordinate', 'of', 'the', 'longitude', 'string', 'address', 'e', 'mail', 'Email', 'address', 'email', 'string', 'website', 'WebSite', 'the', 'official', 'website', 'address', 'organization', 'phone', 'Telephone', 'string', 'telephone', 'number', 'string', 'Fax', 'Fax', 'Fax', 'string', 'task', 'and', 'function', 'Functions', 'and', 'tasks', 'the', 'size', 'of', 'payment', 'of', 'work', 'for', 'the', 'vacant', 'position', 'string', '']\n",
      "0\n",
      "the -------\n",
      "organization -------\n",
      "to -------\n",
      "be -------\n",
      "under -------\n",
      "the -------\n",
      "jurisdiction -------\n",
      "of -------\n",
      "the -------\n",
      "Ministry -------\n",
      "of -------\n",
      "Finance -------\n",
      "Russia -------\n",
      "list -------\n",
      " -------\n",
      "and -------\n",
      "the -------\n",
      "function -------\n",
      "of -------\n",
      "the -------\n",
      "organization -------\n",
      "to -------\n",
      "be -------\n",
      "under -------\n",
      "the -------\n",
      "jurisdiction -------\n",
      "of -------\n",
      "the -------\n",
      "Ministry -------\n",
      "of -------\n",
      "Finance -------\n",
      " -------\n",
      "Russia -------\n",
      "field -------\n",
      "name -------\n",
      "english -------\n",
      "description -------\n",
      "English -------\n",
      "description -------\n",
      "format -------\n",
      "name -------\n",
      "of -------\n",
      "the -------\n",
      "organization -------\n",
      "Name -------\n",
      "of -------\n",
      "the -------\n",
      "organization -------\n",
      "name -------\n",
      "of -------\n",
      "the -------\n",
      "organization -------\n",
      "to -------\n",
      "be -------\n",
      "under -------\n",
      "the -------\n",
      "jurisdiction -------\n",
      "of -------\n",
      "the -------\n",
      "Ministry -------\n",
      "of -------\n",
      "Finance -------\n",
      "Russia -------\n",
      "string -------\n",
      "head -------\n",
      "organization -------\n",
      "of -------\n",
      "the -------\n",
      "CEO -------\n",
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      "e -------\n",
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      "Email -------\n",
      "address -------\n",
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      "string -------\n",
      "website -------\n",
      "WebSite -------\n",
      "the -------\n",
      "official -------\n",
      "website -------\n",
      "address -------\n",
      "organization -------\n",
      "phone -------\n",
      "Telephone -------\n",
      "string -------\n",
      "telephone -------\n",
      "number -------\n",
      "string -------\n",
      "Fax -------\n",
      "Fax -------\n",
      "Fax -------\n",
      "string -------\n",
      "task -------\n",
      "and -------\n",
      "function -------\n",
      "Functions -------\n",
      "and -------\n",
      "tasks -------\n",
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      "size -------\n",
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      "payment -------\n",
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      "work -------\n",
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      "the -------\n",
      "vacant -------\n",
      "position -------\n",
      "string -------\n",
      " -------\n",
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      "1\n",
      " -------\n",
      "the -------\n",
      "plan -------\n",
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      "control -------\n",
      "activities -------\n",
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      "the -------\n",
      "administrative -------\n",
      "Department -------\n",
      "of -------\n",
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      "Ministry -------\n",
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      "Finance -------\n",
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      "Ministry -------\n",
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      "Finance -------\n",
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      "the -------\n",
      "Russian -------\n",
      "Federation -------\n",
      "Federal -------\n",
      "state -------\n",
      "institution -------\n",
      "fgku -------\n",
      "in -------\n",
      "the -------\n",
      "course -------\n",
      "of -------\n",
      "control -------\n",
      "at -------\n",
      "the -------\n",
      "level -------\n",
      "of -------\n",
      "jurisdiction -------\n",
      "of -------\n",
      "xs -------\n",
      "element -------\n",
      "name -------\n",
      "table -------\n",
      "coll -------\n",
      "xs -------\n",
      "complexType -------\n",
      "xs -------\n",
      "sequence -------\n",
      "xs -------\n",
      "element -------\n",
