E17_KPCA-FaultDetection_with new eigvals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc5c4d51",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a945f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import seaborn as sns\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6e1732",
   "metadata": {},
   "source": [
    "# The Datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcac490a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_normal = pd.read_csv('./data/normal_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cbaef6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_faulty = pd.read_csv('./data/faulty_testing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "842eb7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preparing\n",
    "df_train = train_normal[(train_normal.simulationRun>=1)].iloc[:500,3:]\n",
    "\n",
    "df_test = train_normal[(train_normal.simulationRun>5)&(train_normal.simulationRun<10)].iloc[:,3:].reset_index(drop=True)\n",
    "# IDV = 1\n",
    "# df_test = test_faulty[(test_faulty.faultNumber==IDV) & \n",
    "#                                (test_faulty.simulationRun==1)].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55e44e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25038</td>\n",
       "      <td>3674.0</td>\n",
       "      <td>4529.0</td>\n",
       "      <td>9.2320</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.402</td>\n",
       "      <td>2704.3</td>\n",
       "      <td>74.863</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33818</td>\n",
       "      <td>...</td>\n",
       "      <td>53.744</td>\n",
       "      <td>24.657</td>\n",
       "      <td>62.544</td>\n",
       "      <td>22.137</td>\n",
       "      <td>39.935</td>\n",
       "      <td>42.323</td>\n",
       "      <td>47.757</td>\n",
       "      <td>47.510</td>\n",
       "      <td>41.258</td>\n",
       "      <td>18.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25109</td>\n",
       "      <td>3659.4</td>\n",
       "      <td>4556.6</td>\n",
       "      <td>9.4264</td>\n",
       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>75.000</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33620</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
       "      <td>59.259</td>\n",
       "      <td>22.084</td>\n",
       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
       "      <td>17.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>74.771</td>\n",
       "      <td>120.42</td>\n",
       "      <td>0.33563</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24977</td>\n",
       "      <td>3661.3</td>\n",
       "      <td>4512.1</td>\n",
       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>75.224</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.33553</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>75.388</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.32632</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   xmeas_1  xmeas_2  xmeas_3  xmeas_4  xmeas_5  xmeas_6  xmeas_7  xmeas_8  \\\n",
       "0  0.25038   3674.0   4529.0   9.2320   26.889   42.402   2704.3   74.863   \n",
       "1  0.25109   3659.4   4556.6   9.4264   26.721   42.576   2705.0   75.000   \n",
       "2  0.25038   3660.3   4477.8   9.4426   26.875   42.070   2706.2   74.771   \n",
       "3  0.24977   3661.3   4512.1   9.4776   26.758   42.063   2707.2   75.224   \n",
       "4  0.29405   3679.0   4497.0   9.3381   26.889   42.650   2705.1   75.388   \n",
       "\n",
       "   xmeas_9  xmeas_10  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6   xmv_7  \\\n",
       "0   120.41   0.33818  ...  53.744  24.657  62.544  22.137  39.935  42.323   \n",
       "1   120.41   0.33620  ...  53.414  24.588  59.259  22.084  40.176  38.554   \n",
       "2   120.42   0.33563  ...  54.357  24.666  61.275  22.380  40.244  38.990   \n",
       "3   120.39   0.33553  ...  53.946  24.725  59.856  22.277  40.257  38.072   \n",
       "4   120.39   0.32632  ...  53.658  28.797  60.717  21.947  39.144  41.955   \n",
       "\n",
       "    xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  47.757  47.510  41.258  18.447  \n",
       "1  43.692  47.427  41.359  17.194  \n",
       "2  46.699  47.468  41.199  20.530  \n",
       "3  47.541  47.658  41.643  18.089  \n",
       "4  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e5479b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25025</td>\n",
       "      <td>3676.1</td>\n",
       "      <td>4518.7</td>\n",
       "      <td>9.3769</td>\n",
       "      <td>26.748</td>\n",
       "      <td>42.130</td>\n",
       "      <td>2704.3</td>\n",
       "      <td>75.375</td>\n",
       "      <td>120.42</td>\n",
       "      <td>0.33850</td>\n",
       "      <td>...</td>\n",
       "      <td>53.867</td>\n",
       "      <td>24.670</td>\n",
       "      <td>62.322</td>\n",
       "      <td>22.375</td>\n",
       "      <td>39.896</td>\n",
       "      <td>36.040</td>\n",
       "      <td>47.321</td>\n",
       "      <td>47.384</td>\n",
       "      <td>41.161</td>\n",
       "      <td>15.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25331</td>\n",
       "      <td>3639.9</td>\n",
       "      <td>4487.0</td>\n",
       "      <td>9.3852</td>\n",
       "      <td>27.153</td>\n",
       "      <td>42.877</td>\n",
       "      <td>2704.6</td>\n",
       "      <td>74.702</td>\n",
       "      <td>120.37</td>\n",
       "      <td>0.33614</td>\n",
       "      <td>...</td>\n",
       "      <td>54.247</td>\n",
       "      <td>24.370</td>\n",
       "      <td>59.331</td>\n",
       "      <td>22.425</td>\n",
       "      <td>40.183</td>\n",
       "      <td>38.570</td>\n",
       "      <td>51.266</td>\n",
       "      <td>47.403</td>\n",
       "      <td>40.431</td>\n",
       "      <td>15.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25096</td>\n",
       "      <td>3667.0</td>\n",
       "      <td>4481.4</td>\n",
       "      <td>9.3008</td>\n",
       "      <td>26.742</td>\n",
       "      <td>42.349</td>\n",
       "      <td>2706.5</td>\n",
       "      <td>76.107</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33515</td>\n",
       "      <td>...</td>\n",
       "      <td>54.313</td>\n",
       "      <td>24.608</td>\n",
       "      <td>60.264</td>\n",
       "      <td>22.032</td>\n",
       "      <td>40.303</td>\n",
       "      <td>40.878</td>\n",
       "      <td>52.589</td>\n",
       "      <td>47.485</td>\n",
       "      <td>42.107</td>\n",
       "      <td>19.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24879</td>\n",
       "      <td>3676.8</td>\n",
       "      <td>4536.7</td>\n",
       "      <td>9.3483</td>\n",
       "      <td>26.752</td>\n",
       "      <td>42.211</td>\n",
       "      <td>2706.3</td>\n",
       "      <td>75.858</td>\n",
       "      <td>120.38</td>\n",
       "      <td>0.33705</td>\n",
       "      <td>...</td>\n",
       "      <td>53.651</td>\n",
       "      <td>24.822</td>\n",
       "      <td>61.995</td>\n",
       "      <td>22.102</td>\n",
       "      <td>40.071</td>\n",
       "      <td>40.130</td>\n",
       "      <td>40.554</td>\n",
       "      <td>47.630</td>\n",
       "      <td>41.308</td>\n",
       "      <td>18.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.23790</td>\n",
       "      <td>3652.6</td>\n",
       "      <td>4492.4</td>\n",
       "      <td>9.2783</td>\n",
       "      <td>27.071</td>\n",
       "      <td>42.767</td>\n",
       "      <td>2704.7</td>\n",
       "      <td>74.040</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.32679</td>\n",
       "      <td>...</td>\n",
       "      <td>53.430</td>\n",
       "      <td>23.416</td>\n",
       "      <td>60.937</td>\n",
       "      <td>22.235</td>\n",
       "      <td>38.458</td>\n",
       "      <td>39.328</td>\n",
       "      <td>43.594</td>\n",
       "      <td>47.543</td>\n",
       "      <td>41.020</td>\n",
       "      <td>19.733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
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      ],
      "text/plain": [
       "   xmeas_1  xmeas_2  xmeas_3  xmeas_4  xmeas_5  xmeas_6  xmeas_7  xmeas_8  \\\n",
       "0  0.25025   3676.1   4518.7   9.3769   26.748   42.130   2704.3   75.375   \n",
       "1  0.25331   3639.9   4487.0   9.3852   27.153   42.877   2704.6   74.702   \n",
       "2  0.25096   3667.0   4481.4   9.3008   26.742   42.349   2706.5   76.107   \n",
       "3  0.24879   3676.8   4536.7   9.3483   26.752   42.211   2706.3   75.858   \n",
       "4  0.23790   3652.6   4492.4   9.2783   27.071   42.767   2704.7   74.040   \n",
       "\n",
       "   xmeas_9  xmeas_10  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6   xmv_7  \\\n",
       "0   120.42   0.33850  ...  53.867  24.670  62.322  22.375  39.896  36.040   \n",
       "1   120.37   0.33614  ...  54.247  24.370  59.331  22.425  40.183  38.570   \n",
       "2   120.41   0.33515  ...  54.313  24.608  60.264  22.032  40.303  40.878   \n",
       "3   120.38   0.33705  ...  53.651  24.822  61.995  22.102  40.071  40.130   \n",
       "4   120.41   0.32679  ...  53.430  23.416  60.937  22.235  38.458  39.328   \n",
       "\n",
       "    xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  47.321  47.384  41.161  15.931  \n",
       "1  51.266  47.403  40.431  15.211  \n",
       "2  52.589  47.485  42.107  19.123  \n",
       "3  40.554  47.630  41.308  18.192  \n",
       "4  43.594  47.543  41.020  19.733  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d9a669",
   "metadata": {},
   "source": [
    "## Scaling Training Datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0409589a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scl_pick(name, data):\n",
    "    \"\"\"\n",
    "    the several option for name:\n",
    "    -std\n",
    "    -minmax\n",
    "    -robust\n",
    "    -norm\n",
    "    -pwr\n",
    "    \"\"\"\n",
    "    if name == 'std':\n",
    "        std_scl = StandardScaler().fit(data)\n",
    "        df_scl = std_scl.transform(data)\n",
    "        df_scl = pd.DataFrame(df_scl)\n",
    "    elif name == 'minmax':\n",
    "        MM_scl = MinMaxScaler().fit(data)\n",
    "        df_scl = MM_scl.transform(data)\n",
    "        df_scl = pd.DataFrame(df_scl)\n",
    "    elif name == 'robust':\n",
    "        rb_scl = RobustScaler().fit(data)\n",
    "        df_scl = rb_scl.transform(data)\n",
    "        df_scl = pd.DataFrame(df_scl)\n",
    "    elif name == 'norm': ##### xxxxxx\n",
    "        df_norm = Normalizer().fit(data)\n",
    "        df_scl = df_norm.transform(data)\n",
    "        df_scl = pd.DataFrame(df_scl)\n",
    "    else:\n",
    "        pt_scl = PowerTransformer().fit(data)\n",
    "        df_scl = pt_scl.transform(data)\n",
    "        df_scl = pd.DataFrame(df_scl)\n",
    "    return df_scl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5878bef2",
   "metadata": {},
   "source": [
    "### Important Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0698776e",
   "metadata": {},
   "outputs": [],
   "source": [
    "nComponent = 16 # 30\n",
    "nSamples = df_train.shape[0]\n",
    "conf_T2 = 0.92 # 0.99 or 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1711636",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-std-minmax-robust-norm-pwr\n",
    "df_scl = scl_pick('std', df_train) #robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbe9fca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_scl = round(df_scl, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6514ebcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>0.343859</td>\n",
       "      <td>0.502126</td>\n",
       "      <td>-1.431359</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>0.278542</td>\n",
       "      <td>-0.009557</td>\n",
       "      <td>-0.292773</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>0.098431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.488936</td>\n",
       "      <td>0.000962</td>\n",
       "      <td>1.050090</td>\n",
       "      <td>-0.051818</td>\n",
       "      <td>-0.122736</td>\n",
       "      <td>1.319983</td>\n",
       "      <td>0.471559</td>\n",
       "      <td>-0.720571</td>\n",
       "      <td>0.279630</td>\n",
       "      <td>0.250397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017875</td>\n",
       "      <td>-0.103316</td>\n",
       "      <td>1.215653</td>\n",
       "      <td>1.035368</td>\n",
       "      <td>-0.866377</td>\n",
       "      <td>1.068875</td>\n",
       "      <td>0.107804</td>\n",
       "      <td>-0.032734</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>-0.071378</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.237481</td>\n",
       "      <td>-0.024152</td>\n",
       "      <td>-1.590601</td>\n",
       "      <td>-0.185906</td>\n",
       "      <td>0.049624</td>\n",
       "      <td>0.089123</td>\n",
       "      <td>-1.227605</td>\n",
       "      <td>-0.760505</td>\n",
       "      <td>0.460598</td>\n",
       "      <td>-0.634866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>-0.075750</td>\n",
       "      <td>-0.821517</td>\n",
       "      <td>1.240929</td>\n",
       "      <td>-0.149278</td>\n",
       "      <td>-1.229449</td>\n",
       "      <td>0.308995</td>\n",
       "      <td>-0.467397</td>\n",
       "      <td>1.023929</td>\n",
       "      <td>-0.120262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.901543</td>\n",
       "      <td>0.004238</td>\n",
       "      <td>0.029987</td>\n",
       "      <td>0.562959</td>\n",
       "      <td>0.098256</td>\n",
       "      <td>0.231510</td>\n",
       "      <td>0.029317</td>\n",
       "      <td>-0.740779</td>\n",
       "      <td>0.173916</td>\n",
       "      <td>1.722067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.029225</td>\n",
       "      <td>-0.045122</td>\n",
       "      <td>0.065220</td>\n",
       "      <td>1.685042</td>\n",
       "      <td>-0.694087</td>\n",
       "      <td>-1.261244</td>\n",
       "      <td>0.476653</td>\n",
       "      <td>0.392438</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.128838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030736</td>\n",
       "      <td>0.025713</td>\n",
       "      <td>-1.110695</td>\n",
       "      <td>0.302375</td>\n",
       "      <td>0.107554</td>\n",
       "      <td>-0.068286</td>\n",
       "      <td>0.381272</td>\n",
       "      <td>-0.649365</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>-0.002535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.550795</td>\n",
       "      <td>0.497001</td>\n",
       "      <td>-0.325151</td>\n",
       "      <td>-0.085064</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>1.404994</td>\n",
       "      <td>0.124570</td>\n",
       "      <td>0.703725</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.918704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.684011</td>\n",
       "      <td>1.507821</td>\n",
       "      <td>-0.418568</td>\n",
       "      <td>-0.532509</td>\n",
       "      <td>-0.688446</td>\n",
       "      <td>1.199804</td>\n",
       "      <td>0.424744</td>\n",
       "      <td>-0.799476</td>\n",
       "      <td>0.725778</td>\n",
       "      <td>0.260288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.007459  0.343859  0.502126 -1.431359 -0.084087  0.278542 -0.009557   \n",
       "1  0.017875 -0.103316  1.215653  1.035368 -0.866377  1.068875  0.107804   \n",
       "2 -0.007459 -0.075750 -0.821517  1.240929 -0.149278 -1.229449  0.308995   \n",
       "3 -0.029225 -0.045122  0.065220  1.685042 -0.694087 -1.261244  0.476653   \n",
       "4  1.550795  0.497001 -0.325151 -0.085064 -0.084087  1.404994  0.124570   \n",
       "\n",
       "         7         8         9   ...        42        43        44        45  \\\n",
       "0 -0.292773  0.510938  0.098431  ... -0.488936  0.000962  1.050090 -0.051818   \n",
       "1 -0.032734  0.510938 -0.071378  ... -1.237481 -0.024152 -1.590601 -0.185906   \n",
       "2 -0.467397  1.023929 -0.120262  ...  0.901543  0.004238  0.029987  0.562959   \n",
       "3  0.392438 -0.515042 -0.128838  ... -0.030736  0.025713 -1.110695  0.302375   \n",
       "4  0.703725 -0.515042 -0.918704  ... -0.684011  1.507821 -0.418568 -0.532509   \n",
       "\n",
       "         46        47        48        49        50        51  \n",
       "0 -0.122736  1.319983  0.471559 -0.720571  0.279630  0.250397  \n",
       "1  0.049624  0.089123 -1.227605 -0.760505  0.460598 -0.634866  \n",
       "2  0.098256  0.231510  0.029317 -0.740779  0.173916  1.722067  \n",
       "3  0.107554 -0.068286  0.381272 -0.649365  0.969458 -0.002535  \n",
       "4 -0.688446  1.199804  0.424744 -0.799476  0.725778  0.260288  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5709e9ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_scl)\n",
    "plt.title('data training scaling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8751e7c0",
   "metadata": {},
   "source": [
    "## Training Centraling kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "502e8ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training proccess\n",
    "A = df_scl\n",
    "# K = np.matmul(A, A.T)\n",
    "K = A@A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eb9dd91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(K)\n",
    "plt.title('K')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1a062b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = round(K, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9a35c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.2546</td>\n",
       "      <td>-1.0061</td>\n",
       "      <td>0.0674</td>\n",
