Задание 6.ipynb

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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "df = pd.read_csv('svm-data.csv', header=None).as_matrix()\n",
    "X, y = df[:, 1:], df[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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     "data": {
      "text/plain": [
       "SVC(C=100000, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=241, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(C=100000, random_state=241, kernel='linear')\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 9], dtype=int32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('answer.txt', 'w') as f:\n",
    "    f.write('4,5,10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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