      "name -------\n",
      "p -------\n",
      "p -------\n",
      "type -------\n",
      "xs -------\n",
      "string -------\n",
      "xs -------\n",
      "element -------\n",
      "name -------\n",
      "theme -------\n",
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      "type -------\n",
      "xs -------\n",
      "string -------\n",
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      "member -------\n",
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      "the -------\n",
      "control -------\n",
      "group -------\n",
      "accounting -------\n",
      "supervisor -------\n",
      "the -------\n",
      "employee -------\n",
      "financial -------\n",
      "control -------\n",
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      "take -------\n",
      "part -------\n",
      "in -------\n",
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      "xs -------\n",
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      "xs -------\n",
      "element -------\n",
      "name -------\n",
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      "count -------\n",
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      "xs -------\n",
      "element -------\n",
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      "means -------\n",
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      "Federal -------\n",
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      "name -------\n",
      "number -------\n",
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      "identify -------\n",
      "the -------\n",
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      "budget -------\n",
      "legislation -------\n",
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      "Federation -------\n",
      "type -------\n",
      "xs -------\n",
      "string -------\n",
      "xs -------\n",
      "element -------\n",
      "name -------\n",
      "number -------\n",
      "of -------\n",
      "appeal -------\n",
      "against -------\n",
      "the -------\n",
      "decision -------\n",
      "action -------\n",
      "inaction -------\n",
      "a -------\n",
      "member -------\n",
      "of -------\n",
      "the -------\n",
      "control -------\n",
      "group -------\n",
      "or -------\n",
      "the -------\n",
      "employee -------\n",
      "financial -------\n",
      "control -------\n",
      "recognized -------\n",
      "in -------\n",
      "the -------\n",
      "established -------\n",
      "order -------\n",
      "reasonable -------\n",
      "type -------\n",
      "xs -------\n",
      "string -------\n",
      " -------\n",
      "['', 'report', 'on', 'the', 'controlling', 'activities', 'of', 'the', 'administrative', 'Department', 'of', 'the', 'information', 'about', 'the', 'result', 'of', 'scheduled', 'and', 'unscheduled', 'inspection', 'to', 'carry', 'out', 'the', 'Ministry', 'of', 'Finance', 'in', 'Russia', 'the', 'limit', 'of', 'his', 'authority', 'and', 'the', 'result', 'of', 'the', 'test', 'carried', 'out', 'in', 'Russia', '', 'the', 'Ministry', 'of', 'Finance', 'and', 'its', 'subordinate', 'organization', 'of', 'the', 'xs', 'element', 'name', 'table', 'coll', 'xs', 'complexType', 'xs', 'sequence', 'xs', 'element', 'name', 'p', 'p', 'type', 'xs', 'string', 'xs', 'element', 'name', 'theme', 'test', 'type', 'xs', 'string', 'xs', 'element', 'name', 'number', 'of', 'the', 'member', 'of', 'the', 'control', 'group', 'accounting', 'supervisor', 'the', 'employee', 'financial', 'control', 'to', 'take', 'part', 'in', 'the', 'conduct', 'of', 'inspection', 'type', 'xs', 'string', 'xs', 'element', 'name', 'to', 'render', 'for', 'the', 'result', 'of', 'the', 'verification', 'representation', 'about', 'inadequate', 'execution', 'of', 'the', 'budget', 'and', 'the', 'adoption', 'of', 'a', 'measure', 'on', 'elimination', 'to', 'identify', 'the', 'violation', 'of', 'the', 'budget', 'legislation', 'date', 'room', 'type', 'xs', 'string', 'xs', 'element', 'name', 'violation', 'period', 'based', 'on', 'the', 'extension', 'of', 'the', 'holding', 'in', 'check', 'the', 'number', 'of', 'unscheduled', 'inspection', 'day', 'count', 'type', 'xs', 'string', 'xs', 'element', 'name', 'the', 'amount', 'of', 'means', 'of', 'the', 'Federal', 'budget', 'against', 'which', 'to', 'identify', 'a', 'violation', 'of', 'the', 'budgetary', 'legislation', 'of', 'the', 'Russian', 'Federation', 'RUB', 'type', 'xs', 'string', 'xs', 'element', 'name', 'number', 'to', 'identify', 'the', 'violation', 'of', 'the', 'budget', 'legislation', 'of', 'the', 'Russian', 'Federation', 'type', 'xs', 'string', 'xs', 'element', 'name', 'number', 'of', 'appeal', 'against', 'the', 'decision', 'action', 'inaction', 'a', 'member', 'of', 'the', 'control', 'group', 'or', 'the', 'employee', 'financial', 'control', 'recognized', 'in', 'the', 'established', 'order', 'reasonable', 'type', 'xs', 'string', '']\n",