       "      <td>-2.9111</td>\n",
       "      <td>7.1808</td>\n",
       "      <td>2.8010</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>1.4510</td>\n",
       "      <td>0.4063</td>\n",
       "      <td>1.5802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5930</td>\n",
       "      <td>3.8502</td>\n",
       "      <td>-9.1364</td>\n",
       "      <td>4.2919</td>\n",
       "      <td>-3.3995</td>\n",
       "      <td>-10.6717</td>\n",
       "      <td>-3.1036</td>\n",
       "      <td>5.0830</td>\n",
       "      <td>-1.1397</td>\n",
       "      <td>-2.0652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.0061</td>\n",
       "      <td>15.6481</td>\n",
       "      <td>-1.6439</td>\n",
       "      <td>5.6558</td>\n",
       "      <td>2.5903</td>\n",
       "      <td>-0.4408</td>\n",
       "      <td>6.0022</td>\n",
       "      <td>4.6069</td>\n",
       "      <td>-2.4341</td>\n",
       "      <td>2.6042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9103</td>\n",
       "      <td>1.0257</td>\n",
       "      <td>-2.7119</td>\n",
       "      <td>0.1541</td>\n",
       "      <td>0.3194</td>\n",
       "      <td>8.1394</td>\n",
       "      <td>5.3727</td>\n",
       "      <td>-5.6364</td>\n",
       "      <td>-8.4340</td>\n",
       "      <td>-1.2430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0674</td>\n",
       "      <td>-1.6439</td>\n",
       "      <td>26.7531</td>\n",
       "      <td>17.8784</td>\n",
       "      <td>-0.3144</td>\n",
       "      <td>5.1255</td>\n",
       "      <td>3.0532</td>\n",
       "      <td>9.6956</td>\n",
       "      <td>-6.1012</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0349</td>\n",
       "      <td>5.8823</td>\n",
       "      <td>9.4580</td>\n",
       "      <td>-6.5917</td>\n",
       "      <td>5.7670</td>\n",
       "      <td>-1.7374</td>\n",
       "      <td>-1.2486</td>\n",
       "      <td>-3.1189</td>\n",
       "      <td>-1.1444</td>\n",
       "      <td>-3.6523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.9111</td>\n",
       "      <td>5.6558</td>\n",
       "      <td>17.8784</td>\n",
       "      <td>28.0756</td>\n",
       "      <td>-1.2179</td>\n",
       "      <td>1.0285</td>\n",
       "      <td>7.3694</td>\n",
       "      <td>7.8788</td>\n",
       "      <td>-6.3967</td>\n",
       "      <td>-0.0440</td>\n",
       "      <td>...</td>\n",
       "      <td>4.3926</td>\n",
       "      <td>5.6318</td>\n",
       "      <td>7.7203</td>\n",
       "      <td>0.8116</td>\n",
       "      <td>2.4675</td>\n",
       "      <td>7.4404</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>-8.0281</td>\n",
       "      <td>-7.5140</td>\n",
       "      <td>1.3202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.1808</td>\n",
       "      <td>2.5903</td>\n",
       "      <td>-0.3144</td>\n",
       "      <td>-1.2179</td>\n",
       "      <td>40.4632</td>\n",
       "      <td>34.7838</td>\n",
       "      <td>-1.6998</td>\n",
       "      <td>-4.6655</td>\n",
       "      <td>5.3079</td>\n",
       "      <td>4.0269</td>\n",
       "      <td>...</td>\n",
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       "      <td>6.9229</td>\n",
       "      <td>-7.3993</td>\n",
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       "      <td>1.2561</td>\n",
       "      <td>-2.8091</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>7.9712</td>\n",
       "      <td>-3.6725</td>\n",
       "      <td>2.6617</td>\n",
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       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
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       "       0        1        2        3        4        5       6       7    \\\n",
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       "1  -1.0061  15.6481  -1.6439   5.6558   2.5903  -0.4408  6.0022  4.6069   \n",
       "2   0.0674  -1.6439  26.7531  17.8784  -0.3144   5.1255  3.0532  9.6956   \n",
       "3  -2.9111   5.6558  17.8784  28.0756  -1.2179   1.0285  7.3694  7.8788   \n",
       "4   7.1808   2.5903  -0.3144  -1.2179  40.4632  34.7838 -1.6998 -4.6655   \n",
       "\n",
       "      8       9    ...     490     491     492     493     494      495  \\\n",
       "0  0.4063  1.5802  ...  0.5930  3.8502 -9.1364  4.2919 -3.3995 -10.6717   \n",
       "1 -2.4341  2.6042  ...  0.9103  1.0257 -2.7119  0.1541  0.3194   8.1394   \n",
       "2 -6.1012  3.9284  ...  6.0349  5.8823  9.4580 -6.5917  5.7670  -1.7374   \n",
       "3 -6.3967 -0.0440  ...  4.3926  5.6318  7.7203  0.8116  2.4675   7.4404   \n",
       "4  5.3079  4.0269  ...  2.6487  6.9229 -7.3993  0.6240  1.2561  -2.8091   \n",
       "\n",
       "      496     497     498     499  \n",
       "0 -3.1036  5.0830 -1.1397 -2.0652  \n",
       "1  5.3727 -5.6364 -8.4340 -1.2430  \n",
       "2 -1.2486 -3.1189 -1.1444 -3.6523  \n",
       "3  0.0878 -8.0281 -7.5140  1.3202  \n",
       "4 -0.0297  7.9712 -3.6725  2.6617  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = pd.DataFrame(K)\n",
    "K.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b7e0729",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = (nSamples, nSamples)\n",
    "unit = np.ones(s)/nSamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58548df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit = pd.DataFrame(unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6af65b98",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 18,
     "metadata": {},
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    "unit.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4e716e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_c = K-unit@K-K@unit+unit@K@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7316c4a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_c = round(K_c, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dfe32064",
   "metadata": {},
   "outputs": [
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       "       0        1        2        3        4        5       6       7    \\\n",
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       "\n",
       "      8       9    ...     490     491     492     493     494      495  \\\n",
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       "3 -6.3967 -0.0440  ...  4.3926  5.6318  7.7203  0.8116  2.4675   7.4404   \n",
       "4  5.3079  4.0269  ...  2.6487  6.9229 -7.3993  0.6240  1.2561  -2.8091   \n",
       "\n",
       "      496     497     498     499  \n",
       "0 -3.1036  5.0830 -1.1397 -2.0652  \n",
       "1  5.3727 -5.6364 -8.4340 -1.2430  \n",
       "2 -1.2486 -3.1189 -1.1444 -3.6523  \n",
       "3  0.0878 -8.0281 -7.5140  1.3202  \n",
       "4 -0.0297  7.9712 -3.6725  2.6617  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8965afd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute Eigenvalues and Eigenvector\n",
    "# eigvls, eigvct = np.linalg.eig(K_c) # K_c\n",
    "u, s, vh = np.linalg.svd(K_c/nSamples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a18f2a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAMBDA\n",
    "lambda_ = s\n",
    "lambd = lambda_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea2c0e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2f46049",
   "metadata": {},
   "outputs": [],
   "source": [
    "# COEFFICIENT\n",
    "cf = np.sqrt(nSamples*lambd)\n",
    "coefficient = np.divide(u, cf.T) # u\n",
    "# coefficient = np.divide(vh, cf.T) # vh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "53ee1459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9beaaa78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefficient.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac3d6d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eigvls = eigvls.real\n",
    "eigvls = lambd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a9c0e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eigvct = eigvct.real\n",
    "eigvct = coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "40be854e",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvct = pd.DataFrame(eigvct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6b88f7b2",
   "metadata": {},
   "outputs": [
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       "      <td>-10.137464</td>\n",
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       "      <td>-12.176828</td>\n",
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       "      <td>-0.000178</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.001443</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>-0.001811</td>\n",
       "      <td>...</td>\n",
       "      <td>7.171127</td>\n",
       "      <td>-6.085880</td>\n",
       "      <td>-13.487047</td>\n",
       "      <td>1.368614</td>\n",
       "      <td>-18.415453</td>\n",
       "      <td>3.122767</td>\n",
       "      <td>-12.763626</td>\n",
       "      <td>-2.383554</td>\n",
       "      <td>-10.051432</td>\n",
       "      <td>49.627076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>-0.000370</td>\n",
       "      <td>0.001192</td>\n",
       "      <td>-0.001477</td>\n",
       "      <td>...</td>\n",
       "      <td>8.923839</td>\n",
       "      <td>-10.229764</td>\n",
       "      <td>8.474007</td>\n",
       "      <td>14.667617</td>\n",
       "      <td>8.471012</td>\n",
       "      <td>-5.260246</td>\n",
       "      <td>-9.264575</td>\n",
       "      <td>25.567977</td>\n",
       "      <td>9.018188</td>\n",
       "      <td>20.400687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000174</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000804</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>-0.002369</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.780264</td>\n",
       "      <td>-4.667133</td>\n",
       "      <td>8.841610</td>\n",
       "      <td>16.247365</td>\n",
       "      <td>-10.739974</td>\n",
       "      <td>-2.740979</td>\n",
       "      <td>28.547725</td>\n",
       "      <td>11.045431</td>\n",
       "      <td>25.301894</td>\n",
       "      <td>34.202195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000287</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>-0.001292</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>-0.001864</td>\n",
       "      <td>-0.000639</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.327189</td>\n",
       "      <td>-7.815692</td>\n",
       "      <td>5.273028</td>\n",
       "      <td>-7.071183</td>\n",
       "      <td>24.137368</td>\n",
       "      <td>2.821445</td>\n",
       "      <td>-20.565573</td>\n",
       "      <td>31.546764</td>\n",
       "      <td>-5.530525</td>\n",
       "      <td>-63.019382</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
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       "        0         1         2         3         4         5         6    \\\n",
       "0 -0.000105  0.000477 -0.000122  0.000811 -0.001505  0.000064 -0.001167   \n",
       "1 -0.000178  0.000106  0.000391 -0.001443 -0.000515  0.000089  0.000312   \n",
       "2 -0.000149  0.000125  0.000719  0.001335 -0.000942 -0.002309  0.000356   \n",
       "3 -0.000174  0.000067  0.000574  0.000236 -0.000009 -0.000804  0.000018   \n",
       "4 -0.000287  0.000859  0.000617  0.000668 -0.001292  0.001648 -0.001265   \n",
       "\n",
       "        7         8         9    ...        490        491        492  \\\n",
       "0  0.000629 -0.000325  0.001204  ...  10.083152   7.496293  -6.524933   \n",
       "1 -0.000700  0.001253 -0.001811  ...   7.171127  -6.085880 -13.487047   \n",
       "2 -0.000370  0.001192 -0.001477  ...   8.923839 -10.229764   8.474007   \n",
       "3  0.000073  0.002019 -0.002369  ...  -0.780264  -4.667133   8.841610   \n",
       "4  0.001282 -0.001864 -0.000639  ...  -6.327189  -7.815692   5.273028   \n",
       "\n",
       "         493        494       495        496        497        498        499  \n",
       "0  17.754944 -10.137464  9.398385 -14.582244 -12.176828 -43.442538  20.996854  \n",
       "1   1.368614 -18.415453  3.122767 -12.763626  -2.383554 -10.051432  49.627076  \n",
       "2  14.667617   8.471012 -5.260246  -9.264575  25.567977   9.018188  20.400687  \n",
       "3  16.247365 -10.739974 -2.740979  28.547725  11.045431  25.301894  34.202195  \n",
       "4  -7.071183  24.137368  2.821445 -20.565573  31.546764  -5.530525 -63.019382  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c036e77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls = pd.DataFrame(eigvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "862b9af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls = round(eigvls, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a3acc649",
   "metadata": {},
   "outputs": [
    {
     "data": {
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d69d0fe",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000348</td>\n",
       "      <td>-0.000891</td>\n",
       "      <td>0.000208</td>\n",
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       "      <td>0.000391</td>\n",
       "      <td>-0.001443</td>\n",
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       "      <td>-0.000407</td>\n",
       "      <td>-0.000771</td>\n",
       "      <td>-0.000451</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>-0.001233</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.000898</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.001428</td>\n",
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       "      <td>-0.000174</td>\n",
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       "      <td>0.000574</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000804</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>-0.002369</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.001115</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000287</td>\n",
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       "      <td>0.000617</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>-0.001292</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>-0.001864</td>\n",
       "      <td>-0.000639</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>-0.003548</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>-0.001548</td>\n",
       "      <td>-0.001637</td>\n",
       "      <td>0.001718</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-0.000256</td>\n",
       "      <td>-0.000719</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>0.002197</td>\n",
       "      <td>0.001234</td>\n",
       "      <td>0.002049</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>-0.003077</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.001117</td>\n",
       "      <td>0.002107</td>\n",
       "      <td>-0.001239</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>-0.001238</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000723</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>-0.002209</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>-0.001866</td>\n",
       "      <td>-0.001186</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>-0.000153</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>-0.002007</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.000190</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.003574</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.001349</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>0.000764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>-0.001113</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>-0.000604</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>-0.000127</td>\n",
       "      <td>-0.002323</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000912</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>-0.000787</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>-0.001817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000172</td>\n",
       "      <td>-0.000627</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>0.001738</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>-0.001889</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>-0.000484</td>\n",
       "      <td>-0.001028</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>-0.002046</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>-0.002196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -0.000105  0.000477 -0.000122  0.000811 -0.001505  0.000064 -0.001167   \n",
       "1   -0.000178  0.000106  0.000391 -0.001443 -0.000515  0.000089  0.000312   \n",
       "2   -0.000149  0.000125  0.000719  0.001335 -0.000942 -0.002309  0.000356   \n",
       "3   -0.000174  0.000067  0.000574  0.000236 -0.000009 -0.000804  0.000018   \n",
       "4   -0.000287  0.000859  0.000617  0.000668 -0.001292  0.001648 -0.001265   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495 -0.000256 -0.000719  0.000564 -0.001085  0.002197  0.001234  0.002049   \n",
       "496 -0.000051 -0.000723  0.000010 -0.002209 -0.000266 -0.001866 -0.001186   \n",
       "497  0.000190 -0.000349 -0.000977 -0.000453 -0.000839 -0.001561 -0.003574   \n",
       "498 -0.000042 -0.000170 -0.001113  0.000964  0.000718 -0.000604 -0.000384   \n",
       "499 -0.000185 -0.000172 -0.000627  0.002653  0.001738  0.000828 -0.001889   \n",
       "\n",
       "           7         8         9         10        11        12        13  \\\n",
       "0    0.000629 -0.000325  0.001204 -0.000408  0.001098 -0.000510  0.000348   \n",
       "1   -0.000700  0.001253 -0.001811  0.000260 -0.000407 -0.000771 -0.000451   \n",
       "2   -0.000370  0.001192 -0.001477  0.001862 -0.000898 -0.000665  0.001869   \n",
       "3    0.000073  0.002019 -0.002369  0.001528  0.000125  0.000476  0.001006   \n",
