Python65-数据挖掘与机器学习作业（六）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![作业](https://upload-images.jianshu.io/upload_images/1531909-2265d329f2ddd494.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 问题1 K-menas实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>V2</th>\n",
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       "      <td>-0.887</td>\n",
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       "      <td>-0.033</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.762</td>\n",
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       "      <td>0.803</td>\n",
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       "      <td>-0.862</td>\n",
       "      <td>-0.871</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
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       "      <td>-0.041</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-1.0</td>\n",
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       "      <td>-1.000</td>\n",
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       "      <td>0.966</td>\n",
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       "      <td>-1.000</td>\n",
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       "      <td>-1.000</td>\n",
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       "     V2   V3   V4     V5     V6     V7     V8     V9    V10    V11  ...   \\\n",
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       "31 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -0.270  0.973  ...    \n",
       "39 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.912  ...    \n",
       "50 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -0.701  0.708 -0.722  ...    \n",
       "55 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000  0.023  0.782  ...    \n",
       "56 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -0.227  0.825  ...    \n",
       "89 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -0.767  0.209  0.954  0.752  ...    \n",
       "91 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -0.987 -0.487  0.307  0.897  ...    \n",
       "\n",
       "     V248   V249   V250   V251   V252   V253   V254   V255   V256  V257  \n",
       "0   0.304  0.823  1.000  0.482 -0.474 -0.991 -1.000 -1.000 -1.000  -1.0  \n",
       "1  -0.671 -0.671 -0.033  0.761  0.762  0.126 -0.095 -0.671 -0.828  -1.0  \n",
       "5   0.803  0.015 -0.862 -0.871 -0.437 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "31 -0.041  0.628  0.573 -0.184 -0.891 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "39 -0.993 -0.257  0.480  0.966  0.408 -0.895 -1.000 -1.000 -1.000  -1.0  \n",
       "50 -0.479  0.883 -0.249 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "55 -0.727  0.610  1.000  0.472 -0.864 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "56 -0.852  0.260  0.987  0.375 -0.946 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "89  1.000  0.738  0.181 -0.565 -0.999 -1.000 -1.000 -1.000 -1.000  -1.0  \n",
       "91  0.704  0.360  0.360  0.268 -0.280 -0.757 -0.995 -1.000 -1.000  -1.0  \n",
       "\n",
       "[10 rows x 256 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择数字为5或6的列  形成训练集train\n",
    "or_train=pd.read_csv('files/data/python64-data/zip_train.csv')\n",
    "or_train=or_train.iloc[:,1:]\n",
    "train=or_train[or_train['V1'].isin([5,6])]\n",
    "X_train=train.iloc[:,1:] # 去除第一列\n",
    "y_train=train.iloc[:,0]\n",
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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     "execution_count": 65,
     "metadata": {},
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    "X_train.shape"
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       "      <td>-0.369</td>\n",
       "      <td>-0.867</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.761</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.691</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.679</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>-0.907</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.926</td>\n",
       "      <td>0.138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>0.391</td>\n",
       "      <td>-0.560</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.612</td>\n",
       "      <td>0.992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.179</td>\n",
       "      <td>-0.298</td>\n",
       "      <td>-0.785</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.879</td>\n",
       "      <td>-0.441</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.933</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.371</td>\n",
       "      <td>-0.397</td>\n",
       "      <td>-0.949</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     V2   V3   V4   V5     V6     V7     V8     V9    V10    V11  ...    V248  \\\n",
       "1  -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  ...  -1.000   \n",
       "3  -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  ...  -1.000   \n",
       "4  -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -0.858 -0.106  0.802  ...   0.901   \n",
       "8  -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -0.761  0.438  0.691  ...   1.000   \n",
       "10 -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -0.926  0.138  ...   0.795   \n",
       "17 -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -0.887  0.391 -0.560 -1.000  ...  -1.000   \n",
       "19 -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -0.612  0.992  ...   0.999   \n",
       "24 -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -0.307 -0.169 -1.000  ...  -0.185   \n",
       "32 -1.0 -1.0 -1.0 -1.0 -0.933  0.461  0.779  0.584  0.584  0.584  ...   1.000   \n",
       "36 -1.0 -1.0 -1.0 -1.0 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  ...  -1.000   \n",
       "\n",
       "     V249   V250   V251   V252   V253  V254  V255  V256  V257  \n",
       "1  -1.000 -1.000 -1.000 -1.000 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "3  -1.000 -1.000 -1.000 -1.000 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "4   0.901  0.901  0.290 -0.369 -0.867  -1.0  -1.0  -1.0  -1.0  \n",
       "8   1.000  0.679 -0.017 -0.907 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "10  1.000  1.000  0.908  0.201 -0.600  -1.0  -1.0  -1.0  -1.0  \n",
       "17 -1.000 -1.000 -1.000 -1.000 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "19  1.000  0.540  0.179 -0.298 -0.785  -1.0  -1.0  -1.0  -1.0  \n",
       "24  0.086  0.879 -0.441 -1.000 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "32  0.857  0.371 -0.397 -0.949 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "36 -1.000 -1.000 -1.000 -1.000 -1.000  -1.0  -1.0  -1.0  -1.0  \n",
       "\n",
       "[10 rows x 256 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# 选择值为5或6的列,形成测试集test\n",
    "original_test=pd.read_csv('files/data/python64-data/zip_test.csv')\n",
    "original_test=original_test.iloc[:,1:]\n",
    "test=original_test[original_test['V1'].isin([5,6])]\n",
    "\n",
    "# 提出test\n",
    "encoder=LabelEncoder()\n",
    "y_test=encoder.fit_transform(test.iloc[:,0])\n",
    "print(y_test)\n",
    "\n",
    "X_test=test.iloc[:,1:]\n",
    "X_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(330, 256)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K-means 实现\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.externals import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 类簇的数量\n",
    "n_clusters=2\n",
    "# 将训练集和类簇数量加入到聚类函数中\n",
    "cls=KMeans(n_clusters).fit(X_train)\n",
    "print(cls.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0\n",
      " 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0 0 1 0 1 0 1 0\n",
      " 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1\n",
      " 1 1 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0\n",
      " 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0 0 0\n",
      " 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1\n",
      " 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0\n",
      " 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0]\n",
      "[0 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1 0\n",
      " 1 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1\n",
      " 1 1 0 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0\n",
      " 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0\n",
      " 0 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1\n",
      " 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0\n",
      " 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 0\n",
      " 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 预测结果\n",
    "pred=cls.predict(X_test)\n",
    "print(y_test)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8272727272727273"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测准确率\n",
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  问题2 混合高斯分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1220, 256) (1220,) (330, 256) (330,)\n"
     ]
    }
   ],
   "source": [
    "# 导入sklearn包\n",
    "from sklearn.mixture import GaussianMixture\n",
    "# 导入数据\n",
    "X_train,y_train,X_test,y_test=X_train,y_train,X_test,y_test\n",
    "print(X_train.shape,y_train.shape,X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1 0\n",
      " 1 0 1 1 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1\n",
      " 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 0 0\n",
      " 1 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0\n",
      " 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1\n",
      " 0 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0\n",
      " 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "clf=GaussianMixture(n_components=2)\n",
    "clf.fit(X_train)\n",
    "y_pred=clf.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.806060606060606\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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