      "2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -------\n",
      "report -------\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-7f0e971706ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0msynset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msynsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mlemma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msynset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlemmas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                 \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlemma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#Синонимы\n",
    "z= [[],[]]\n",
    "ind=-1\n",
    "for i in ru_ls:\n",
    "    print(i)\n",
    "    ind +=1\n",
    "    print(ind)\n",
    "    for a in i:\n",
    "        print(a,'-------')\n",
    "        for synset in wn.synsets(a):\n",
    "            for lemma in synset.lemmas():\n",
    "                z[ind].append(lemma.name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 2, ..., 1, 1, 4],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "U, s, V = np.linalg.svd(tdm, full_matrices=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dfU = pd.DataFrame(U, index=vectorizer.get_feature_names() )\n",
    "dfV = pd.DataFrame(V)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abbreviated</th>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about</th>\n",
       "      <td>0.004000</td>\n",
       "      <td>-0.004269</td>\n",
       "      <td>0.046473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>academic</th>\n",
       "      <td>0.002673</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>academicdegree</th>\n",
       "      <td>0.002673</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>academy</th>\n",
       "      <td>-0.000007</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acap</th>\n",
       "      <td>-0.000282</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.004552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceptance</th>\n",
       "      <td>-0.000059</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>access</th>\n",
       "      <td>-0.000127</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.001816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accommodation</th>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accordance</th>\n",
       "      <td>-0.001590</td>\n",
       "      <td>0.004204</td>\n",
       "      <td>0.009933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>according</th>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001634</td>\n",
       "      <td>0.017649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <td>-0.008480</td>\n",
       "      <td>-0.042358</td>\n",
       "      <td>0.137140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>-0.001315</td>\n",
       "      <td>0.003521</td>\n",
       "      <td>0.005696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achieve</th>\n",
       "      <td>-0.000709</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.010014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achievement</th>\n",
       "      <td>-0.000118</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.001669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>achieving</th>\n",
       "      <td>-0.001300</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.018359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>act</th>\n",
       "      <td>-0.004306</td>\n",
       "      <td>-0.030823</td>\n",
       "      <td>-0.005591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activities</th>\n",
       "      <td>-0.001345</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.019229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activity</th>\n",
       "      <td>0.010824</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.034297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acts</th>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>additional</th>\n",
       "      <td>-0.000403</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.003539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>additionally</th>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.000022</td>\n",
       "      <td>0.000645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <td>-0.000666</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.021446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adjust</th>\n",
       "      <td>-0.000074</td>\n",
       "      <td>-0.000105</td>\n",
       "      <td>0.000537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>administrative</th>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>-0.000211</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.002063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admission</th>\n",