       "4    0.001282 -0.001864 -0.000639  0.000299 -0.003548  0.000587 -0.001548   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495  0.000149  0.001369 -0.003077  0.000878  0.001117  0.002107 -0.001239   \n",
       "496 -0.001148  0.000779 -0.000153  0.000680  0.000533  0.000979 -0.002007   \n",
       "497  0.000513 -0.001148  0.000184  0.001021  0.000055  0.001349 -0.002050   \n",
       "498 -0.000127 -0.002323  0.000124  0.000912  0.001587  0.002913 -0.000787   \n",
       "499  0.000525 -0.000484 -0.001028  0.002731  0.000201  0.003199 -0.002046   \n",
       "\n",
       "           14        15  \n",
       "0   -0.000891  0.000208  \n",
       "1    0.001210 -0.001233  \n",
       "2    0.001183  0.001428  \n",
       "3    0.001898  0.001115  \n",
       "4   -0.001637  0.001718  \n",
       "..        ...       ...  \n",
       "495  0.001061 -0.001238  \n",
       "496  0.000251  0.000520  \n",
       "497 -0.002169  0.000764  \n",
       "498  0.000197 -0.001817  \n",
       "499  0.003460 -0.002196  \n",
       "\n",
       "[500 rows x 16 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " eigvct.iloc[:,:nComponent]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ffb3770f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_8784/485419572.py:2: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  score_train = np.matmul(K_c, eigvct.iloc[:,:nComponent])\n"
     ]
    }
   ],
   "source": [
    "# score matrix\n",
    "score_train = np.matmul(K_c, eigvct.iloc[:,:nComponent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a266c42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_train = round(score_train, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9918600",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_train = pd.DataFrame(score_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9adebfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.3890</td>\n",
       "      <td>1.1623</td>\n",
       "      <td>-0.1784</td>\n",
       "      <td>0.9177</td>\n",
       "      <td>-1.5744</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>-1.1179</td>\n",
       "      <td>0.5124</td>\n",
       "      <td>-0.2487</td>\n",
       "      <td>0.8591</td>\n",
       "      <td>-0.2701</td>\n",
       "      <td>0.7001</td>\n",
       "      <td>-0.3089</td>\n",
       "      <td>0.2011</td>\n",
       "      <td>-0.5087</td>\n",
       "      <td>0.1123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6599</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.5698</td>\n",
       "      <td>-1.6324</td>\n",
       "      <td>-0.5389</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>-0.5701</td>\n",
       "      <td>0.9584</td>\n",
       "      <td>-1.2918</td>\n",
       "      <td>0.1722</td>\n",
       "      <td>-0.2593</td>\n",
       "      <td>-0.4675</td>\n",
       "      <td>-0.2611</td>\n",
       "      <td>0.6911</td>\n",
       "      <td>-0.6665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.5514</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>1.0482</td>\n",
       "      <td>1.5101</td>\n",
       "      <td>-0.9849</td>\n",
       "      <td>-2.2676</td>\n",
       "      <td>0.3406</td>\n",
       "      <td>-0.3014</td>\n",
       "      <td>0.9113</td>\n",
       "      <td>-1.0536</td>\n",
       "      <td>1.2337</td>\n",
       "      <td>-0.5730</td>\n",
       "      <td>-0.4030</td>\n",
       "      <td>1.0813</td>\n",
       "      <td>0.6757</td>\n",
       "      <td>0.7721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.6455</td>\n",
       "      <td>0.1636</td>\n",
       "      <td>0.8370</td>\n",
       "      <td>0.2675</td>\n",
       "      <td>-0.0092</td>\n",
       "      <td>-0.7891</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>1.5440</td>\n",
       "      <td>-1.6896</td>\n",
       "      <td>1.0119</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.2884</td>\n",
       "      <td>0.5822</td>\n",
       "      <td>1.0841</td>\n",
       "      <td>0.6028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.0660</td>\n",
       "      <td>2.0936</td>\n",
       "      <td>0.9006</td>\n",
       "      <td>0.7556</td>\n",
       "      <td>-1.3513</td>\n",
       "      <td>1.6183</td>\n",
       "      <td>-1.2116</td>\n",
       "      <td>1.0441</td>\n",
       "      <td>-1.4258</td>\n",
       "      <td>-0.4561</td>\n",
       "      <td>0.1984</td>\n",
       "      <td>-2.2627</td>\n",
       "      <td>0.3561</td>\n",
       "      <td>-0.8953</td>\n",
       "      <td>-0.9349</td>\n",
       "      <td>0.9291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-0.9481</td>\n",
       "      <td>-1.7525</td>\n",
       "      <td>0.8231</td>\n",
       "      <td>-1.2280</td>\n",
       "      <td>2.2978</td>\n",
       "      <td>1.2122</td>\n",
       "      <td>1.9624</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>1.0467</td>\n",
       "      <td>-2.1946</td>\n",
       "      <td>0.5813</td>\n",
       "      <td>0.7122</td>\n",
       "      <td>1.2771</td>\n",
       "      <td>-0.7165</td>\n",
       "      <td>0.6059</td>\n",
       "      <td>-0.6695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-0.1893</td>\n",
       "      <td>-1.7633</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>-2.4990</td>\n",
       "      <td>-0.2784</td>\n",
       "      <td>-1.8322</td>\n",
       "      <td>-1.1354</td>\n",
       "      <td>-0.9352</td>\n",
       "      <td>0.5958</td>\n",
       "      <td>-0.1090</td>\n",
       "      <td>0.4507</td>\n",
       "      <td>0.3397</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>-1.1611</td>\n",
       "      <td>0.1433</td>\n",
       "      <td>0.2810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.7030</td>\n",
       "      <td>-0.8516</td>\n",
       "      <td>-1.4245</td>\n",
       "      <td>-0.5130</td>\n",
       "      <td>-0.8777</td>\n",
       "      <td>-1.5329</td>\n",
       "      <td>-3.4224</td>\n",
       "      <td>0.4179</td>\n",
       "      <td>-0.8778</td>\n",
       "      <td>0.1314</td>\n",
       "      <td>0.6761</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.8179</td>\n",
       "      <td>-1.1857</td>\n",
       "      <td>-1.2387</td>\n",
       "      <td>0.4130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.1561</td>\n",
       "      <td>-0.4147</td>\n",
       "      <td>-1.6232</td>\n",
       "      <td>1.0913</td>\n",
       "      <td>0.7514</td>\n",
       "      <td>-0.5931</td>\n",
       "      <td>-0.3676</td>\n",
       "      <td>-0.1032</td>\n",
       "      <td>-1.7764</td>\n",
       "      <td>0.0884</td>\n",
       "      <td>0.6038</td>\n",
       "      <td>1.0120</td>\n",
       "      <td>1.7659</td>\n",
       "      <td>-0.4551</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>-0.9823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.6872</td>\n",
       "      <td>-0.4192</td>\n",
       "      <td>-0.9147</td>\n",
       "      <td>3.0023</td>\n",
       "      <td>1.8172</td>\n",
       "      <td>0.8128</td>\n",
       "      <td>-1.8093</td>\n",
       "      <td>0.4275</td>\n",
       "      <td>-0.3701</td>\n",
       "      <td>-0.7331</td>\n",
       "      <td>1.8089</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>1.9397</td>\n",
       "      <td>-1.1837</td>\n",
       "      <td>1.9760</td>\n",
       "      <td>-1.1877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1       2       3       4       5       6       7       8   \\\n",
       "0   -0.3890  1.1623 -0.1784  0.9177 -1.5744  0.0625 -1.1179  0.5124 -0.2487   \n",
       "1   -0.6599  0.2576  0.5698 -1.6324 -0.5389  0.0869  0.2991 -0.5701  0.9584   \n",
       "2   -0.5514  0.3050  1.0482  1.5101 -0.9849 -2.2676  0.3406 -0.3014  0.9113   \n",
       "3   -0.6455  0.1636  0.8370  0.2675 -0.0092 -0.7891  0.0169  0.0593  1.5440   \n",
       "4   -1.0660  2.0936  0.9006  0.7556 -1.3513  1.6183 -1.2116  1.0441 -1.4258   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "495 -0.9481 -1.7525  0.8231 -1.2280  2.2978  1.2122  1.9624  0.1210  1.0467   \n",
       "496 -0.1893 -1.7633  0.0140 -2.4990 -0.2784 -1.8322 -1.1354 -0.9352  0.5958   \n",
       "497  0.7030 -0.8516 -1.4245 -0.5130 -0.8777 -1.5329 -3.4224  0.4179 -0.8778   \n",
       "498 -0.1561 -0.4147 -1.6232  1.0913  0.7514 -0.5931 -0.3676 -0.1032 -1.7764   \n",
       "499 -0.6872 -0.4192 -0.9147  3.0023  1.8172  0.8128 -1.8093  0.4275 -0.3701   \n",
       "\n",
       "         9       10      11      12      13      14      15  \n",
       "0    0.8591 -0.2701  0.7001 -0.3089  0.2011 -0.5087  0.1123  \n",
       "1   -1.2918  0.1722 -0.2593 -0.4675 -0.2611  0.6911 -0.6665  \n",
       "2   -1.0536  1.2337 -0.5730 -0.4030  1.0813  0.6757  0.7721  \n",
       "3   -1.6896  1.0119  0.0800  0.2884  0.5822  1.0841  0.6028  \n",
       "4   -0.4561  0.1984 -2.2627  0.3561 -0.8953 -0.9349  0.9291  \n",
       "..      ...     ...     ...     ...     ...     ...     ...  \n",
       "495 -2.1946  0.5813  0.7122  1.2771 -0.7165  0.6059 -0.6695  \n",
       "496 -0.1090  0.4507  0.3397  0.5937 -1.1611  0.1433  0.2810  \n",
       "497  0.1314  0.6761  0.0350  0.8179 -1.1857 -1.2387  0.4130  \n",
       "498  0.0884  0.6038  1.0120  1.7659 -0.4551  0.1126 -0.9823  \n",
       "499 -0.7331  1.8089  0.1280  1.9397 -1.1837  1.9760 -1.1877  \n",
       "\n",
       "[500 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4fa1ea5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### T2 training Process\n",
    "a = eigvls.iloc[:nComponent,:]\n",
    "b = np.zeros((nComponent, nComponent), int)\n",
    "np.fill_diagonal(b, a)\n",
    "# fill the diagonal not in real number\n",
    "# good if the number is double format !!!!!!!!!!!!!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1752f88b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.8772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.9170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.2630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.9641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.9152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.6285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.5295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.4266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.3248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.2755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.1570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0   7.4157\n",
       "1   4.8772\n",
       "2   2.9170\n",
       "3   2.2630\n",
       "4   2.0917\n",
       "5   1.9641\n",
       "6   1.9152\n",
       "7   1.6285\n",
       "8   1.5295\n",
       "9   1.4266\n",
       "10  1.3248\n",
       "11  1.2755\n",
       "12  1.2125\n",
       "13  1.1570\n",
       "14  1.1422\n",
       "15  1.0814"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4b49014c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09e5cdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = a.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2b827aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6f5961c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8772"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9c4868a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.4157"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17855906",
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_egv=[]\n",
    "for i in range(len(z)):\n",
    "    extract_egv.append(z[i][0])\n",
    "#     print(z[i][0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bf35fc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.4157,\n",
       " 4.8772,\n",
       " 2.917,\n",
       " 2.263,\n",
       " 2.0917,\n",
       " 1.9641,\n",
       " 1.9152,\n",
       " 1.6285,\n",
       " 1.5295,\n",
       " 1.4266,\n",
       " 1.3248,\n",
       " 1.2755,\n",
       " 1.2125,\n",
       " 1.157,\n",
       " 1.1422,\n",
       " 1.0814]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_egv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ffcb57e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# c = np.matrix(extract_egv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce07dbe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.diag(extract_egv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2a578b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.4157, 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 4.8772, 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 2.917 , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 2.263 , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 2.0917, 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 1.9641, 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 1.9152, 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 1.6285,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        1.5295, 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 1.4266, 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 1.3248, 0.    , 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 1.2755, 0.    , 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 1.2125, 0.    , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 1.157 , 0.    , 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 1.1422, 0.    ],\n",
       "       [0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 1.0814]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "13ad61cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6ae0de4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bd50c081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b # !!!!!COUTION!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "95ff782e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# invegvldiag = np.linalg.inv(b)\n",
    "# invegvldiag = np.linalg.inv(c) # this is making the value so low\n",
    "invegvldiag = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bfa3516d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invegvldiag.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "65be5f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "invegvldiag = pd.DataFrame(invegvldiag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a3b1415",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>15</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.4157</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.8772</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.917</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.263</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0917</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.9641</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.9152</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.6285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.5295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4266</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.3248</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.2755</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.2125</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.1422</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1      2      3       4       5       6       7       8   \\\n",
       "0   7.4157  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "1   0.0000  4.8772  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "2   0.0000  0.0000  2.917  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "3   0.0000  0.0000  0.000  2.263  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "4   0.0000  0.0000  0.000  0.000  2.0917  0.0000  0.0000  0.0000  0.0000   \n",
       "5   0.0000  0.0000  0.000  0.000  0.0000  1.9641  0.0000  0.0000  0.0000   \n",
       "6   0.0000  0.0000  0.000  0.000  0.0000  0.0000  1.9152  0.0000  0.0000   \n",
       "7   0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  1.6285  0.0000   \n",
       "8   0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  1.5295   \n",
       "9   0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "10  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "11  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "12  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "13  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "14  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "15  0.0000  0.0000  0.000  0.000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
       "\n",
       "        9       10      11      12     13      14      15  \n",
       "0   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "1   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "2   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "3   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "4   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "5   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "6   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "7   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "8   0.0000  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "9   1.4266  0.0000  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "10  0.0000  1.3248  0.0000  0.0000  0.000  0.0000  0.0000  \n",
       "11  0.0000  0.0000  1.2755  0.0000  0.000  0.0000  0.0000  \n",
       "12  0.0000  0.0000  0.0000  1.2125  0.000  0.0000  0.0000  \n",
       "13  0.0000  0.0000  0.0000  0.0000  1.157  0.0000  0.0000  \n",
       "14  0.0000  0.0000  0.0000  0.0000  0.000  1.1422  0.0000  \n",
       "15  0.0000  0.0000  0.0000  0.0000  0.000  0.0000  1.0814  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invegvldiag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "787a9e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_train = score_train@invegvldiag@score_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "31eabdc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# T2_train = round(T2_train, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "32aa8d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8deb723e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\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>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.064105</td>\n",
       "      <td>-2.262551</td>\n",
       "      <td>5.407219</td>\n",
       "      <td>-0.621797</td>\n",
       "      <td>22.363951</td>\n",
       "      <td>17.507985</td>\n",
       "      <td>0.932204</td>\n",
       "      <td>2.831217</td>\n",
       "      <td>2.608601</td>\n",
       "      <td>-0.406580</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.757866</td>\n",
       "      <td>2.538031</td>\n",
       "      <td>-13.639424</td>\n",
       "      <td>4.639330</td>\n",
       "      <td>-5.413831</td>\n",
       "      <td>-25.435154</td>\n",
       "      <td>-13.064147</td>\n",
       "      <td>3.670716</td>\n",
       "      <td>-0.116161</td>\n",
       "      <td>1.077978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.262551</td>\n",
       "      <td>17.133568</td>\n",
       "      <td>4.070493</td>\n",
       "      <td>9.261054</td>\n",
       "      <td>4.893655</td>\n",
       "      <td>7.825935</td>\n",
       "      <td>5.035958</td>\n",
       "      <td>11.818918</td>\n",
       "      <td>1.460069</td>\n",
       "      <td>8.467569</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.837000</td>\n",
       "      <td>0.279551</td>\n",
       "      <td>-0.098166</td>\n",
       "      <td>-6.642341</td>\n",
       "      <td>8.624941</td>\n",
       "      <td>12.900034</td>\n",
       "      <td>9.174353</td>\n",
       "      <td>-9.370592</td>\n",
       "      <td>-10.579907</td>\n",
       "      <td>-10.265188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.407219</td>\n",
       "      <td>4.070493</td>\n",
       "      <td>31.577291</td>\n",
       "      <td>18.086026</td>\n",
       "      <td>6.518820</td>\n",
       "      <td>9.331773</td>\n",
       "      <td>1.146271</td>\n",
       "      <td>12.290549</td>\n",
       "      <td>-7.690971</td>\n",
       "      <td>11.687549</td>\n",
       "      <td>...</td>\n",
       "      <td>12.871801</td>\n",
       "      <td>8.760264</td>\n",
       "      <td>0.256256</td>\n",
       "      <td>-25.337943</td>\n",
       "      <td>8.949570</td>\n",
       "      <td>-5.746522</td>\n",
       "      <td>-1.808811</td>\n",
       "      <td>-6.888039</td>\n",
       "      <td>-4.835054</td>\n",
       "      <td>2.449433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.621797</td>\n",
       "      <td>9.261054</td>\n",
       "      <td>18.086026</td>\n",
       "      <td>17.967245</td>\n",
       "      <td>3.745784</td>\n",
       "      <td>4.694944</td>\n",
       "      <td>1.299490</td>\n",
       "      <td>7.997788</td>\n",
       "      <td>-7.444986</td>\n",
       "      <td>8.436469</td>\n",
       "      <td>...</td>\n",
       "      <td>5.379144</td>\n",
       "      <td>4.783717</td>\n",
       "      <td>-3.579993</td>\n",
       "      <td>-16.081800</td>\n",
       "      <td>8.585188</td>\n",
       "      <td>11.449923</td>\n",
       "      <td>2.833269</td>\n",
       "      <td>-8.767937</td>\n",
       "      <td>-5.688617</td>\n",
       "      <td>6.111795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.363951</td>\n",
       "      <td>4.893655</td>\n",
       "      <td>6.518820</td>\n",
       "      <td>3.745784</td>\n",
       "      <td>60.013921</td>\n",
       "      <td>58.820148</td>\n",
       "      <td>-3.527804</td>\n",
       "      <td>0.487346</td>\n",
       "      <td>15.302747</td>\n",
       "      <td>10.633613</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.091384</td>\n",
       "      <td>2.603225</td>\n",
       "      <td>-18.139193</td>\n",
       "      <td>0.092369</td>\n",
       "      <td>-0.654587</td>\n",
       "      <td>-20.110293</td>\n",
       "      <td>-25.238931</td>\n",
       "      <td>-7.387221</td>\n",
       "      <td>-7.549064</td>\n",
       "      <td>6.405594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0          1          2          3          4          5         6    \\\n",
       "0  20.064105  -2.262551   5.407219  -0.621797  22.363951  17.507985  0.932204   \n",
       "1  -2.262551  17.133568   4.070493   9.261054   4.893655   7.825935  5.035958   \n",
       "2   5.407219   4.070493  31.577291  18.086026   6.518820   9.331773  1.146271   \n",
       "3  -0.621797   9.261054  18.086026  17.967245   3.745784   4.694944  1.299490   \n",
       "4  22.363951   4.893655   6.518820   3.745784  60.013921  58.820148 -3.527804   \n",
       "\n",
       "         7          8          9    ...        490       491        492  \\\n",
       "0   2.831217   2.608601  -0.406580  ...  -1.757866  2.538031 -13.639424   \n",
       "1  11.818918   1.460069   8.467569  ...  -0.837000  0.279551  -0.098166   \n",
       "2  12.290549  -7.690971  11.687549  ...  12.871801  8.760264   0.256256   \n",
       "3   7.997788  -7.444986   8.436469  ...   5.379144  4.783717  -3.579993   \n",
       "4   0.487346  15.302747  10.633613  ...  -6.091384  2.603225 -18.139193   \n",
       "\n",
       "         493       494        495        496       497        498        499  \n",
       "0   4.639330 -5.413831 -25.435154 -13.064147  3.670716  -0.116161   1.077978  \n",
       "1  -6.642341  8.624941  12.900034   9.174353 -9.370592 -10.579907 -10.265188  \n",
       "2 -25.337943  8.949570  -5.746522  -1.808811 -6.888039  -4.835054   2.449433  \n",
       "3 -16.081800  8.585188  11.449923   2.833269 -8.767937  -5.688617   6.111795  \n",
       "4   0.092369 -0.654587 -20.110293 -25.238931 -7.387221  -7.549064   6.405594  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7a70bdf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here the data is from training data \n",
    "# or with using same data\n",
    "T2_tfinal = np.diag(T2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a0756384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       144.07527715, 118.88065709, 139.34458467, 162.44388393,\n",
       "       140.73309231, 154.34073166,  83.61047055,  89.51163164,\n",
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       "        40.70770672,  34.96807462,  31.94612103,  24.5172554 ,\n",
       "        63.78047672, 108.62602991,  86.99885463, 124.60568154,\n",
       "        67.52317165, 177.42216996, 124.96905245, 107.18343118,\n",
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       "        79.94351465,  86.46020414, 100.23735034,  84.54075816,\n",
       "       101.64372238,  96.26160911,  99.0838838 , 112.51450274,\n",
       "       130.32532673, 108.79324448, 100.36791091,  82.91920339,\n",
       "        79.41319875, 111.58063224, 116.57265897, 102.8074808 ,\n",
       "        83.70431258,  62.73677167,  56.69781429, 118.67759021,\n",
       "        83.57522682, 102.1840343 ,  72.86105316, 108.25646873,\n",
       "        65.48003434, 115.20487055, 121.4420462 , 107.09610276,\n",
       "        96.20804014,  56.4440188 ,  59.76987121,  64.68797436,\n",
       "        27.08776345,  47.31957429,  53.78678945,  74.35988888,\n",
       "        50.51830453,  95.33749698,  42.78406538,  61.48000642,\n",
       "        43.28404096,  48.84509923,  25.25377965,  59.47022784])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_tfinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4f3eca0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### T2 limit\n",
    "# im using 16 component\n",
    "# nComponent = 30\n",
    "# nSamples = df_train.shape[0]\n",
    "# conf_T2 = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2d32dde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f\n",
    "F = f.ppf(conf_T2, nComponent, nSamples-nComponent)\n",
    "T2_trlim = ((nComponent*(nSamples**2-1))/(nSamples*(nSamples-nComponent)))*F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3ae540ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.537906838592278"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_trlim\n",
    "# conf_T2 = 0.95, T2_trlim = 47.3464294521146\n",
    "# conf_T2 = 0.99, T2_trlim = 55.46204002064628"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ac4aa121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xu+36sVs+MBXlWo+SUWssO72vXl/S9nzGz0zksp4qC/aQkYK/f2Q89LJjQbATOPJ4WG0bPzNhmV8IwPsWN+OVN06jVJpy2XvguQPWUn3Dmo5MPTz6hA0At36z1xJ6W+7osYTHxKQhPFrmNAU+v18zTlj3Uj+X3iZ1MxfCKIQBIaBmtSEAsxoL+MxVyzB05K2qXDRelAy3/tSaPJ3et/M0Uics9Zmt170yLbBgdyEKm2nctMxpQoEMsdvYUEDLnKbQ29Yyp8moeiOXt2s7AExNHqqpRgiB8TMT9XcsZtSV2vrv9mGiZIiziVIFjw+M4q8+/l5sWNNhLfM3bh/ylYsniBklbBdHu3Op59NXFtJWLfPG18ofX0sw1+pPrZVNrXqv8lxqkB5g7I3lwS1XhQW7A1HZTONEFoNQNeXxMxOhts3uN1T7sKShQJgsG59J2tRQD/0j43j6laNVx6TpYejwKZQr02t8RkWY48zLuZxWFlIBqjWRecnLbnnk2LSh1srG68pH1dBV05K8dxOT2doDsoMFuwPqIDs/WfHlApUWm6mdphx22zxr4+aqwfg/u/QOj1XtJxQJWGdmJKyu9xns2vpZJYZ1L/tHxvH6ybNoKBYsj6+WOU22pkRdi/ejANUSvOpeTUUAp89OTmtDrZWNfM+LSUV9xoWyX1Ax25JlWLA70NPeioaCsVsuYFRK95KzQz6YqktVUjO/3YMf9gavm3CR1+LwybOWL3O5nG1tSJqd5Apl442dlv1Zrff5iS7/m3B+V4m67TiIfViPqv7kFUvRsagZ92wbqtpDcDpnmKsGfa/mWz/7T8u853XF7Md1tKe9FQ1FY+wWzALaMogqi+ZCFRbsDnS1teDm7iXYvPugZ4GUJts64KwhheEW5/Ybuu9wQ4GqtMGsmmJ0s5NarESf4NYFcJkLIiTl+6GYDU0l5pm9x6btITidz8+qwYsNfVajvfeK1wnD6Ro6JSWTtVbJ3IMqlSogAC8eOpnJQDoJC3YXbjIrrHtd6oYZ1h2WRh2mEPfyG3Y1O8sVgVuuWIJL588OJbFTUriZncJYCQU1rdTraqmuTB/pO4SK5rvqFsLop9+12lnLe0Xi9nw4XUMn7zBZa1UI4EOXX4SfvHIUZQE89fJRPLPvuOtqJc2wYLdBHTh+HtYwbJ5p0/r9ogcrEVClwWalb0FC/OudRL0KSTuTQtBx19XWgmtWXIynXjY2hMvlat90uYdQ6xxe+u01x4yd94quODiNIadr6PTb6rGL586q2j/JWiCdCgt2jXoyOYahtaVR6/fzW3p+DvX6bdq139Lk0+x5oJZsKxJwx6+3Y+7sRs8TfT3XvpaQdBJs9fjBP7P3mPW6WCQUiKwYBLmHEAa12mkXNap/pnd4rOYYsvue02/r8QmP9h2qKvyRVbMhC3aNKperssBDLpkc7ahXa8ua1m/3W04Pb8ucpkx4HmwdGLUe7rIAvvHscFXOFLf8P1Ffe7cgnKCKhLrpe+2Ki3HR3FmRRWE6tdPrdXMaQ14mU9XN8UvffwknTp+fVkA7L1GoLNg1VJujxM6n1m0g1auxhan127U9jHba/ZZaqcnufONnJlAgw5UtzZ4Hdg6ZbtkOVaKOYXCa+IPeS32F9VMlUVucOVO8Xje7MeRnMpUpepXHG4/2j1q29Dj2pOKABbsN7144Dy+OnrJeF6g6qKaWS1W9Glu9g0v3B7bTjDfvPliVDCmoZulnhZEW//5a3LRqMR7uO4SS8vQTDA3xZ6+ewO7hMcec3WqkbxRpip28kIKOOfV8h0+exZbnD4YavBb2/pR6fWXdXq+Tgqwnqwp1INu2dCdYsCtIYVdWlqaqr7Lql+00kNIQdar6AxcwXTPWCyb7DcBS8bPCCGM1EgddbS14WFmSdyxqxsN7DuJFs1iEU85u1R2yQMb13/K8P1Oe1/ap56pnzKnCd+8bp6vST9Qz8dbjk+80NtyinN0mBfW5LWseP4CRVrpWX9NUQMcLLNhNplWHB3D1ZVNFAfRNVTu/bLsIviS0UtUf2K4Njw+MWv0E4CsAyw4/K4ysLHX1dg4dPlW1irMz16geQRUBVMrRphdQU+gGWQnpYxpE04RmUIEW1Cff7TNO7qZuk4Lex0bzOqleWzd3Lwm0YZ1mWLCb9A5XV4cvFqjKlqoOVNUv2245LCP4ktp8qTXQH+07NO07WY8IjZqbVi3Go/1TMQ129mfVnEAECBCE8Kb9+s0zrgubIJHOenASIKqEphqToAdk1SIKs5vXJF8AbFfX8rkFUFUwp5Y7ZxpW4X7JvWD3qnFILff8pPFQ3n71O6s+rw+qzkXNVSYOXfAvmj870ZvvpP2oXhCAMYHBo/CZyXS1tWDLHe6mAjmhyqhbuSFZK01xrf0OO41RFzbjZyZ8V+vSN05BVLXSVF0LSxWBDU8Oes5YGYXZzes53VbXclXqVjBHJyt7Qyq5Fux+ExSpaVcfeO4APtxxifV5dVCp1WOkJpPmm+/kZ94YUNObqXgxI3W1TVXs8ZKm2G6/Q/e8qfJyMt9f3bmw7vEmx/yOwSNY3bnQNiCoYObFAYz6tX601SjMbk6audrmWqtrv23zO0mlwR6fa8Hudwk1fmbCyk9h59omB4MaaCM1mYc/d1UqNwb9+Jnr30tTX9LWHjfk5Oml6IZuAhQAfr7/BPYceHNaBSBZh1S+X++kLDcjJ0oV6/d0rZ+UONSGgBkr68WtJmqtikmqll4PbhOB2j4gHdHVuRbsfrVopwfILllRsUCWllWuCGzcNoTOS5st22saqicB/vzMJUlvFtWyJ6d980pf/bkV3ZAmwInJCmBYxRwrAOl1SIOYX1RqKT69w1MpignBMlbWi5uZyi1YK2olS6/9KsfmulWLU2GPz7Vg93uDnR4gu2RFG2/stAIdBIAXRw2viYf7Dhkh2eV0CKEgJqIkN4u82JOjbk8YqwN19TdZMtxJ7c5pZ+Jz2hy0q0NaT3trjQ07zTdOdDOVHmznZzNVni8MYW9X+1Xe52Onz4fmMloPuRbsgD9bmrzxqzsX2j5AKisumYtisYByqVJ1vFQWIMRXRacWQbSXJPYL3GIE9E2+sIN+9HaEsTrQA5VkCmOnxFXytZ2dW7Zr68Ao3ndpM86XKrjlA0vrXs3UGhv6+0C8K1HdTKUHCvoZ2/VcJ31CeHxgdCp7qVn7lcz7/NN9x1NR2zf3gt0rft3H5AaZToOZRClNucdl+7cOjOLxgVHLXOT1gQ57cLqZWuxiBHRvkyiCfiS1VgdetD49kOaaFRdj5ytHPa04nDRNmZRM8sobQ9YkUM9qppbiI99PwhymmqmcEpJ5VdyCXic7uaBWympoKOCetYaskJG7XjbNo4YFu4lf9zE9p0yBgOvf/Q587kO/Yp0vDTZ2YLpgcDIX2WXXC/JbtfyxXU0tZYHr33MxVi6ZP82LQU6mUZpk3FYrXoWbHkizYO4sq1KPWjLPboKzu3a9w2Omn/kUdquZKBWJJMxztRQMuYohGNHBboqYXtTd63XSPZLuf/a1aZWypG//5t0HI00n4QcW7CZ+H5CutuoKSwTg/UvmVwmiIETh/aELBjtzEVD/br4XwWcnINRJUgD46b7j+IMP/cq078YhxNyEiVfhZhfz8JgMCjMr9thpguomnHrtetpb0VisTkwXValDO5KMqHZSMOxWMWoGTn2V5ZSKoBa6Q8XI2BmrfJ7cLNV/I8p0El4JXbAT0W8D+C8A5gH4thDiqbB/IypuWrXYV7pOvxWWahHVclcXDHbmojA0MrtzyONS6NgJZ32SdIqCjUOIyd/xIrCd7rfeThkUJmB4UMnrol6rHYNHHK9/V1uLlU5WTTULTNm86/GOccOpJqrsQ1IrUrtVjJt3j6eC6zbYOVQUAHxw+QLbWIO40knUwpNgJ6LvAFgD4JgQolM5/hEAfw+gCOBbQoivCyGeAPAEEbUA+FsAqRfsukD1mq40bEET1XJXFQxy4pK/p7a7Xm1YF3wtc5o8F4XwOkkGNRGFgZ/7rbezVvWeWhv2+vnisnnrwT4CcFxZxIndKqZA9sUx6lnpOTlUrO5cWDW5qaaeBpso3rjxqrE/AOAfAXxXHiCiIoBNAD4MYBTAHiL6gRDiZfMjXzbfTz1+BWoYtmg7ojQ12LVT2nWl1lfvJGWnqfq5rjetWmxppGklyP12mhD0Y04eMXbEZfPWg632Hz1teYTUkxW0XnRlxc3GHkQB0wuyNzUU8JmrlmHoyFvoWDivanKTZjRp6rnnY52+7mUUkBDT01jafpBoGYDtUmMnoqsA3COE+C3z9d3mR79u/vuxEGKnl3N3d3eLvr4+n00PD6n9SIHqpoVErSnFXdIu6r7o1xWYbsuXx6TAcLKVMlP4GbP1oqaz1qVFU0MhdQWf1eChIJG5dgXZASOvkhCiyne9SMCvLV9gmWmKBHzhhhWRmcZ0iKhfCNGtH6/Hxn4pADVN4CiAKwH8NwDXA2gmouVCiG84NGg9gPUAsHSpt4xxUeFnRo9aU4rT1BBHX6SbItn8pkzbsPTtc6oKYHutVjSTiWu/AZgKtrJTAdOWFVQXykGUBNVermL51Cu+605mtKRTYIS+eSqE+AcA/+Dhc/cDuB8wNPaw2+EXrwI1ieCdqIirL48PjGLCjL7csKajysvgZ6+eQLFg5CEplYxjBLimdGAM4lICdM8Qw5LsbNOOG1WI6kJZKgl+Jh+9vyoEoKlxepyLanoBnD3M4hL49Qj21wEsUV4vNo/lmjg1paiJoy928QEP3d6DjduGrIpEpQrw4XddhJVL5qNlThN2DB5xTekQJ2E8iElrb/WijhNp3ghq5ggbO7fRpoZClRnFb+F01RPmZ6+esM5TJDjWWVAn2U279tuuhOMM8qpHsO8BcBkRvROGQP8kgNtCaVXKSdIzI2yi7ouTe2Pnpc1VFYkunjvLskuuuGRuKpa2YTyI0t96sizQWCTHWqlpJ61j3klxUIVykMLpXW1TuXnUyFcvhUacVsJxBnl5dXfcAuAaAAuIaBTA/xRCfJuI/hjAj2C4O35HCDEUSSt9ErYQyJrGlab2Oq0KOhY1o1ggVCpiWkUi/TtAMqlQw3gQtw6MWi55TrVS/ZKm+5s0ToqDU8I0PwRd0Tp9L04zrifBLoS41eH4DwH8MNQW1UnYyx0v50vTg5bGFLd2Ptgbtw+hYkbprVzcjMcHRq3P6t9xWtpGTRgPItV47XfspPH+JoF63eyEaFhmxqArFSf34rjMuImmFCCitQDWLl8enmtQ2MsdL0mhvDxoXh/geieJuJZ79bRT3eAqC+D5A+N4/sA4Hu0fxZY7eqzPuEWrxtHOMB5Et5VJECGdRM6WtGF33ezcC+sxH0WlrMVl0kpUsAshtgHY1t3dfUdY5wx7udPT3mqbwEni5UFzeoDVJEbyga9XG4tjuVev1ijbqPsJy7zl0oumVrRq1O0E6hcOcmVSLBDuWdsBYCoNQBAhnSevrKBEPbl5LeJdr798lOQuCVgkyx0hqv9X8PKgOeVQUZMYPdo/ipu76q++koSni992qv7tj/QdQsm8Bo0NBRBge+4gAjZp7VbPUTJ4+NS0iEW/QjpPXllBcXrmwtKye4edi3jrlZPSGlSXO8EOhLvcsUvgpJ7by4NmNxD1JEaTpgAIQxsLs/92D0sYWqNs4zozR4y6agkrsVrS2q3++/qkJT04gmzOpUF4JIXdMxfm3kNPe3XpS1nEG5haUcvoU9VfPk2msVwKdi94nd3tElvpVWRqPWhOwl9NYiRLj61btTg12pjTwxKm1mh37fxUxXH7nN92hm1XtfPu0SetmS6kg6Jft7BXZ9e+62L85JfHIMxaq1IZkxq6EIZ5DUKggvQEa0lmpGD3Uy1JNRucOH0e92wbCqWeaVfb9IyL+kCVn0sKt4clSoHk5dxeNTSv7YzK20T//ZluRomKsFZneprimz8wFZC0943TloYuANx+9Tsxd3Yj29h1ovCK8YI6856frDhWQVepqnOI+sprqZqvbE+tz/ohLM0zaVOGxG6TSr0fUXo/RaHFp+XhzxNhrSLVcVCuCCyaP9s61/iZCRTIMLsUCJg7uzG2ZF9+yZ1XjBda5jRVzbwya52TgNDzTxD8LbvchIZrmTifAssu1Wi9q4qkNUw9s2CBgIYCoQJMhXrbeCv5xW4SS7PPeJpiJ5LCLn12vdfCTZmpV9GJ857lxhTj56KpMy9hKh2n081Sb2ixQLi5e4nnKkv692uFGPsdPE679ECwicEu0MPp+1EP1P6RcWx4ctDaxAJg1UWVEIy6k/X+vt0kllRgVC3SPOHERZSmMydlph5FJ+57lgvB7vei6cLTzcYO1K+5On3fKRzaz+ah0y6931WF32sYx0DtHR6bSpVqIjV2tULNOo8Vr2qhT2JpMUXpJO3GmQaivAZuykxQRSfue5YLwe73ogUR1PUu8+y+79QOr7+l9lvNER1kVeH3GsYxUHvaWzGrsWAlYZKbVeqKx+3+1RvtmwZTlB1pnXDixMl0ltS9qqXoxH3PciHYg1y0ODex3AZcPe3wu/Lwc65a1zBMLwS3a+MmWMNYUdT6XBo3O9M64cSJnStpkuapWopO3PcsF4K9XttX1HbiqAZcmIPF77nC+G0v1yaoYFUfNLfanGkwawQZg2mccOJGvQZJ74d4UXTivGe5cXcMctGkx0UtV8d6SIPg8Irfa1jvQI3y2vS0t6KhYASACQCP9Y9inY1pKmmzRpY2QtPsiZP0fUzbKmpGujsC0z0uJiK0E0c14NImFPw++FFem662FtzcvQSbdx80XFodanMm/UDWO7nFJWzTNtZ0kr6Psg1AOoILc2GK8YpeG1H1uCgQ1SVYktiAi0LjteuHF+ER5MH3cm28/rbdZ24y89DUmjiSMmv0j4zj9ZNn0VAsWB4+fsZgnMI2CyvPpM1TdvcDqL3JHwUzRrDbpRFQPS423tgZ+MIntQEXtsbrNDC9CI+gD34t9zEvRU6cUiL3Do/VtaEcJXroulMtTTfiFLZJmzrSil0hbXk/nFJQx0HuBbu88C8eOmkF70xMVrBj8EhoD73fByzMAskb1nRg8PApEIC9b5yu67x2/QDs0+jqRPHg17qu/SPjuG/nPts2p9lsALiHrnslTmGbBlNH2nAqpO2UzTOqNBV25Fqwqwnz1TCXCoCf7z+BPQfexIY1HXXbxGo9YOqNBPwJHSfTiKrtgchK+1tPXminfnjtW9gPvtt11e+tml0vC2aDMIRy3MI2aVNH2tDHmZ6GGZiezTMu81kuBLvTDKjneFGpCENzD8Mrxu0B02/kulXei2k4DYKqAVUWAMLJC+3UD699cypRFhS366re2wKADy5fgLuuvxxdbUYWvgIRAOc0EWofktBCwxLKLGyTwylyXFXAblq1uCp7q+qWOTFZwX0791njNkwyL9jdZkB54ScmK1bOZDUcncww/DA0O6cHTJ/V/RTTcNI81QFVNPtTKlVCyQtt1w+vfYtCM3b6bfXeFgqE1Z0L0dXWgs27D+IrT7yEsgCKBGxY0xHqhm+YsFDONn4UOllERpdJ0nIQ9tjLvB+7m3BRL7xM+doyp8mySXcsasbG7UOR2ij1WV0tptEyp8nVDOS0XNcHlLwOceeFTnJDrautBRvWdFgrro3bhwDAEuqAUSh78PApx3NkwWTDpBu/So98du/buQ8/338isrGXeT/2WsJFXxrps2jUNkq3Wd1L1KVbpjn9dZh4MVEkvaE2fmaiasW1Y/AIyprdjVy+z54eTL04PSc97a1oKJqrai2tdFdbC+66/nLsOfBmZGMv86YYP77Qh0+erfKM6R0ew53XLk9k08mLtrh590HsGDximRniwo+JIklzgi6YV3cuxO7hsalyg0WylsB2SK0/iWvMZJ+az4kQ1f8rRK0UZV6wA959oQs0VZyhAqPgRlLU0hY37z6IL33/JQDAv716AgBw25VLI2mLrnXY+eOmyc3NzRNnxSVzHcsN2p1n4/YhTJQq2HPgTasSPcN4wU056x0eQ8ksDlOuCNtnKEqlKBeC3Q314qsTZ4GMpXxS1JqxdwwemfbaTbAH9e6w0zr0zdmwKjKFQS1PHD8PC9vYmXpwU86SfoZyL9jtPEiChG9HgS6EVOG8unOhpakDwOrOhY7nmVaA10cudjvhdue1y61J5/DJs9jy/MFEhJ/dZOV1NeFlomMbO1MPtfbAknyGci/YnTxI/OZCiRo7TfRrH3+vZf9109ZVYTdRFti8+yC2Dox60gx04dYypwmbdu1HT3sr7rx2uVVHNW7h52S/dNKE1AkN8BYEpntNpSF5E5Mt3FaI8r0knqFcCvZaRW5rBdkk8WA7ac5e7OpS2MmNYbfC3DpSuG0dGMWJ0+dxz7ahaUvGJDxfarmL6ZqQOqH5CQKTx2UUa9HMGxTVfgYz81CfMTcvrTDJnWD3K6jTYmetxyygDpzH+kd9m5r2vnEaj+w5hHJF2BbCTsLzxe166JqQPqH5CQIDjDEgz1GqCGx4cpA3UpnQkQnBtg6MRp6cLvMBSjp+BXVa7KxOmrFXM5EUdjL4yeuA0fPSA94KYUdtvvKyUtAntFLJiCbuXNTs6zr0tLeiWCDrGlSE4I1UJlSqzKUhpTJxI/MBSjp+BXXSQTZ6W+o1E/nVrnuHq/PSFwk1U8jGZb5y21zWXcY6FzVXRaH6yVvT1daCjTd2Vj1svJHKhIWedz/MVCZO5M4UE2RDLMkgGzfiMBP1tLdOy0tfy76chPmqluePHoXqt023Xbm0ygeeYcLALu9+HKlMcifYpVbXMqfJCj5Jg/+13j6vJoKozURuKxa3cOkoy/25ZXN08vzx0iY1ktdp8lLtoGkZM0x2UcetzLsvlYgorQS5EuzVUabRL3fqaZ+XySYuM5HdisWtrVG1y0umTifPn1oT1Nd3vII9B8YBTEXy6g9XWjbSmfzglsgvyrGVK8GuPpgQAoUCgTzk5I4Ku1B9PVdN2DbzsKgl5NSAIfV1VL/pxfPHaYK69f7nrPwxkof3HMTeo6enRdw2FAiTZYFiob4auAwDJLeHlyvBrs+OSda7tNM+W+Y0pSZXTS1qmTai2EB1C5ZS3S79eLz0Do+ZxUiqece8C/DS66eqJpGe9lbALNBh/M8w9aMqHKqpOLfujmGTJg8XO+0TMHLUVETyuWpqUetaRmG20De+nfZI/Kxietpb0VgkS2MnAj736+34cMclePbV41UTV+/wGEplY0VVLrMphgkXu3KOuXR3jIK0eLjYabxqyTY/LnW6SSeuFAhu1zKqDVT5m2oJsXomjq62FmxZf1VVxkfAmJjsVnRpiGlg8olUhsIoY1mL3An2tKCHEe994