       "      <td>-0.000898</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>-0.001309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adopt</th>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adoption</th>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.003141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>when</th>\n",
       "      <td>-0.000871</td>\n",
       "      <td>-0.005810</td>\n",
       "      <td>-0.002860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>where</th>\n",
       "      <td>0.004986</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>-0.001315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>which</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.007127</td>\n",
       "      <td>0.019795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>who</th>\n",
       "      <td>0.002673</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whole</th>\n",
       "      <td>-0.001332</td>\n",
       "      <td>0.008570</td>\n",
       "      <td>-0.011885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whose</th>\n",
       "      <td>-0.000037</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>-0.014664</td>\n",
       "      <td>-0.081706</td>\n",
       "      <td>-0.011522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>withdrawal</th>\n",
       "      <td>-0.003844</td>\n",
       "      <td>-0.037685</td>\n",
       "      <td>-0.025314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>within</th>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>without</th>\n",
       "      <td>-0.000937</td>\n",
       "      <td>0.002079</td>\n",
       "      <td>-0.001105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work</th>\n",
       "      <td>0.003245</td>\n",
       "      <td>0.003169</td>\n",
       "      <td>0.023773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worked</th>\n",
       "      <td>0.002673</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worker</th>\n",
       "      <td>-0.000036</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working</th>\n",
       "      <td>-0.000074</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.001374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>works</th>\n",
       "      <td>-0.000565</td>\n",
       "      <td>0.000865</td>\n",
       "      <td>0.006648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop</th>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrap</th>\n",
       "      <td>-0.000141</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.002276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>write</th>\n",
       "      <td>-0.001524</td>\n",
       "      <td>-0.010168</td>\n",
       "      <td>-0.005005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>written</th>\n",
       "      <td>-0.000218</td>\n",
       "      <td>-0.001453</td>\n",
       "      <td>-0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>www</th>\n",
       "      <td>0.005346</td>\n",
       "      <td>-0.000275</td>\n",
       "      <td>-0.000758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xml</th>\n",
       "      <td>0.163056</td>\n",
       "      <td>-0.008403</td>\n",
       "      <td>-0.023121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xmlns</th>\n",
       "      <td>0.010692</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>-0.001516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xmlschema</th>\n",
       "      <td>0.002673</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xs</th>\n",
       "      <td>0.816325</td>\n",
       "      <td>-0.039208</td>\n",
       "      <td>-0.096831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>-0.033269</td>\n",
       "      <td>-0.389915</td>\n",
       "      <td>-0.122441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <td>-0.000238</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.002548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>-0.000403</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.003539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>your</th>\n",
       "      <td>-0.000080</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yyyy</th>\n",
       "      <td>-0.000094</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>-0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>сonsecutive</th>\n",
       "      <td>-0.000562</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>0.010890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1185 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       1         2         3\n",
       "abbreviated    -0.000039  0.000142  0.001162\n",
       "about           0.004000 -0.004269  0.046473\n",
       "academic        0.002673 -0.000138 -0.000379\n",
       "academicdegree  0.002673 -0.000138 -0.000379\n",
       "academy        -0.000007  0.000041  0.000819\n",
       "acap           -0.000282  0.000304  0.004552\n",
       "acceptance     -0.000059  0.000012  0.000767\n",