zQ2bh9CuWLYb91KtqnYVUKXmqzfhF9hEvXqKMjE4TTh6UvhuDeFvbSRyT9yTKulOtndMaNI9znppWPs6wrPZhjd5LFj8EjghF9hE+XqyK+Q9Wrz97IpHFWf0pKXiEkG3dTINvaMEoaXjl2VoD0H3gyU8Ctr+BGyXm3+diuBuLRodqdk4jIV5y5XTJoIw0vHTnOVEZJBEn4FJe0mBK+mG/16At5S/IZBy5wma4+FbfhMlJCwqccXN93d3aKvry+y8ycplLz8tsxSKDf3vLYxrn5lxYQQ5Hps2rUf9z61FxVh5Mn5wg0rPOeY8ds2Tg3MhA0R9QshuvXjuTfFJC2Uai299ACaR/tHseUOb22Ma1mXFRNCkOsRV3ZP1SPCzx4LwwShkHQDokZPl3nfzn3oHxlPulkWegCN6vOeNP0j49i0az9a5jShqaGAIgHFAuHFQyfx599/KVXXMSjSNPOFG1ZEOunLCaQYoScEw0hyr7HrLkY/338Cew68mRpzgh5Ak5aH3s7NcvDwKTzSdwhPvXwUgL/VRZqwM31F3Yc0Bc8x+Sf3Grt8oD542QIr6jNNWrEMoLntyqX41JVLUyModfPL+JkJXDp/NsopXV14RZq+Nu8+iId2H8St3+yNbeXR1dZi2e837dqfixUPk05yr7EDxgN11/WXY8+BN1MZVRiGxhj2RqqT7TmNqws/OJm+4ppMk97zYWYGM0KwA/leCkchLJyulx6en7XrmLTpKysb0Uy2mTGCHUhPHpmwiUpY2F2vrF9Du9wxcfYnLTV2mXwzowR7XtDNLmELCyezTtqDlLyS5OTU1daCDWs6rEpOWb6OTHrJvGDPi7DxipPZJSwzk9P5024bzso46B8Zt5K47TnwJlZcMjfV7WWySaYFe9qFjR+8CiYns0tYWqjT+dNsG87SOEjzdWTyQ6bdHZ2KWWQNKZjufWovPvUtd/e7qANdnM6f5gCbLI2DNF9HJj9kWmN3sy1nZWkO+NPiovbucTp/mr2K/OwxJD0u0nwdmfyQ+SRgdg9qlpbmwFR7pWBKe3vTSC2BLaNNH+sfRamcjXHBMLVIZRKwMNL22tmWs2bHZC2uftz2GPRak0A2xgXDBCVRG7sQYpsQYn1zc3Oo582iHVOGm6urjrSFnaexTV7Qa00SsjMuGCYImbaxO5F1DTiNpqQ0tskrqg2+mGCdWIaJi1wKdiDbEZJpNCWpbTo/WcHWgdHE2+SVrE/0TLZJYsM+V4I9aY+HsEhj2HlPeysaCkaOFQHgsf5RrMuQ1pvliZ7JLkmtdHMj2LNsKtDxomHGPYl1tbXg5u4l2Lz7IASAcjkdKwmGSTNJrb5zI9jTaL5wo5Zg9uLlEfckdtOqxdg6MJqqlQTDpJmkVt/pEOx79wLXXFN97Hd+B/ijPwLOnAE++tHp3/nMZ4x/J04An/gEfv9cCR848hYqQqBAhAWXfh64djlw6BDwe783/ft/8ifA2rXGb3/uc9Pf//KXgeuvB154Abjrrunvf+1rwK/9GvDv/w586UvT37/vPmDlSmDnTuAv/7LqrdPnSvjyqk9jb/Mi/NbwHlw+shNzL9BuxYMPAkuWAA8/DPzzP1e9dcnJs7jwQ5/HudnN+NjAU7jkxnuA+bOrv//DHwJz5gD/9E/AI49Mb98zzxj//+3fAtu3V783ezawY4fx91e/Cjz9NACgC8CecyUca7oQJx/8njGZ3H038Nxz1d9fvBj41381/r7rLuMaqlx+OXD//cbf69cD+/ZVv79ypXH9AOB3fxcYHa1+/6qrgL/+a+PvdeuAMS3S9Dd/E/jKV4y/V68Gzp6tfn/NGuBP/9T4Wx93gO+xN40//EPglltSOfYAAP/yL8CKFcC2bcC9905/32XsAQAeewxYsAB44AHjn04EY8+itRXYutX4OwNjr+vsWew5V8Jb5yYx74JGzG3+bWvsne65euq4fP7rHXsm6RDsITD3gga8e+G8qQt10duSbpIjb52btFYXpXIFb52bnC7YHTh9roTzpQoaimYN0iJh3gWNEbd4irkXNGBu69uAFK+GskD/yDh+2TuCj50reb73TDaZe0HDtHvcPzKOsqKIvnvhvFDHQeYjT7NI0EhT1QTTwG57mSVP+0FMMDbt2o97n9qLigCKBHzhhhVW2UQ/pDLydKYS1P1O3UcoVwQWzZ/NAiGDZG0/iAmfqG3vuRXsaXd9DOJ+l0Y3SMY/fB+ZqGMrcmmKyfNSN+0TFuMNvo9MGMwoU0yel7ocaJMP+D4yUZLpQhtOZDEJGMMwTFjkUmPn3CAMw8xkcinYgXQvddm+yjBMlORKsGdBYNazsZuF/jEMkzy5EexZ8YQJurGblf4xDJM8md48VSv6ZKVSfdCN3az0j2GY5Mmsxq5rsBvWdGQi6CPoxi4HtTAM45XMFrPWNdjxMxOZ8YQJsrHLnj4Mw3glUcEuhNgGYFt3d/cdfr9rp8Gm2RMmDPLeP4ZhwiGzphjWYBmGYezJ9OZpV1uLlepSbqIyDMPMdDKrsUvYDZAJAscEMHkm84I9zwm/VFgQhQcrA0zeybxgb5nThAIRAJFbN0AWROEyU5QBZuaSaRt7/8g4Nm4fQrli1A3csKYjlw8oByeFC2f/ZPJOpjV2KfAEACEExs9MJN2kSODgpHBhjyom72RasM8UgceCKHw4JoDJM5kvjcebigzDzFRyWxqPNS+GYZhqMr15yjAMw0yHBTvDMEzOYMHOMAyTM1iwZwS1qAjDMIwbmd88nQlw5CnDMH5gjT0DcOQpwzB+YMGeATgEnmEYP7ApJgNw5CnDMH5gwZ4ROBCLYRivsCmGYRgmZ7BgZxiGyRks2BmGYXIGC3aGYZicwYKdYRgmZ7BgZxiGyRks2BmGYXIGC3aGYZicwYKdYRgmZ7BgZxiGyRks2BmGYXJG6IKdiNqJ6NtE9FjY52YYhmFq40mwE9F3iOgYEQ1qxz9CRHuJaD8RfREAhBDDQojPRtHYmQ5XUWIYxgteszs+AOAfAXxXHiCiIoBNAD4MYBTAHiL6gRDi5bAbyXAVJYZhvONJYxdCPAvgTe3wFQD2mxr6BIDvAbgx5PYxJlxFiWEYr9RjY78UwCHl9SiAS4molYi+AeBXiehupy8T0Xoi6iOivuPHj9fRjJkBV1FiGMYroRfaEEKMAfgDD5+7H8D9ANDd3S3Cbkfe4CpKDMN4pR7B/jqAJcrrxeYxJiK4ihLDMF6oxxSzB8BlRPROImoC8EkAPwinWQzDMExQvLo7bgHwHIAVRDRKRJ8VQpQA/DGAHwF4BcAjQoih6JrKMAzDeMGTKUYIcavD8R8C+GHQHyeitQDWLl++POgpGIZhGI1EUwoIIbYJIdY3Nzcn2QyGYZhcwbliGIZhcgYLdoZhmJxBQiTvQk5ExwGMBPz6AgAnQmxOFuA+zwy4zzODevrcJoS4SD+YCsFeD0TUJ4ToTrodccJ9nhlwn2cGUfSZTTEMwzA5gwU7wzBMzsiDYL8/6QYkAPd5ZsB9nhmE3ufM29gZhmGYavKgsTMMwzAKmRbsdqX58oBdKUIiejsR/ZiIXjX/bzGPExH9g3kNfkFEq5JreTCIaAkR7SKil4loiIg+bx7Pc58vIKLniehFs89/YR5/JxHtNvv2sJlgD0Q0y3y933x/WaIdqAMiKhLRfxDRdvN1rvtMRAeI6CUieoGI+sxjkY7tzAp2pTTfagDvAXArEb0n2VaFxgMAPqId+yKAp4UQlwF42nwNGP2/zPy3HsA/x9TGMCkB+BMhxHsA9AC407yXee7zeQDXCSHeD2AlgI8QUQ+AvwHwd0KI5QDGAcj6wZ8FMG4e/zvzc1nl8zASB0pmQp+vFUKsVNwaox3bQohM/gNwFYAfKa/vBnB30u0KsX/LAAwqr/cCWGj+vRDAXvPvfwFwq93nsvoPwJMwaunOiD4DmANgAMCVMAJVGszj1hiHkUX1KvPvBvNzlHTbA/R1sSnIrgOwHQDNgD4fALBAOxbp2M6sxg6H0nwJtSUO3iGEOGL+/QaAd5h/5+o6mMvtXwWwGznvs2mSeAHAMQA/BvAagJPCSIkNVPfL6rP5/ikAWayPeB+APwNQMV+3Iv99FgCeIqJ+IlpvHot0bIdeGo+JHiGEIKLcuTMR0dsAbAVwlxDiLSKy3stjn4UQZQAriWg+gO8DeFeyLYoWIloD4JgQop+Irkm4OXFytRDidSK6GMCPieiX6ptRjO0sa+wzrTTfUSJaCADm/8fM47m4DkTUCEOoPySEeNw8nOs+S4QQJwHsgmGGmE9EUuFS+2X12Xy/GcBYvC2tmw8C+BgRHQDwPRjmmL9HvvsMIcTr5v/HYEzgVyDisZ1lwT7TSvP9AMCnzb8/DcMOLY//vrmb3gPglLLEywRkqObfBvCKEOJ/K2/luc8XmZo6iGg2jD2FV2AI+E+YH9P7LK/FJwD8RJhG2KwghLhbCLFYCLEMxvP6EyHEp5DjPhPRhUQ0V/4N4AYAg4h6bCe9sVDnpsRHAeyDYZv886TbE2K/tgA4AmASho3tszBsi08DeBXATgBvNz9LMLyDXgPwEoDupNsfoL9Xw7BD/gLAC+a/j+a8z+8D8B9mnwcBbDCPtwN4HsB+AI8CmGUev8B8vd98vz3pPtTZ/2sAbM97n82+vWj+G5JyKuqxzZGnDMMwOSPLphiGYRjGBhbsDMMwOYMFO8MwTM5gwc4wDJMzWLAzDMPkDBbsDMMwOYMFO8MwTM5gwc4wDJMz/j8cb2fCTbgXNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the result\n",
    "plt.semilogy(T2_tfinal,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()\n",
    "# in this scatter plot the data has been use is\n",
    "# training data it self "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "37f1c8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T2_tfinal)\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ddbbab14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_tfinal>T2_trlim).sum()/T2_tfinal.shape[0]}')\n",
    "# ofcourse 100% because we using train and test in the same dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d62062",
   "metadata": {},
   "source": [
    "## Scaling Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7a38c201",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ts = scl_pick('std', df_test) ##robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "31dea5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ts = round(X_ts, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f76d024e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.3651</td>\n",
       "      <td>0.2699</td>\n",
       "      <td>0.3347</td>\n",
       "      <td>-0.7111</td>\n",
       "      <td>-0.9676</td>\n",
       "      <td>-0.1787</td>\n",
       "      <td>0.6909</td>\n",
       "      <td>1.0440</td>\n",
       "      <td>0.1237</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2658</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>0.8418</td>\n",
       "      <td>0.2861</td>\n",
       "      <td>-0.0898</td>\n",
       "      <td>-0.6886</td>\n",
       "      <td>0.3738</td>\n",
       "      <td>-0.2311</td>\n",
       "      <td>0.1161</td>\n",
       "      <td>-1.4720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1012</td>\n",
       "      <td>-0.6851</td>\n",
       "      <td>-0.5414</td>\n",
       "      <td>0.4295</td>\n",
       "      <td>1.2144</td>\n",
       "      <td>2.4034</td>\n",
       "      <td>-0.1390</td>\n",
       "      <td>-0.5592</td>\n",
       "      <td>-1.6050</td>\n",
       "      <td>-0.0636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5275</td>\n",
       "      <td>-0.0753</td>\n",
       "      <td>-1.5869</td>\n",
       "      <td>0.3743</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>0.1563</td>\n",
       "      <td>2.0125</td>\n",
       "      <td>-0.2243</td>\n",
       "      <td>-1.2538</td>\n",
       "      <td>-1.9615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.1011</td>\n",
       "      <td>-0.6847</td>\n",
       "      <td>-0.5349</td>\n",
       "      <td>-0.7397</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>2.0507</td>\n",
       "      <td>0.5142</td>\n",
       "      <td>-0.1422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6653</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.8293</td>\n",
       "      <td>-0.3195</td>\n",
       "      <td>0.1727</td>\n",
       "      <td>0.9271</td>\n",
       "      <td>2.5620</td>\n",
       "      <td>-0.1945</td>\n",
       "      <td>1.8914</td>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.0444</td>\n",
       "      <td>0.3855</td>\n",
       "      <td>0.7305</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>-0.6921</td>\n",
       "      <td>-0.6021</td>\n",
       "      <td>0.0861</td>\n",
       "      <td>1.5882</td>\n",
       "      <td>-1.0752</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7166</td>\n",
       "      <td>0.0722</td>\n",
       "      <td>0.5763</td>\n",
       "      <td>-0.1959</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.6773</td>\n",
       "      <td>-2.4370</td>\n",
       "      <td>-0.1420</td>\n",
       "      <td>0.3920</td>\n",
       "      <td>0.0650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.3952</td>\n",
       "      <td>-0.3166</td>\n",
       "      <td>-0.4032</td>\n",
       "      <td>-0.7920</td>\n",
       "      <td>0.8245</td>\n",
       "      <td>1.9070</td>\n",
       "      <td>-0.1258</td>\n",
       "      <td>-1.7890</td>\n",
       "      <td>0.5142</td>\n",
       "      <td>-0.8057</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.1780</td>\n",
       "      <td>-0.3868</td>\n",
       "      <td>-0.2828</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>-1.0175</td>\n",
       "      <td>0.4095</td>\n",
       "      <td>-1.1743</td>\n",
       "      <td>-0.1735</td>\n",
       "      <td>-0.1485</td>\n",
       "      <td>1.1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0.1424</td>\n",
       "      <td>-0.8476</td>\n",
       "      <td>0.2008</td>\n",
       "      <td>0.3941</td>\n",
       "      <td>1.3808</td>\n",
       "      <td>-0.8909</td>\n",
       "      <td>-0.8674</td>\n",
       "      <td>0.1411</td>\n",
       "      <td>-1.0752</td>\n",
       "      <td>-0.2811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.9463</td>\n",
       "      <td>0.1173</td>\n",
       "      <td>1.3469</td>\n",
       "      <td>-0.3777</td>\n",
       "      <td>-0.2085</td>\n",
       "      <td>0.3644</td>\n",
       "      <td>0.0739</td>\n",
       "      <td>1.0983</td>\n",
       "      <td>-0.3099</td>\n",
       "      <td>0.0854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1.8542</td>\n",
       "      <td>0.4928</td>\n",
       "      <td>1.0811</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>-1.5099</td>\n",
       "      <td>-0.0064</td>\n",
       "      <td>-0.5363</td>\n",
       "      <td>-0.3697</td>\n",
       "      <td>0.5142</td>\n",
       "      <td>0.8459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3438</td>\n",
       "      <td>1.8349</td>\n",
       "      <td>-0.6847</td>\n",
       "      <td>-1.6136</td>\n",
       "      <td>0.6572</td>\n",
       "      <td>-1.1037</td>\n",
       "      <td>-0.8295</td>\n",
       "      <td>1.0874</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>-1.5958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.8201</td>\n",
       "      <td>-0.1542</td>\n",
       "      <td>-0.2215</td>\n",
       "      <td>2.3435</td>\n",
       "      <td>1.0289</td>\n",
       "      <td>1.4602</td>\n",
       "      <td>-0.5098</td>\n",
       "      <td>0.3696</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>0.6419</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6151</td>\n",
       "      <td>1.8692</td>\n",
       "      <td>-1.5130</td>\n",
       "      <td>-1.6348</td>\n",
       "      <td>0.8598</td>\n",
       "      <td>0.9662</td>\n",
       "      <td>1.2544</td>\n",
       "      <td>1.1146</td>\n",
       "      <td>-1.0699</td>\n",
       "      <td>0.4376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.1524</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>0.7689</td>\n",
       "      <td>1.2660</td>\n",
       "      <td>-0.1549</td>\n",
       "      <td>1.2707</td>\n",
       "      <td>-0.4039</td>\n",
       "      <td>0.5349</td>\n",
       "      <td>2.1036</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3396</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>-0.2990</td>\n",
       "      <td>-0.7750</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>1.6244</td>\n",
       "      <td>-0.3684</td>\n",
       "      <td>1.2154</td>\n",
       "      <td>0.8274</td>\n",
       "      <td>-1.5176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>-0.0961</td>\n",
       "      <td>0.3324</td>\n",
       "      <td>1.7136</td>\n",
       "      <td>1.8844</td>\n",
       "      <td>-0.5230</td>\n",
       "      <td>-0.2174</td>\n",
       "      <td>0.5142</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1850</td>\n",
       "      <td>0.1541</td>\n",
       "      <td>0.8711</td>\n",
       "      <td>-1.2569</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.5908</td>\n",
       "      <td>0.8266</td>\n",
       "      <td>1.2106</td>\n",
       "      <td>-1.7305</td>\n",
       "      <td>0.6973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0       1       2       3       4       5       6       7       8   \\\n",
       "0     0.0026  0.3651  0.2699  0.3347 -0.7111 -0.9676 -0.1787  0.6909  1.0440   \n",
       "1     0.1012 -0.6851 -0.5414  0.4295  1.2144  2.4034 -0.1390 -0.5592 -1.6050   \n",
       "2     0.0255  0.1011 -0.6847 -0.5349 -0.7397  0.0207  0.1126  2.0507  0.5142   \n",
       "3    -0.0444  0.3855  0.7305  0.0079 -0.6921 -0.6021  0.0861  1.5882 -1.0752   \n",
       "4    -0.3952 -0.3166 -0.4032 -0.7920  0.8245  1.9070 -0.1258 -1.7890  0.5142   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "1995  0.1424 -0.8476  0.2008  0.3941  1.3808 -0.8909 -0.8674  0.1411 -1.0752   \n",
       "1996  1.8542  0.4928  1.0811  0.6752 -1.5099 -0.0064 -0.5363 -0.3697  0.5142   \n",
       "1997  1.8201 -0.1542 -0.2215  2.3435  1.0289  1.4602 -0.5098  0.3696 -0.0156   \n",
       "1998  0.1524  0.7075  0.7689  1.2660 -0.1549  1.2707 -0.4039  0.5349  2.1036   \n",
       "1999  0.1794  0.0315 -0.0961  0.3324  1.7136  1.8844 -0.5230 -0.2174  0.5142   \n",
       "\n",
       "          9   ...      42      43      44      45      46      47      48  \\\n",
       "0     0.1237  ... -0.2658  0.0226  0.8418  0.2861 -0.0898 -0.6886  0.3738   \n",
       "1    -0.0636  ...  0.5275 -0.0753 -1.5869  0.3743  0.0953  0.1563  2.0125   \n",
       "2    -0.1422  ...  0.6653  0.0023 -0.8293 -0.3195  0.1727  0.9271  2.5620   \n",
       "3     0.0086  ... -0.7166  0.0722  0.5763 -0.1959  0.0231  0.6773 -2.4370   \n",
       "4    -0.8057  ... -1.1780 -0.3868 -0.2828  0.0389 -1.0175  0.4095 -1.1743   \n",
       "...      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "1995 -0.2811  ... -0.9463  0.1173  1.3469 -0.3777 -0.2085  0.3644  0.0739   \n",
       "1996  0.8459  ...  0.3438  1.8349 -0.6847 -1.6136  0.6572 -1.1037 -0.8295   \n",
       "1997  0.6419  ...  1.6151  1.8692 -1.5130 -1.6348  0.8598  0.9662  1.2544   \n",
       "1998  0.0975  ...  0.3396  0.1812 -0.2990 -0.7750  0.0076  1.6244 -0.3684   \n",
       "1999  0.1054  ...  1.1850  0.1541  0.8711 -1.2569 -0.0002 -0.5908  0.8266   \n",
       "\n",
       "          49      50      51  \n",
       "0    -0.2311  0.1161 -1.4720  \n",
       "1    -0.2243 -1.2538 -1.9615  \n",
       "2    -0.1945  1.8914  0.6980  \n",
       "3    -0.1420  0.3920  0.0650  \n",
       "4    -0.1735 -0.1485  1.1126  \n",
       "...      ...     ...     ...  \n",
       "1995  1.0983 -0.3099  0.0854  \n",
       "1996  1.0874  1.0094 -1.5958  \n",
       "1997  1.1146 -1.0699  0.4376  \n",
       "1998  1.2154  0.8274 -1.5176  \n",
       "1999  1.2106 -1.7305  0.6973  \n",
       "\n",
       "[2000 rows x 52 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1cc81e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_ts)\n",
    "plt.title('data test scaling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "309e002d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>0.343859</td>\n",
       "      <td>0.502126</td>\n",
       "      <td>-1.431359</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>0.278542</td>\n",
       "      <td>-0.009557</td>\n",
       "      <td>-0.292773</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>0.098431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.488936</td>\n",
       "      <td>0.000962</td>\n",
       "      <td>1.050090</td>\n",
       "      <td>-0.051818</td>\n",
       "      <td>-0.122736</td>\n",
       "      <td>1.319983</td>\n",
       "      <td>0.471559</td>\n",
       "      <td>-0.720571</td>\n",
       "      <td>0.279630</td>\n",
       "      <td>0.250397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017875</td>\n",
       "      <td>-0.103316</td>\n",
       "      <td>1.215653</td>\n",
       "      <td>1.035368</td>\n",
       "      <td>-0.866377</td>\n",
       "      <td>1.068875</td>\n",
       "      <td>0.107804</td>\n",
       "      <td>-0.032734</td>\n",
       "      <td>0.510938</td>\n",
       "      <td>-0.071378</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.237481</td>\n",
       "      <td>-0.024152</td>\n",
       "      <td>-1.590601</td>\n",
       "      <td>-0.185906</td>\n",
       "      <td>0.049624</td>\n",
       "      <td>0.089123</td>\n",
       "      <td>-1.227605</td>\n",
       "      <td>-0.760505</td>\n",
       "      <td>0.460598</td>\n",
       "      <td>-0.634866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.007459</td>\n",
       "      <td>-0.075750</td>\n",
       "      <td>-0.821517</td>\n",
       "      <td>1.240929</td>\n",
       "      <td>-0.149278</td>\n",
       "      <td>-1.229449</td>\n",
       "      <td>0.308995</td>\n",
       "      <td>-0.467397</td>\n",
       "      <td>1.023929</td>\n",
       "      <td>-0.120262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.901543</td>\n",
       "      <td>0.004238</td>\n",
       "      <td>0.029987</td>\n",
       "      <td>0.562959</td>\n",
       "      <td>0.098256</td>\n",
       "      <td>0.231510</td>\n",
       "      <td>0.029317</td>\n",
       "      <td>-0.740779</td>\n",