       "access         -0.000127  0.000146  0.001816\n",
       "accommodation  -0.000107  0.000081  0.001325\n",
       "accordance     -0.001590  0.004204  0.009933\n",
       "according      -0.001517  0.001634  0.017649\n",
       "account        -0.008480 -0.042358  0.137140\n",
       "accounting     -0.001315  0.003521  0.005696\n",
       "achieve        -0.000709  0.000968  0.010014\n",
       "achievement    -0.000118  0.000161  0.001669\n",
       "achieving      -0.001300  0.001775  0.018359\n",
       "act            -0.004306 -0.030823 -0.005591\n",
       "action          0.000196  0.000140  0.000968\n",
       "activities     -0.001345  0.001547  0.019229\n",
       "activity        0.010824  0.002650  0.034297\n",
       "acts           -0.000030  0.000006  0.000384\n",
       "additional     -0.000403  0.000575  0.003539\n",
       "additionally   -0.000061 -0.000022  0.000645\n",
       "address        -0.000666  0.001850  0.021446\n",
       "adjust         -0.000074 -0.000105  0.000537\n",
       "administrative  0.000196  0.000140  0.000968\n",
       "administrator  -0.000211  0.000510  0.002063\n",
       "admission      -0.000898  0.002585 -0.001309\n",
       "adopt          -0.000029  0.000040  0.000608\n",
       "adoption       -0.000045  0.000243  0.003141\n",
       "...                  ...       ...       ...\n",
       "when           -0.000871 -0.005810 -0.002860\n",
       "where           0.004986  0.000659 -0.001315\n",
       "which           0.000096  0.007127  0.019795\n",
       "who             0.002673 -0.000138 -0.000379\n",
       "whole          -0.001332  0.008570 -0.011885\n",
       "whose          -0.000037  0.000119  0.000687\n",
       "with           -0.014664 -0.081706 -0.011522\n",
       "withdrawal     -0.003844 -0.037685 -0.025314\n",
       "within         -0.000004  0.000018  0.000110\n",
       "without        -0.000937  0.002079 -0.001105\n",
       "work            0.003245  0.003169  0.023773\n",
       "worked          0.002673 -0.000138 -0.000379\n",
       "worker         -0.000036  0.000051  0.000376\n",
       "working        -0.000074  0.000238  0.001374\n",
       "works          -0.000565  0.000865  0.006648\n",
       "workshop       -0.000004  0.000018  0.000110\n",
       "wrap           -0.000141  0.000152  0.002276\n",
       "write          -0.001524 -0.010168 -0.005005\n",
       "written        -0.000218 -0.001453 -0.000715\n",
       "www             0.005346 -0.000275 -0.000758\n",
       "xml             0.163056 -0.008403 -0.023121\n",
       "xmlns           0.010692 -0.000551 -0.001516\n",
       "xmlschema       0.002673 -0.000138 -0.000379\n",
       "xs              0.816325 -0.039208 -0.096831\n",
       "year           -0.033269 -0.389915 -0.122441\n",
       "years          -0.000238  0.000137  0.002548\n",
       "yes            -0.000403  0.000575  0.003539\n",
       "your           -0.000080  0.000053  0.000944\n",
       "yyyy           -0.000094  0.000464 -0.000038\n",
       "сonsecutive    -0.000562  0.000648  0.010890\n",
       "\n",
       "[1185 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfU[[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.007257</td>\n",
       "      <td>0.034809</td>\n",
       "      <td>0.035193</td>\n",
       "      <td>-0.013210</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>-0.009688</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.006397</td>\n",
       "      <td>-0.006713</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005444</td>\n",
       "      <td>-0.048055</td>\n",
       "      <td>-0.037256</td>\n",
       "      <td>0.000960</td>\n",
       "      <td>-0.003635</td>\n",
       "      <td>-0.017434</td>\n",
       "      <td>-0.008460</td>\n",
       "      <td>-0.002718</td>\n",
       "      <td>-0.005527</td>\n",
       "      <td>-0.009607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.020590</td>\n",
       "      <td>0.007395</td>\n",
       "      <td>-0.010482</td>\n",
       "      <td>-0.011348</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>-0.004217</td>\n",
       "      <td>0.005101</td>\n",
       "      <td>0.002313</td>\n",
       "      <td>0.002308</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002762</td>\n",
       "      <td>0.038221</td>\n",
       "      <td>-0.010987</td>\n",
       "      <td>0.007644</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>-0.009073</td>\n",
       "      <td>0.025682</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.009241</td>\n",
       "      <td>0.005604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "1 -0.007257  0.034809  0.035193 -0.013210 -0.002468 -0.009688 -0.004063   \n",
       "2  0.005040  0.020590  0.007395 -0.010482 -0.011348  0.000039 -0.004217   \n",
       "\n",
       "        7         8         9      ...          95        96        97   \\\n",