       "      <td>0.173916</td>\n",
       "      <td>1.722067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.029225</td>\n",
       "      <td>-0.045122</td>\n",
       "      <td>0.065220</td>\n",
       "      <td>1.685042</td>\n",
       "      <td>-0.694087</td>\n",
       "      <td>-1.261244</td>\n",
       "      <td>0.476653</td>\n",
       "      <td>0.392438</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.128838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030736</td>\n",
       "      <td>0.025713</td>\n",
       "      <td>-1.110695</td>\n",
       "      <td>0.302375</td>\n",
       "      <td>0.107554</td>\n",
       "      <td>-0.068286</td>\n",
       "      <td>0.381272</td>\n",
       "      <td>-0.649365</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>-0.002535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.550795</td>\n",
       "      <td>0.497001</td>\n",
       "      <td>-0.325151</td>\n",
       "      <td>-0.085064</td>\n",
       "      <td>-0.084087</td>\n",
       "      <td>1.404994</td>\n",
       "      <td>0.124570</td>\n",
       "      <td>0.703725</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.918704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.684011</td>\n",
       "      <td>1.507821</td>\n",
       "      <td>-0.418568</td>\n",
       "      <td>-0.532509</td>\n",
       "      <td>-0.688446</td>\n",
       "      <td>1.199804</td>\n",
       "      <td>0.424744</td>\n",
       "      <td>-0.799476</td>\n",
       "      <td>0.725778</td>\n",
       "      <td>0.260288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-0.163035</td>\n",
       "      <td>-0.333028</td>\n",
       "      <td>-0.133843</td>\n",
       "      <td>1.615252</td>\n",
       "      <td>-1.429812</td>\n",
       "      <td>-0.130251</td>\n",
       "      <td>0.476653</td>\n",
       "      <td>-0.748315</td>\n",
       "      <td>-0.002052</td>\n",
       "      <td>-1.133966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.391172</td>\n",
       "      <td>-0.360829</td>\n",
       "      <td>-1.326130</td>\n",
       "      <td>0.818484</td>\n",
       "      <td>-0.825761</td>\n",
       "      <td>-1.564979</td>\n",
       "      <td>-0.596009</td>\n",
       "      <td>0.429321</td>\n",
       "      <td>-0.080515</td>\n",
       "      <td>-1.161925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-0.571599</td>\n",
       "      <td>0.006947</td>\n",
       "      <td>0.515052</td>\n",
       "      <td>-0.010199</td>\n",
       "      <td>-0.163247</td>\n",
       "      <td>0.423891</td>\n",
       "      <td>-0.043088</td>\n",
       "      <td>0.200731</td>\n",
       "      <td>-0.002052</td>\n",
       "      <td>1.501493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155266</td>\n",
       "      <td>-0.683676</td>\n",
       "      <td>-0.539952</td>\n",
       "      <td>0.201177</td>\n",
       "      <td>2.053568</td>\n",
       "      <td>-0.086248</td>\n",
       "      <td>-1.668594</td>\n",
       "      <td>0.513518</td>\n",
       "      <td>0.424762</td>\n",
       "      <td>-0.464596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-0.710404</td>\n",
       "      <td>-0.195201</td>\n",
       "      <td>-0.428561</td>\n",
       "      <td>-0.596428</td>\n",
       "      <td>0.060264</td>\n",
       "      <td>0.887190</td>\n",
       "      <td>-0.680190</td>\n",
       "      <td>-0.142824</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>1.848829</td>\n",
       "      <td>...</td>\n",
       "      <td>1.146522</td>\n",
       "      <td>-0.544637</td>\n",
       "      <td>0.694782</td>\n",
       "      <td>-1.311733</td>\n",
       "      <td>1.699552</td>\n",
       "      <td>0.893150</td>\n",
       "      <td>0.064011</td>\n",
       "      <td>0.524584</td>\n",
       "      <td>0.313673</td>\n",
       "      <td>0.157137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.283998</td>\n",
       "      <td>-0.927219</td>\n",
       "      <td>0.308233</td>\n",
       "      <td>0.592525</td>\n",
       "      <td>0.227898</td>\n",
       "      <td>-0.107540</td>\n",
       "      <td>-0.411937</td>\n",
       "      <td>-0.222544</td>\n",
       "      <td>-1.541023</td>\n",
       "      <td>0.303401</td>\n",
       "      <td>...</td>\n",
       "      <td>1.298499</td>\n",
       "      <td>-0.461651</td>\n",
       "      <td>1.548485</td>\n",
       "      <td>1.273875</td>\n",
       "      <td>-0.392360</td>\n",
       "      <td>-0.127070</td>\n",
       "      <td>0.350758</td>\n",
       "      <td>0.627545</td>\n",
       "      <td>-0.976396</td>\n",
       "      <td>0.780990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.392830</td>\n",
       "      <td>-1.012978</td>\n",
       "      <td>1.466421</td>\n",
       "      <td>2.059365</td>\n",
       "      <td>1.452554</td>\n",
       "      <td>1.891003</td>\n",
       "      <td>-0.093386</td>\n",
       "      <td>1.345279</td>\n",
       "      <td>-0.515042</td>\n",
       "      <td>-0.032785</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082680</td>\n",
       "      <td>-0.352458</td>\n",
       "      <td>-1.354265</td>\n",
       "      <td>1.496511</td>\n",
       "      <td>-0.050502</td>\n",
       "      <td>-0.099311</td>\n",
       "      <td>2.401877</td>\n",
       "      <td>0.827693</td>\n",
       "      <td>0.077160</td>\n",
       "      <td>0.661589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -0.007459  0.343859  0.502126 -1.431359 -0.084087  0.278542 -0.009557   \n",
       "1    0.017875 -0.103316  1.215653  1.035368 -0.866377  1.068875  0.107804   \n",
       "2   -0.007459 -0.075750 -0.821517  1.240929 -0.149278 -1.229449  0.308995   \n",
       "3   -0.029225 -0.045122  0.065220  1.685042 -0.694087 -1.261244  0.476653   \n",
       "4    1.550795  0.497001 -0.325151 -0.085064 -0.084087  1.404994  0.124570   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495 -0.163035 -0.333028 -0.133843  1.615252 -1.429812 -0.130251  0.476653   \n",
       "496 -0.571599  0.006947  0.515052 -0.010199 -0.163247  0.423891 -0.043088   \n",
       "497 -0.710404 -0.195201 -0.428561 -0.596428  0.060264  0.887190 -0.680190   \n",
       "498 -0.283998 -0.927219  0.308233  0.592525  0.227898 -0.107540 -0.411937   \n",
       "499 -0.392830 -1.012978  1.466421  2.059365  1.452554  1.891003 -0.093386   \n",
       "\n",
       "           7         8         9   ...        42        43        44  \\\n",
       "0   -0.292773  0.510938  0.098431  ... -0.488936  0.000962  1.050090   \n",
       "1   -0.032734  0.510938 -0.071378  ... -1.237481 -0.024152 -1.590601   \n",
       "2   -0.467397  1.023929 -0.120262  ...  0.901543  0.004238  0.029987   \n",
       "3    0.392438 -0.515042 -0.128838  ... -0.030736  0.025713 -1.110695   \n",
       "4    0.703725 -0.515042 -0.918704  ... -0.684011  1.507821 -0.418568   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "495 -0.748315 -0.002052 -1.133966  ...  0.391172 -0.360829 -1.326130   \n",
       "496  0.200731 -0.002052  1.501493  ...  0.155266 -0.683676 -0.539952   \n",
       "497 -0.142824 -0.515042  1.848829  ...  1.146522 -0.544637  0.694782   \n",
       "498 -0.222544 -1.541023  0.303401  ...  1.298499 -0.461651  1.548485   \n",
       "499  1.345279 -0.515042 -0.032785  ...  0.082680 -0.352458 -1.354265   \n",
       "\n",
       "           45        46        47        48        49        50        51  \n",
       "0   -0.051818 -0.122736  1.319983  0.471559 -0.720571  0.279630  0.250397  \n",
       "1   -0.185906  0.049624  0.089123 -1.227605 -0.760505  0.460598 -0.634866  \n",
       "2    0.562959  0.098256  0.231510  0.029317 -0.740779  0.173916  1.722067  \n",
       "3    0.302375  0.107554 -0.068286  0.381272 -0.649365  0.969458 -0.002535  \n",
       "4   -0.532509 -0.688446  1.199804  0.424744 -0.799476  0.725778  0.260288  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "495  0.818484 -0.825761 -1.564979 -0.596009  0.429321 -0.080515 -1.161925  \n",
       "496  0.201177  2.053568 -0.086248 -1.668594  0.513518  0.424762 -0.464596  \n",
       "497 -1.311733  1.699552  0.893150  0.064011  0.524584  0.313673  0.157137  \n",
       "498  1.273875 -0.392360 -0.127070  0.350758  0.627545 -0.976396  0.780990  \n",
       "499  1.496511 -0.050502 -0.099311  2.401877  0.827693  0.077160  0.661589  \n",
       "\n",
       "[500 rows x 52 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417c9e03",
   "metadata": {},
   "source": [
    "## Testing Process using Normal Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "96cba9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_8784/1694315934.py:3: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  Kt = np.matmul(X_ts, df_scl.T)\n"
     ]
    }
   ],
   "source": [
    "# linear kernel\n",
    "# Kt = np.matmul(X_ts, df_train.T)\n",
    "Kt = np.matmul(X_ts, df_scl.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "45bbb38a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 500)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1cfc3dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Kt)\n",
    "plt.title('Kt')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "85177e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7ce3418a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centralize the kernel\n",
    "st = (len(X_ts),len(df_train)) # 2000 or 960\n",
    "unit_t = np.ones(st)/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2acab630",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit_t = pd.DataFrame(unit_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "35663c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1      2      3      4      5      6      7      8      9    \\\n",
       "0     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "2     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "3     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "4     0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "1995  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1996  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1997  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1998  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1999  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "\n",
       "      ...    490    491    492    493    494    495    496    497    498  \\\n",
       "0     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "2     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "3     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "4     ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "1995  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1996  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1997  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1998  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "1999  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "\n",
       "        499  \n",
       "0     0.002  \n",
       "1     0.002  \n",
       "2     0.002  \n",
       "3     0.002  \n",
       "4     0.002  \n",
       "...     ...  \n",
       "1995  0.002  \n",
       "1996  0.002  \n",
       "1997  0.002  \n",
       "1998  0.002  \n",
       "1999  0.002  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0ff59d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = Kt-unit_t@K-Kt@unit+unit_t@K@unit\n",
    "# the result are NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "472b1026",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = round(Kt_c, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "21f69d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.6289</td>\n",
       "      <td>0.9060</td>\n",
       "      <td>-4.0662</td>\n",
       "      <td>2.4642</td>\n",
       "      <td>-2.9974</td>\n",
       "      <td>-3.1695</td>\n",
       "      <td>3.3708</td>\n",
       "      <td>-0.4704</td>\n",
       "      <td>-2.4411</td>\n",
       "      <td>-0.5296</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.3831</td>\n",
       "      <td>0.8513</td>\n",
       "      <td>2.0701</td>\n",
       "      <td>7.9550</td>\n",
       "      <td>-5.6627</td>\n",
       "      <td>3.9446</td>\n",
       "      <td>-1.3967</td>\n",
       "      <td>-0.9623</td>\n",
       "      <td>-2.5832</td>\n",
       "      <td>-1.0449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.5811</td>\n",
       "      <td>0.1530</td>\n",
       "      <td>-9.2776</td>\n",
       "      <td>0.3951</td>\n",
       "      <td>4.3314</td>\n",
       "      <td>-1.0268</td>\n",
       "      <td>-2.3032</td>\n",
       "      <td>-3.5360</td>\n",
       "      <td>4.4956</td>\n",
       "      <td>-9.0719</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.1037</td>\n",
       "      <td>-1.3611</td>\n",
       "      <td>-3.5530</td>\n",
       "      <td>16.4052</td>\n",
       "      <td>-3.5088</td>\n",
       "      <td>3.8032</td>\n",
       "      <td>-4.9841</td>\n",
       "      <td>2.0554</td>\n",
       "      <td>1.6501</td>\n",
       "      <td>17.2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.7720</td>\n",
       "      <td>-5.5447</td>\n",
       "      <td>10.4793</td>\n",
       "      <td>12.0165</td>\n",
       "      <td>3.4317</td>\n",
       "      <td>-0.6239</td>\n",
       "      <td>-0.2566</td>\n",
       "      <td>-4.9396</td>\n",
       "      <td>-10.0674</td>\n",
       "      <td>2.5970</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3519</td>\n",
       "      <td>5.8730</td>\n",
       "      <td>0.6310</td>\n",
       "      <td>9.1658</td>\n",
       "      <td>0.9667</td>\n",
       "      <td>-5.3859</td>\n",
       "      <td>-7.2175</td>\n",
       "      <td>-0.2755</td>\n",
       "      <td>-4.3783</td>\n",
       "      <td>11.1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.1334</td>\n",
       "      <td>7.9443</td>\n",
       "      <td>4.6449</td>\n",
       "      <td>10.5122</td>\n",
       "      <td>-4.5408</td>\n",
       "      <td>-10.4906</td>\n",
       "      <td>7.7444</td>\n",
       "      <td>9.2618</td>\n",
       "      <td>-1.1518</td>\n",
       "      <td>-8.1986</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.9648</td>\n",
       "      <td>-9.6546</td>\n",
       "      <td>4.7692</td>\n",
       "      <td>-2.0399</td>\n",
       "      <td>-0.9167</td>\n",
       "      <td>3.3312</td>\n",
       "      <td>7.0395</td>\n",
       "      <td>-4.1171</td>\n",
       "      <td>-5.3652</td>\n",
       "      <td>-11.7010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.2109</td>\n",
       "      <td>4.4823</td>\n",
       "      <td>-1.3871</td>\n",
       "      <td>-7.3820</td>\n",
       "      <td>10.5803</td>\n",
       "      <td>7.0647</td>\n",
       "      <td>-1.3723</td>\n",
       "      <td>6.9745</td>\n",
       "      <td>1.2519</td>\n",
       "      <td>-0.8746</td>\n",
       "      <td>...</td>\n",
       "      <td>6.7427</td>\n",
       "      <td>2.0788</td>\n",
       "      <td>-5.4818</td>\n",
       "      <td>-8.7334</td>\n",
       "      <td>7.9030</td>\n",
       "      <td>2.2938</td>\n",
       "      <td>1.8805</td>\n",
       "      <td>1.3071</td>\n",
       "      <td>-1.1567</td>\n",
       "      <td>-2.6639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-1.9158</td>\n",
       "      <td>-5.8954</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.1905</td>\n",
       "      <td>-2.5424</td>\n",
       "      <td>-2.9060</td>\n",
       "      <td>-1.5991</td>\n",
       "      <td>-0.3721</td>\n",
       "      <td>-0.8218</td>\n",
       "      <td>-9.2763</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1767</td>\n",
       "      <td>-1.7402</td>\n",
       "      <td>-6.9983</td>\n",
       "      <td>-8.4692</td>\n",
       "      <td>1.9516</td>\n",
       "      <td>-0.8772</td>\n",
       "      <td>-3.9510</td>\n",
       "      <td>-1.3758</td>\n",
       "      <td>7.6816</td>\n",
       "      <td>4.6769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-8.3922</td>\n",
       "      <td>6.6267</td>\n",
       "      <td>-7.9222</td>\n",
       "      <td>1.4692</td>\n",
       "      <td>-0.9445</td>\n",
       "      <td>-4.6046</td>\n",
       "      <td>-1.7096</td>\n",
       "      <td>-7.3825</td>\n",
       "      <td>-2.1680</td>\n",
       "      <td>3.7503</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1062</td>\n",
       "      <td>4.3621</td>\n",
       "      <td>15.5989</td>\n",
       "      <td>14.8060</td>\n",
       "      <td>-0.8814</td>\n",
       "      <td>14.8551</td>\n",
       "      <td>7.5673</td>\n",
       "      <td>-1.8712</td>\n",
       "      <td>-2.1669</td>\n",
       "      <td>-2.0149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-4.0755</td>\n",
       "      <td>-4.0312</td>\n",
       "      <td>-0.6799</td>\n",
       "      <td>-1.0568</td>\n",
       "      <td>10.7642</td>\n",
       "      <td>7.4575</td>\n",
       "      <td>-14.6388</td>\n",
       "      <td>-9.1693</td>\n",
       "      <td>3.7343</td>\n",
       "      <td>-2.3938</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.4157</td>\n",
       "      <td>5.5184</td>\n",
       "      <td>18.4892</td>\n",
       "      <td>16.5164</td>\n",
       "      <td>-2.6164</td>\n",
       "      <td>-1.8528</td>\n",
       "      <td>-1.5770</td>\n",
       "      <td>10.6478</td>\n",
       "      <td>8.7060</td>\n",
       "      <td>22.9540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-0.0724</td>\n",
       "      <td>3.7377</td>\n",
       "      <td>-0.6903</td>\n",
       "      <td>-1.7563</td>\n",
       "      <td>5.5177</td>\n",
       "      <td>1.1977</td>\n",