       "1 -0.006397 -0.006713 -0.007762    ...    -0.005444 -0.048055 -0.037256   \n",
       "2  0.005101  0.002313  0.002308    ...    -0.002762  0.038221 -0.010987   \n",
       "\n",
       "        98        99        100       101       102       103       104  \n",
       "1  0.000960 -0.003635 -0.017434 -0.008460 -0.002718 -0.005527 -0.009607  \n",
       "2  0.007644  0.000457 -0.009073  0.025682  0.007112  0.009241  0.005604  \n",
       "\n",
       "[2 rows x 105 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfV.iloc[[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "#from pandas.tools.plotting import scatter_matrix\n",
    "#scatter_matrix(dfV, alpha=0.1, figsize=(12, 12), diagonal='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f1(x):\n",
    "    if abs(x)<0.1: \n",
    "        return 0  \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Выше показаны ключевые образы их три 1-ая компонента самая сильная, вторая послабей и последняя самая слабая.\n",
    "Действительно текст первой компоненты повторяется чаще всего."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, -0.57735026918962573, 0, 0, -0.81649658092772615, 0, 0, 0]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(f1,dfV[0] ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Первая компонента говорит, что  стоит взять 2 и 5 \n",
    "предложения - остальные похожи на эти два, \n",
    "скорее всего или на одно или на другое "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt('nump.out', tdm, delimiter=',',fmt = )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO  \n",
    "numps = StringIO(\"0 1\\n2 3\")\n",
    "np.loadtxt(numps).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, -0.57735026918962584, 0, 0.70441146084575823, 0.40824829046386291, 0, 0, 0]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KMeans()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В третье компоненте есть различия. Это  показывает что слова образуют две устойчивые группы по повтояемости, то есть две темы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(`.${CLASS_NAME.split(' ')[0]}`);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1eaf69d6-24fe-4ad8-a2ec-f793d4e15b5f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32mfakesource\u001b[0m in \u001b[0;36mcircle\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n",
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      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/models/plots.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;31m# Want to pass selector that is a dictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_list_attr_splat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreferences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/model.py\u001b[0m in \u001b[0;36mreferences\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m         '''\n\u001b[0;32m--> 423\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollect_models\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[0m\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/model.py\u001b[0m in \u001b[0;36mcollect_models\u001b[0;34m(*input_values)\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0mids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0mcollected\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m             \u001b[0m_visit_immediate_value_references\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqueue_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcollected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/model.py\u001b[0m in \u001b[0;36m_visit_immediate_value_references\u001b[0;34m(value, visitor)\u001b[0m\n\u001b[1;32m    685\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHasProps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperties_with_refs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             \u001b[0mchild\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    688\u001b[0m             \u001b[0m_visit_value_and_its_immediate_references\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/core/property/descriptors.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, owner)\u001b[0m\n\u001b[1;32m    455\u001b[0m         '''\n\u001b[1;32m    456\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mowner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/core/property/descriptors.