       "      <td>0.7749</td>\n",
       "      <td>2.4325</td>\n",
       "      <td>-23.7556</td>\n",
       "      <td>-10.2547</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6523</td>\n",
       "      <td>16.8524</td>\n",
       "      <td>-14.6476</td>\n",
       "      <td>4.0207</td>\n",
       "      <td>-8.0666</td>\n",
       "      <td>-7.6458</td>\n",
       "      <td>11.4412</td>\n",
       "      <td>10.1550</td>\n",
       "      <td>-6.7117</td>\n",
       "      <td>-2.8091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-3.9493</td>\n",
       "      <td>-8.0059</td>\n",
       "      <td>3.4067</td>\n",
       "      <td>-3.1852</td>\n",
       "      <td>-1.8636</td>\n",
       "      <td>-7.8393</td>\n",
       "      <td>-1.4350</td>\n",
       "      <td>2.4190</td>\n",
       "      <td>-11.4035</td>\n",
       "      <td>-19.3142</td>\n",
       "      <td>...</td>\n",
       "      <td>8.7415</td>\n",
       "      <td>6.3180</td>\n",
       "      <td>-10.1672</td>\n",
       "      <td>-11.4365</td>\n",
       "      <td>-12.2321</td>\n",
       "      <td>-8.1577</td>\n",
       "      <td>0.7146</td>\n",
       "      <td>7.2233</td>\n",
       "      <td>2.8861</td>\n",
       "      <td>6.7138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1        2        3        4        5        6       7    \\\n",
       "0     0.6289  0.9060  -4.0662   2.4642  -2.9974  -3.1695   3.3708 -0.4704   \n",
       "1    -1.5811  0.1530  -9.2776   0.3951   4.3314  -1.0268  -2.3032 -3.5360   \n",
       "2     3.7720 -5.5447  10.4793  12.0165   3.4317  -0.6239  -0.2566 -4.9396   \n",
       "3    -0.1334  7.9443   4.6449  10.5122  -4.5408 -10.4906   7.7444  9.2618   \n",
       "4     3.2109  4.4823  -1.3871  -7.3820  10.5803   7.0647  -1.3723  6.9745   \n",
       "...      ...     ...      ...      ...      ...      ...      ...     ...   \n",
       "1995 -1.9158 -5.8954   0.0381   0.1905  -2.5424  -2.9060  -1.5991 -0.3721   \n",
       "1996 -8.3922  6.6267  -7.9222   1.4692  -0.9445  -4.6046  -1.7096 -7.3825   \n",
       "1997 -4.0755 -4.0312  -0.6799  -1.0568  10.7642   7.4575 -14.6388 -9.1693   \n",
       "1998 -0.0724  3.7377  -0.6903  -1.7563   5.5177   1.1977   0.7749  2.4325   \n",
       "1999 -3.9493 -8.0059   3.4067  -3.1852  -1.8636  -7.8393  -1.4350  2.4190   \n",
       "\n",
       "          8        9    ...     490      491      492      493      494  \\\n",
       "0     -2.4411  -0.5296  ... -6.3831   0.8513   2.0701   7.9550  -5.6627   \n",
       "1      4.4956  -9.0719  ... -1.1037  -1.3611  -3.5530  16.4052  -3.5088   \n",
       "2    -10.0674   2.5970  ...  2.3519   5.8730   0.6310   9.1658   0.9667   \n",
       "3     -1.1518  -8.1986  ... -4.9648  -9.6546   4.7692  -2.0399  -0.9167   \n",
       "4      1.2519  -0.8746  ...  6.7427   2.0788  -5.4818  -8.7334   7.9030   \n",
       "...       ...      ...  ...     ...      ...      ...      ...      ...   \n",
       "1995  -0.8218  -9.2763  ... -0.1767  -1.7402  -6.9983  -8.4692   1.9516   \n",
       "1996  -2.1680   3.7503  ... -0.1062   4.3621  15.5989  14.8060  -0.8814   \n",
       "1997   3.7343  -2.3938  ... -3.4157   5.5184  18.4892  16.5164  -2.6164   \n",
       "1998 -23.7556 -10.2547  ...  3.6523  16.8524 -14.6476   4.0207  -8.0666   \n",
       "1999 -11.4035 -19.3142  ...  8.7415   6.3180 -10.1672 -11.4365 -12.2321   \n",
       "\n",
       "          495      496      497     498      499  \n",
       "0      3.9446  -1.3967  -0.9623 -2.5832  -1.0449  \n",
       "1      3.8032  -4.9841   2.0554  1.6501  17.2528  \n",
       "2     -5.3859  -7.2175  -0.2755 -4.3783  11.1020  \n",
       "3      3.3312   7.0395  -4.1171 -5.3652 -11.7010  \n",
       "4      2.2938   1.8805   1.3071 -1.1567  -2.6639  \n",
       "...       ...      ...      ...     ...      ...  \n",
       "1995  -0.8772  -3.9510  -1.3758  7.6816   4.6769  \n",
       "1996  14.8551   7.5673  -1.8712 -2.1669  -2.0149  \n",
       "1997  -1.8528  -1.5770  10.6478  8.7060  22.9540  \n",
       "1998  -7.6458  11.4412  10.1550 -6.7117  -2.8091  \n",
       "1999  -8.1577   0.7146   7.2233  2.8861   6.7138  \n",
       "\n",
       "[2000 rows x 500 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e6184f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m477a\\AppData\\Local\\Temp/ipykernel_8784/2005656394.py:1: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  score_test = np.matmul(Kt_c, eigvct.iloc[:,:nComponent])\n"
     ]
    }
   ],
   "source": [
    "score_test = np.matmul(Kt_c, eigvct.iloc[:,:nComponent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1ecab55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_test = round(score_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ed40b816",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-0.0558</td>\n",
       "      <td>0.3423</td>\n",
       "      <td>-0.2334</td>\n",
       "      <td>-1.3331</td>\n",
       "      <td>1.0851</td>\n",
       "      <td>1.0401</td>\n",
       "      <td>-0.6927</td>\n",
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       "      <td>0.1392</td>\n",
       "      <td>-0.0404</td>\n",
       "      <td>0.1108</td>\n",
       "      <td>0.8667</td>\n",
       "      <td>-0.4963</td>\n",
       "      <td>-0.1270</td>\n",
       "      <td>-0.7957</td>\n",
       "      <td>0.0818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1476</td>\n",
       "      <td>0.4219</td>\n",
       "      <td>-1.1503</td>\n",
       "      <td>0.5305</td>\n",
       "      <td>1.4935</td>\n",
       "      <td>3.1926</td>\n",
       "      <td>-2.2010</td>\n",
       "      <td>0.1365</td>\n",
       "      <td>0.1870</td>\n",
       "      <td>-1.1792</td>\n",
       "      <td>-0.0095</td>\n",
       "      <td>-0.6837</td>\n",
       "      <td>0.8211</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>1.6294</td>\n",
       "      <td>0.3363</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4759</td>\n",
       "      <td>0.2975</td>\n",
       "      <td>0.9828</td>\n",
       "      <td>2.9782</td>\n",
       "      <td>-0.1629</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>-1.4978</td>\n",
       "      <td>1.1079</td>\n",
       "      <td>1.9045</td>\n",
       "      <td>0.6714</td>\n",
       "      <td>1.0616</td>\n",
       "      <td>0.6819</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>0.1540</td>\n",
       "      <td>-0.3219</td>\n",
       "      <td>0.8987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2890</td>\n",
       "      <td>-0.2047</td>\n",
       "      <td>-0.4093</td>\n",
       "      <td>-2.0298</td>\n",
       "      <td>-2.0621</td>\n",
       "      <td>-1.6758</td>\n",
       "      <td>1.8188</td>\n",
       "      <td>-0.6760</td>\n",
       "      <td>0.0412</td>\n",
       "      <td>0.4538</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.6462</td>\n",
       "      <td>0.3143</td>\n",
       "      <td>-0.2537</td>\n",
       "      <td>0.5617</td>\n",
       "      <td>0.6589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.4394</td>\n",
       "      <td>-0.2594</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>0.4224</td>\n",
       "      <td>-1.7907</td>\n",
       "      <td>-0.3318</td>\n",
       "      <td>1.1547</td>\n",
       "      <td>0.3214</td>\n",
       "      <td>-0.8992</td>\n",
       "      <td>-0.7692</td>\n",
       "      <td>-1.1924</td>\n",
       "      <td>-1.0843</td>\n",
       "      <td>-0.4694</td>\n",
       "      <td>0.1342</td>\n",
       "      <td>0.0778</td>\n",
       "      <td>1.6498</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>2.1325</td>\n",
       "      <td>-0.3124</td>\n",
       "      <td>-1.0226</td>\n",
       "      <td>0.4381</td>\n",
       "      <td>-0.6998</td>\n",
       "      <td>0.3524</td>\n",
       "      <td>1.0145</td>\n",
       "      <td>0.3822</td>\n",
       "      <td>0.4612</td>\n",
       "      <td>0.5200</td>\n",
       "      <td>0.2191</td>\n",
       "      <td>-0.2337</td>\n",
       "      <td>1.7274</td>\n",
       "      <td>-0.0635</td>\n",
       "      <td>0.0510</td>\n",
       "      <td>0.9692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>2.1482</td>\n",
       "      <td>-0.9896</td>\n",
       "      <td>2.1694</td>\n",
       "      <td>-2.8496</td>\n",
       "      <td>0.8605</td>\n",
       "      <td>1.3695</td>\n",
       "      <td>-0.0074</td>\n",
       "      <td>-1.3315</td>\n",
       "      <td>0.6532</td>\n",
       "      <td>-0.4522</td>\n",
       "      <td>-0.0090</td>\n",
       "      <td>0.5496</td>\n",
       "      <td>1.9213</td>\n",
       "      <td>-1.3130</td>\n",
       "      <td>-1.3136</td>\n",
       "      <td>0.6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.9294</td>\n",
       "      <td>-0.4837</td>\n",
       "      <td>1.0387</td>\n",
       "      <td>1.3951</td>\n",
       "      <td>-0.7666</td>\n",
       "      <td>1.2775</td>\n",
       "      <td>-2.0384</td>\n",
       "      <td>-0.6272</td>\n",
       "      <td>-0.5780</td>\n",
       "      <td>-1.1828</td>\n",
       "      <td>0.8471</td>\n",
       "      <td>-1.5988</td>\n",
       "      <td>2.6805</td>\n",
       "      <td>-2.1648</td>\n",
       "      <td>-0.0809</td>\n",
       "      <td>1.0635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1.4774</td>\n",
       "      <td>-0.5420</td>\n",
       "      <td>0.6274</td>\n",
       "      <td>-1.9921</td>\n",
       "      <td>-2.0828</td>\n",
       "      <td>1.0988</td>\n",
       "      <td>-1.7914</td>\n",
       "      <td>1.1497</td>\n",
       "      <td>2.4063</td>\n",
       "      <td>0.3736</td>\n",
       "      <td>1.7214</td>\n",
       "      <td>-1.5012</td>\n",
       "      <td>0.3912</td>\n",
       "      <td>0.4729</td>\n",
       "      <td>-0.2371</td>\n",
       "      <td>-1.4417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1.8809</td>\n",
       "      <td>-0.1004</td>\n",
       "      <td>0.3698</td>\n",
       "      <td>0.9202</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>-0.5830</td>\n",
       "      <td>-0.4520</td>\n",
       "      <td>0.3011</td>\n",
       "      <td>0.2663</td>\n",
       "      <td>1.1908</td>\n",
       "      <td>1.1079</td>\n",
       "      <td>-1.6441</td>\n",
       "      <td>2.0658</td>\n",
       "      <td>1.2106</td>\n",
       "      <td>1.1057</td>\n",
       "      <td>0.0126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 16 columns</p>\n",
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       "          0       1       2       3       4       5       6       7       8   \\\n",
       "0    -0.0558  0.3423 -0.2334 -1.3331  1.0851  1.0401 -0.6927  0.5565  0.1392   \n",
       "1     0.1476  0.4219 -1.1503  0.5305  1.4935  3.1926 -2.2010  0.1365  0.1870   \n",
       "2     0.4759  0.2975  0.9828  2.9782 -0.1629  0.0457 -1.4978  1.1079  1.9045   \n",
       "3     0.2890 -0.2047 -0.4093 -2.0298 -2.0621 -1.6758  1.8188 -0.6760  0.0412   \n",
       "4    -0.4394 -0.2594  0.9960  0.4224 -1.7907 -0.3318  1.1547  0.3214 -0.8992   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "1995  2.1325 -0.3124 -1.0226  0.4381 -0.6998  0.3524  1.0145  0.3822  0.4612   \n",
       "1996  2.1482 -0.9896  2.1694 -2.8496  0.8605  1.3695 -0.0074 -1.3315  0.6532   \n",
       "1997  1.9294 -0.4837  1.0387  1.3951 -0.7666  1.2775 -2.0384 -0.6272 -0.5780   \n",
       "1998  1.4774 -0.5420  0.6274 -1.9921 -2.0828  1.0988 -1.7914  1.1497  2.4063   \n",
       "1999  1.8809 -0.1004  0.3698  0.9202  1.0400 -0.5830 -0.4520  0.3011  0.2663   \n",
       "\n",
       "          9       10      11      12      13      14      15  \n",
       "0    -0.0404  0.1108  0.8667 -0.4963 -0.1270 -0.7957  0.0818  \n",
       "1    -1.1792 -0.0095 -0.6837  0.8211  0.4601  1.6294  0.3363  \n",
       "2     0.6714  1.0616  0.6819  0.0426  0.1540 -0.3219  0.8987  \n",
       "3     0.4538  0.1230  0.6462  0.3143 -0.2537  0.5617  0.6589  \n",
       "4    -0.7692 -1.1924 -1.0843 -0.4694  0.1342  0.0778  1.6498  \n",
       "...      ...     ...     ...     ...     ...     ...     ...  \n",
       "1995  0.5200  0.2191 -0.2337  1.7274 -0.0635  0.0510  0.9692  \n",
       "1996 -0.4522 -0.0090  0.5496  1.9213 -1.3130 -1.3136  0.6679  \n",
       "1997 -1.1828  0.8471 -1.5988  2.6805 -2.1648 -0.0809  1.0635  \n",
       "1998  0.3736  1.7214 -1.5012  0.3912  0.4729 -0.2371 -1.4417  \n",
       "1999  1.1908  1.1079 -1.6441  2.0658  1.2106  1.1057  0.0126  \n",
       "\n",
       "[2000 rows x 16 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2af4ca18",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = score_test@invegvldiag@score_test.T\n",
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e7161a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = round(T2_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dd11361f",
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>12.8402</td>\n",
       "      <td>10.1193</td>\n",
       "      <td>-5.0391</td>\n",
       "      <td>-5.0775</td>\n",
       "      <td>-9.3693</td>\n",
       "      <td>4.5410</td>\n",
       "      <td>7.1161</td>\n",
       "      <td>8.1569</td>\n",
       "      <td>-0.5124</td>\n",
       "      <td>-8.3190</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2806</td>\n",
       "      <td>2.6057</td>\n",
       "      <td>7.3124</td>\n",
       "      <td>-8.1138</td>\n",
       "      <td>7.7715</td>\n",
       "      <td>-5.0461</td>\n",
       "      <td>9.1937</td>\n",
       "      <td>-6.3415</td>\n",
       "      <td>3.8693</td>\n",
       "      <td>-6.0234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1193</td>\n",
       "      <td>46.3705</td>\n",
       "      <td>6.4078</td>\n",
       "      <td>-25.7895</td>\n",
       "      <td>-13.9740</td>\n",
       "      <td>-1.6056</td>\n",
       "      <td>10.6460</td>\n",
       "      <td>25.8551</td>\n",
       "      <td>4.0533</td>\n",
       "      <td>-3.9092</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2452</td>\n",
       "      <td>1.0901</td>\n",
       "      <td>6.6831</td>\n",
       "      <td>-17.8704</td>\n",
       "      <td>23.5832</td>\n",
       "      <td>3.0722</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>18.3356</td>\n",
       "      <td>5.2184</td>\n",
       "      <td>7.5378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-5.0391</td>\n",
       "      <td>6.4078</td>\n",
       "      <td>40.6541</td>\n",
       "      <td>-18.3209</td>\n",
       "      <td>-2.7787</td>\n",
       "      <td>-9.6479</td>\n",
       "      <td>4.3279</td>\n",
       "      <td>3.6574</td>\n",
       "      <td>-4.7712</td>\n",
       "      <td>1.2466</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8545</td>\n",
       "      <td>14.9218</td>\n",
       "      <td>8.3667</td>\n",
       "      <td>4.9618</td>\n",
       "      <td>11.5734</td>\n",
       "      <td>8.0906</td>\n",
       "      <td>-6.4597</td>\n",
       "      <td>21.3794</td>\n",
       "      <td>8.0945</td>\n",
       "      <td>17.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.0775</td>\n",
       "      <td>-25.7895</td>\n",
       "      <td>-18.3209</td>\n",
       "      <td>33.9993</td>\n",
       "      <td>8.0367</td>\n",
       "      <td>4.1085</td>\n",
       "      <td>-4.8525</td>\n",
       "      <td>-10.1688</td>\n",
       "      <td>10.9798</td>\n",
       "      <td>9.5207</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2620</td>\n",
       "      <td>-5.4715</td>\n",
       "      <td>-0.3729</td>\n",
       "      <td>22.9495</td>\n",
       "      <td>-4.1719</td>\n",
       "      <td>10.6713</td>\n",
       "      <td>10.2627</td>\n",
       "      <td>-9.9579</td>\n",
       "      <td>8.2378</td>\n",
       "      <td>-4.2466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-9.3693</td>\n",
       "      <td>-13.9740</td>\n",
       "      <td>-2.7787</td>\n",
       "      <td>8.0367</td>\n",
       "      <td>23.4050</td>\n",
       "      <td>11.7849</td>\n",
       "      <td>-9.6164</td>\n",
       "      <td>-11.7345</td>\n",
       "      <td>-10.0592</td>\n",
       "      <td>2.4507</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.5557</td>\n",
       "      <td>-8.3029</td>\n",
       "      <td>-13.3848</td>\n",
       "      <td>3.0856</td>\n",
       "      <td>-23.3775</td>\n",
       "      <td>-4.7577</td>\n",
       "      <td>-8.3683</td>\n",
       "      <td>-1.1265</td>\n",
       "      <td>-7.5893</td>\n",
       "      <td>-10.4216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-5.0461</td>\n",
       "      <td>3.0722</td>\n",
       "      <td>8.0906</td>\n",
       "      <td>10.6713</td>\n",
       "      <td>-4.7577</td>\n",
       "      <td>1.4104</td>\n",
       "      <td>12.8485</td>\n",
       "      <td>14.0129</td>\n",
       "      <td>25.7329</td>\n",
       "      <td>13.7604</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0028</td>\n",
       "      <td>34.6271</td>\n",
       "      <td>35.7858</td>\n",
       "      <td>43.0383</td>\n",
       "      <td>55.1588</td>\n",
       "      <td>46.6471</td>\n",
       "      <td>29.7312</td>\n",
       "      <td>33.5092</td>\n",
       "      <td>23.5686</td>\n",
       "      <td>33.2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>9.1937</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>-6.4597</td>\n",
       "      <td>10.2627</td>\n",
       "      <td>-8.3683</td>\n",
       "      <td>3.3882</td>\n",
       "      <td>22.0322</td>\n",
       "      <td>9.7028</td>\n",
       "      <td>0.8550</td>\n",
       "      <td>-3.3255</td>\n",
       "      <td>...</td>\n",
       "      <td>52.7627</td>\n",