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_property_values\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_property_values\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;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/core/property/descriptors.py\u001b[0m in \u001b[0;36m_get_default\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    668\u001b[0m         \u001b[0mis_themed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthemed_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthemed_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m         \u001b[0mdefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstance_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_themed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/bokeh/core/property/descriptors.py\u001b[0m in \u001b[0;36minstance_default\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         '''\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthemed_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\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[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthemed_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_from_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msetter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import bokeh.plotting\n",
    "\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "bokeh.plotting.output_notebook()  \n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "petal_data = tdm \n",
    "\n",
    "kmean = KMeans() #n_clusters asks for only 3 groupings\n",
    "\n",
    "kmean.fit(petal_data) \n",
    "\n",
    "plot = figure(width=500, height=500, title='Ru', x_axis_label = \"X\", y_axis_label = \"Y\")\n",
    "\n",
    "#plot centroid / cluster center / group mean for each group\n",
    "\n",
    "clus_xs = []\n",
    "\n",
    "clus_ys = []\n",
    "\n",
    "#we get the  cluster x / y values from the k-means algorithm\n",
    "\n",
    "for entry in kmean.cluster_centers_:\n",
    "\n",
    "   clus_xs.append(entry[0])\n",
    "\n",
    "   clus_ys.append(entry[1])\n",
    "\n",
    "#the cluster center is marked by a circle, with a cross in it\n",
    "\n",
    "plot.circle_cross(x=clus_xs, y=clus_ys, size=40, fill_alpha=0, line_width=2, )\n",
    "\n",
    "plot.text(x=clus_xs, y=clus_ys, text_font_size='30pt')\n",
    "\n",
    "i = 0 #counter\n",
    "\n",
    " \n",
    "\n",
    "for sample in petal_data:\n",
    "\n",
    " \n",
    "   if kmean.labels_[i] == 0:\n",
    "        plot.circle(x=sample[0], y=sample[1], size=15, color=\"red\")\n",
    "\n",
    "   if kmean.labels_[i] == 1:\n",
    "    plot.circle(x=sample[0], y=sample[1], size=15, color=\"blue\")\n",
    "\n",
    "   if kmean.labels_[i] == 2:\n",
    "    plot.circle(x=sample[0], y=sample[1], size=15, color=\"purple\")\n",
    "\n",
    "   i += 1\n",
    "\n",
    "bokeh.plotting.show(plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"779d1b0c-3855-42ee-92ee-04d67958aaf0\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(`.${CLASS_NAME.split(' ')[0]}`);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '779d1b0c-3855-42ee-92ee-04d67958aaf0' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '779d1b0c-3855-42ee-92ee-04d67958aaf0' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"779d1b0c-3855-42ee-92ee-04d67958aaf0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import bokeh.plotting\n",
    "\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "bokeh.plotting.output_notebook()  \n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "petal_data = iris.data[:,2:] \n",
    "\n",
    "kmean = KMeans(n_clusters=3) #n_clusters asks for only 3 groupings\n",
    "\n",
    "kmean.fit(petal_data) \n",
    "\n",
    "plot = figure(width=500, height=500, title='Iris Petals', x_axis_label = \"Petal Length\", y_axis_label = \"Petal Width\")\n",
    "\n",
    "#plot centroid / cluster center / group mean for each group\n",
    "\n",
    "clus_xs = []\n",
    "\n",
    "clus_ys = []\n",
    "\n",
    "#we get the  cluster x / y values from the k-means algorithm\n",
    "\n",
    "for entry in kmean.cluster_centers_:\n",
    "\n",
    "   clus_xs.append(entry[0])\n",
    "\n",
    "   clus_ys.append(entry[1])\n",
    "\n",
    "#the cluster center is marked by a circle, with a cross in it\n",
    "\n",
    "plot.circle_cross(x=clus_xs, y=clus_ys, size=40, fill_alpha=0, line_width=2, color=['red', 'blue', 'purple'])\n",
    "\n",
    "plot.text(text = ['setosa', 'versicolor', 'virginica'], x=clus_xs, y=clus_ys, text_font_size='30pt')\n",
    "\n",
    "i = 0 #counter\n",
    "\n",
    " \n",
    "\n",
    "for sample in petal_data:\n",
    "\n",
    " \n",
    "   if kmean.labels_[i] == 0:\n",
    "        plot.circle(x=sample[0], y=sample[1], size=15, color=\"red\")\n",
    "\n",
    "   if kmean.labels_[i] == 1:\n",
    "    plot.circle(x=sample[0], y=sample[1], size=15, color=\"blue\")\n",
    "\n",
    "   if kmean.labels_[i] == 2:\n",
    "    plot.circle(x=sample[0], y=sample[1], size=15, color=\"purple\")\n",
    "\n",
    "   i += 1\n",
    "\n",
    "bokeh.plotting.show(plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'iris' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-7351ba6f3316>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0miris\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'iris' is not defined"
     ]
    }
   ],
   "source": [
    "iris.data[:,2:] "
   ]
  }
 ],
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