       "      <td>37.4895</td>\n",
       "      <td>52.1095</td>\n",
       "      <td>37.3711</td>\n",
       "      <td>52.2590</td>\n",
       "      <td>29.7312</td>\n",
       "      <td>89.4756</td>\n",
       "      <td>43.5698</td>\n",
       "      <td>40.3049</td>\n",
       "      <td>26.1670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-6.3415</td>\n",
       "      <td>18.3356</td>\n",
       "      <td>21.3794</td>\n",
       "      <td>-9.9579</td>\n",
       "      <td>-1.1265</td>\n",
       "      <td>2.0610</td>\n",
       "      <td>11.6927</td>\n",
       "      <td>13.6199</td>\n",
       "      <td>2.8137</td>\n",
       "      <td>0.4887</td>\n",
       "      <td>...</td>\n",
       "      <td>19.5629</td>\n",
       "      <td>32.7099</td>\n",
       "      <td>30.9920</td>\n",
       "      <td>34.2446</td>\n",
       "      <td>48.3321</td>\n",
       "      <td>33.5092</td>\n",
       "      <td>43.5698</td>\n",
       "      <td>71.4138</td>\n",
       "      <td>30.6311</td>\n",
       "      <td>35.4463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>3.8693</td>\n",
       "      <td>5.2184</td>\n",
       "      <td>8.0945</td>\n",
       "      <td>8.2378</td>\n",
       "      <td>-7.5893</td>\n",
       "      <td>4.8845</td>\n",
       "      <td>28.0146</td>\n",
       "      <td>14.3625</td>\n",
       "      <td>11.4112</td>\n",
       "      <td>7.8377</td>\n",
       "      <td>...</td>\n",
       "      <td>30.1382</td>\n",
       "      <td>21.6362</td>\n",
       "      <td>41.7302</td>\n",
       "      <td>51.1825</td>\n",
       "      <td>30.2903</td>\n",
       "      <td>23.5686</td>\n",
       "      <td>40.3049</td>\n",
       "      <td>30.6311</td>\n",
       "      <td>66.1036</td>\n",
       "      <td>22.3389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-6.0234</td>\n",
       "      <td>7.5378</td>\n",
       "      <td>17.1600</td>\n",
       "      <td>-4.2466</td>\n",
       "      <td>-10.4216</td>\n",
       "      <td>-15.4163</td>\n",
       "      <td>6.4650</td>\n",
       "      <td>9.0539</td>\n",
       "      <td>10.5271</td>\n",
       "      <td>4.1466</td>\n",
       "      <td>...</td>\n",
       "      <td>24.2985</td>\n",
       "      <td>35.2949</td>\n",
       "      <td>36.4196</td>\n",
       "      <td>28.3926</td>\n",
       "      <td>49.9525</td>\n",
       "      <td>33.2884</td>\n",
       "      <td>26.1670</td>\n",
       "      <td>35.4463</td>\n",
       "      <td>22.3389</td>\n",
       "      <td>47.5402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 2000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        1        2        3        4        5        6        7     \\\n",
       "0     12.8402  10.1193  -5.0391  -5.0775  -9.3693   4.5410   7.1161   8.1569   \n",
       "1     10.1193  46.3705   6.4078 -25.7895 -13.9740  -1.6056  10.6460  25.8551   \n",
       "2     -5.0391   6.4078  40.6541 -18.3209  -2.7787  -9.6479   4.3279   3.6574   \n",
       "3     -5.0775 -25.7895 -18.3209  33.9993   8.0367   4.1085  -4.8525 -10.1688   \n",
       "4     -9.3693 -13.9740  -2.7787   8.0367  23.4050  11.7849  -9.6164 -11.7345   \n",
       "...       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "1995  -5.0461   3.0722   8.0906  10.6713  -4.7577   1.4104  12.8485  14.0129   \n",
       "1996   9.1937   0.1061  -6.4597  10.2627  -8.3683   3.3882  22.0322   9.7028   \n",
       "1997  -6.3415  18.3356  21.3794  -9.9579  -1.1265   2.0610  11.6927  13.6199   \n",
       "1998   3.8693   5.2184   8.0945   8.2378  -7.5893   4.8845  28.0146  14.3625   \n",
       "1999  -6.0234   7.5378  17.1600  -4.2466 -10.4216 -15.4163   6.4650   9.0539   \n",
       "\n",
       "         8        9     ...     1990     1991     1992     1993     1994  \\\n",
       "0     -0.5124  -8.3190  ...   8.2806   2.6057   7.3124  -8.1138   7.7715   \n",
       "1      4.0533  -3.9092  ...   6.2452   1.0901   6.6831 -17.8704  23.5832   \n",
       "2     -4.7712   1.2466  ...   2.8545  14.9218   8.3667   4.9618  11.5734   \n",
       "3     10.9798   9.5207  ...   6.2620  -5.4715  -0.3729  22.9495  -4.1719   \n",
       "4    -10.0592   2.4507  ...  -9.5557  -8.3029 -13.3848   3.0856 -23.3775   \n",
       "...       ...      ...  ...      ...      ...      ...      ...      ...   \n",
       "1995  25.7329  13.7604  ...  32.0028  34.6271  35.7858  43.0383  55.1588   \n",
       "1996   0.8550  -3.3255  ...  52.7627  37.4895  52.1095  37.3711  52.2590   \n",
       "1997   2.8137   0.4887  ...  19.5629  32.7099  30.9920  34.2446  48.3321   \n",
       "1998  11.4112   7.8377  ...  30.1382  21.6362  41.7302  51.1825  30.2903   \n",
       "1999  10.5271   4.1466  ...  24.2985  35.2949  36.4196  28.3926  49.9525   \n",
       "\n",
       "         1995     1996     1997     1998     1999  \n",
       "0     -5.0461   9.1937  -6.3415   3.8693  -6.0234  \n",
       "1      3.0722   0.1061  18.3356   5.2184   7.5378  \n",
       "2      8.0906  -6.4597  21.3794   8.0945  17.1600  \n",
       "3     10.6713  10.2627  -9.9579   8.2378  -4.2466  \n",
       "4     -4.7577  -8.3683  -1.1265  -7.5893 -10.4216  \n",
       "...       ...      ...      ...      ...      ...  \n",
       "1995  46.6471  29.7312  33.5092  23.5686  33.2884  \n",
       "1996  29.7312  89.4756  43.5698  40.3049  26.1670  \n",
       "1997  33.5092  43.5698  71.4138  30.6311  35.4463  \n",
       "1998  23.5686  40.3049  30.6311  66.1036  22.3389  \n",
       "1999  33.2884  26.1670  35.4463  22.3389  47.5402  \n",
       "\n",
       "[2000 rows x 2000 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1f13e237",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1f1dab63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.8402, 46.3705, 40.6541, ..., 71.4138, 66.1036, 47.5402])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e3e320ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000,)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "aade7a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.semilogy(T2_test_final,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a6c7681c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(T2_test_final)\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "dc1ad8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.9495\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_test_final>T2_trlim).sum()/T2_test_final.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5c1a20",
   "metadata": {},
   "source": [
    "## Testing Process using Faulty Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b534f3c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## STEPs\n",
    "# 1-standardize the data\n",
    "# 2-linear kernel\n",
    "# 3-Centralize the kernel\n",
    "# 4-Score faulty\n",
    "# 5-T2 faulty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81905c3b",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ebc9f01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "IDV = 1\n",
    "df_test = test_faulty[(test_faulty.faultNumber==IDV) & \n",
    "                               (test_faulty.simulationRun==1)].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "de4d9426",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "X_ts = scl_pick('std', df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8d45e27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.032077</td>\n",
       "      <td>0.274369</td>\n",
       "      <td>-0.283462</td>\n",
       "      <td>2.131759</td>\n",
       "      <td>0.748241</td>\n",
       "      <td>0.700908</td>\n",
       "      <td>-0.043226</td>\n",
       "      <td>-0.291872</td>\n",
       "      <td>0.518418</td>\n",
       "      <td>0.665952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964565</td>\n",
       "      <td>-2.043364</td>\n",
       "      <td>0.649894</td>\n",
       "      <td>0.182801</td>\n",
       "      <td>0.666129</td>\n",
       "      <td>0.610804</td>\n",
       "      <td>0.624495</td>\n",
       "      <td>-1.014017</td>\n",
       "      <td>1.495361</td>\n",
       "      <td>-1.732594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.029050</td>\n",
       "      <td>-0.573262</td>\n",
       "      <td>1.206104</td>\n",
       "      <td>1.795302</td>\n",
       "      <td>0.478652</td>\n",
       "      <td>1.171492</td>\n",
       "      <td>-0.057380</td>\n",
       "      <td>0.078948</td>\n",
       "      <td>-1.017636</td>\n",
       "      <td>0.737980</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.531646</td>\n",
       "      <td>-2.046391</td>\n",
       "      <td>0.988141</td>\n",
       "      <td>0.219955</td>\n",
       "      <td>0.596054</td>\n",
       "      <td>-0.514721</td>\n",
       "      <td>-0.615276</td>\n",
       "      <td>-0.997290</td>\n",
       "      <td>-0.667563</td>\n",
       "      <td>-1.274026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.047981</td>\n",
       "      <td>-0.548001</td>\n",
       "      <td>0.315856</td>\n",
       "      <td>1.366896</td>\n",
       "      <td>0.143989</td>\n",
       "      <td>-0.111161</td>\n",
       "      <td>-0.117536</td>\n",
       "      <td>-0.498980</td>\n",
       "      <td>-1.017636</td>\n",
       "      <td>0.693132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361195</td>\n",
       "      <td>-2.027102</td>\n",
       "      <td>1.066886</td>\n",
       "      <td>0.077973</td>\n",
       "      <td>0.638898</td>\n",
       "      <td>1.200694</td>\n",
       "      <td>-0.821408</td>\n",
       "      <td>-0.999194</td>\n",
       "      <td>0.434172</td>\n",
       "      <td>1.529115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.032942</td>\n",
       "      <td>-0.963397</td>\n",
       "      <td>0.487505</td>\n",
       "      <td>1.520488</td>\n",
       "      <td>0.478652</td>\n",
       "      <td>0.105391</td>\n",
       "      <td>-0.103382</td>\n",
       "      <td>-0.182401</td>\n",
       "      <td>-1.017636</td>\n",
       "      <td>0.668670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190200</td>\n",
       "      <td>-2.042094</td>\n",
       "      <td>1.633316</td>\n",
       "      <td>-0.066664</td>\n",
       "      <td>0.663224</td>\n",
       "      <td>0.608173</td>\n",
       "      <td>0.664022</td>\n",
       "      <td>-1.002424</td>\n",
       "      <td>-0.350185</td>\n",
       "      <td>-0.597031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.195823</td>\n",
       "      <td>-0.191547</td>\n",
       "      <td>1.239561</td>\n",
       "      <td>1.430950</td>\n",
       "      <td>0.023139</td>\n",
       "      <td>0.405232</td>\n",
       "      <td>0.031084</td>\n",
       "      <td>-0.656776</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>-0.026248</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.485233</td>\n",
       "      <td>-2.177751</td>\n",
       "      <td>1.625710</td>\n",
       "      <td>0.219292</td>\n",
       "      <td>-0.224509</td>\n",
       "      <td>0.025846</td>\n",
       "      <td>-0.768281</td>\n",
       "      <td>-0.994971</td>\n",
       "      <td>0.081841</td>\n",
       "      <td>0.398023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>0.420413</td>\n",
       "      <td>-0.368371</td>\n",
       "      <td>-0.091448</td>\n",
       "      <td>-0.227229</td>\n",
       "      <td>-1.547917</td>\n",
       "      <td>0.446877</td>\n",
       "      <td>-0.138768</td>\n",
       "      <td>-0.853035</td>\n",
       "      <td>-0.505618</td>\n",
       "      <td>-0.517764</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783799</td>\n",
       "      <td>0.415853</td>\n",
       "      <td>-0.042707</td>\n",
       "      <td>-0.147607</td>\n",
       "      <td>-0.591221</td>\n",
       "      <td>-0.228326</td>\n",
       "      <td>0.884180</td>\n",
       "      <td>-0.761368</td>\n",
       "      <td>-1.359642</td>\n",
       "      <td>0.468916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0.369002</td>\n",
       "      <td>0.007730</td>\n",
       "      <td>-0.295099</td>\n",
       "      <td>-0.777890</td>\n",
       "      <td>1.431511</td>\n",
       "      <td>-1.418800</td>\n",
       "      <td>-0.160000</td>\n",
       "      <td>0.702243</td>\n",
       "      <td>0.518418</td>\n",
       "      <td>-1.026494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.328217</td>\n",
       "      <td>0.370634</td>\n",
       "      <td>0.053935</td>\n",
       "      <td>-0.173482</td>\n",
       "      <td>-0.701235</td>\n",
       "      <td>-0.974401</td>\n",
       "      <td>0.322309</td>\n",
       "      <td>-0.763355</td>\n",
       "      <td>0.674652</td>\n",
       "      <td>0.265818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>0.365206</td>\n",
       "      <td>-0.758506</td>\n",
       "      <td>0.102021</td>\n",
       "      <td>0.415724</td>\n",
       "      <td>-0.014046</td>\n",
       "      <td>0.505179</td>\n",
       "      <td>-0.206001</td>\n",
       "      <td>-0.555195</td>\n",
       "      <td>-1.017636</td>\n",
       "      <td>-0.903275</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069958</td>\n",
       "      <td>0.374442</td>\n",
       "      <td>-1.649381</td>\n",
       "      <td>-0.188079</td>\n",
       "      <td>-0.822141</td>\n",
       "      <td>-0.492033</td>\n",
       "      <td>-0.282913</td>\n",
       "      <td>-0.758055</td>\n",
       "      <td>-0.740266</td>\n",
       "      <td>1.810132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>0.405422</td>\n",
       "      <td>-0.222421</td>\n",
       "      <td>-0.175818</td>\n",
       "      <td>-0.242726</td>\n",
       "      <td>0.399634</td>\n",
       "      <td>0.197009</td>\n",
       "      <td>-0.276773</td>\n",
       "      <td>-0.541387</td>\n",
       "      <td>-1.529654</td>\n",
       "      <td>-0.037573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.477905</td>\n",
       "      <td>0.413753</td>\n",
       "      <td>-1.162144</td>\n",
       "      <td>-0.351292</td>\n",
       "      <td>-0.187112</td>\n",
       "      <td>0.118243</td>\n",
       "      <td>-0.102282</td>\n",
       "      <td>-0.787784</td>\n",
       "      <td>-1.105180</td>\n",
       "      <td>1.183593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>0.403404</td>\n",
       "      <td>-1.221615</td>\n",
       "      <td>-0.162726</td>\n",
       "      <td>-0.170063</td>\n",
       "      <td>-0.134896</td>\n",
       "      <td>-1.323018</td>\n",
       "      <td>-0.202463</td>\n",
       "      <td>0.468507</td>\n",
       "      <td>1.542455</td>\n",
       "      <td>-0.093746</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.491340</td>\n",
       "      <td>0.415755</td>\n",
       "      <td>0.153709</td>\n",
       "      <td>-0.202011</td>\n",
       "      <td>-0.132650</td>\n",
       "      <td>0.204063</td>\n",
       "      <td>0.784301</td>\n",
       "      <td>-0.767910</td>\n",
       "      <td>-0.016029</td>\n",
       "      <td>-0.277056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -2.032077  0.274369 -0.283462  2.131759  0.748241  0.700908 -0.043226   \n",
       "1   -2.029050 -0.573262  1.206104  1.795302  0.478652  1.171492 -0.057380   \n",
       "2   -2.047981 -0.548001  0.315856  1.366896  0.143989 -0.111161 -0.117536   \n",
       "3   -2.032942 -0.963397  0.487505  1.520488  0.478652  0.105391 -0.103382   \n",
       "4   -2.195823 -0.191547  1.239561  1.430950  0.023139  0.405232  0.031084   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "955  0.420413 -0.368371 -0.091448 -0.227229 -1.547917  0.446877 -0.138768   \n",
       "956  0.369002  0.007730 -0.295099 -0.777890  1.431511 -1.418800 -0.160000   \n",
       "957  0.365206 -0.758506  0.102021  0.415724 -0.014046  0.505179 -0.206001   \n",
       "958  0.405422 -0.222421 -0.175818 -0.242726  0.399634  0.197009 -0.276773   \n",
       "959  0.403404 -1.221615 -0.162726 -0.170063 -0.134896 -1.323018 -0.202463   \n",
       "\n",
       "           7         8         9   ...        42        43        44  \\\n",
       "0   -0.291872  0.518418  0.665952  ...  0.964565 -2.043364  0.649894   \n",
       "1    0.078948 -1.017636  0.737980  ... -0.531646 -2.046391  0.988141   \n",
       "2   -0.498980 -1.017636  0.693132  ...  0.361195 -2.027102  1.066886   \n",
       "3   -0.182401 -1.017636  0.668670  ...  0.190200 -2.042094  1.633316   \n",
       "4   -0.656776  0.006400 -0.026248  ... -0.485233 -2.177751  1.625710   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "955 -0.853035 -0.505618 -0.517764  ...  0.783799  0.415853 -0.042707   \n",
       "956  0.702243  0.518418 -1.026494  ...  0.328217  0.370634  0.053935   \n",
       "957 -0.555195 -1.017636 -0.903275  ... -0.069958  0.374442 -1.649381   \n",
       "958 -0.541387 -1.529654 -0.037573  ... -0.477905  0.413753 -1.162144   \n",
       "959  0.468507  1.542455 -0.093746  ... -0.491340  0.415755  0.153709   \n",
       "\n",
       "           45        46        47        48        49        50        51  \n",
       "0    0.182801  0.666129  0.610804  0.624495 -1.014017  1.495361 -1.732594  \n",
       "1    0.219955  0.596054 -0.514721 -0.615276 -0.997290 -0.667563 -1.274026  \n",
       "2    0.077973  0.638898  1.200694 -0.821408 -0.999194  0.434172  1.529115  \n",
       "3   -0.066664  0.663224  0.608173  0.664022 -1.002424 -0.350185 -0.597031  \n",
       "4    0.219292 -0.224509  0.025846 -0.768281 -0.994971  0.081841  0.398023  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "955 -0.147607 -0.591221 -0.228326  0.884180 -0.761368 -1.359642  0.468916  \n",
       "956 -0.173482 -0.701235 -0.974401  0.322309 -0.763355  0.674652  0.265818  \n",
       "957 -0.188079 -0.822141 -0.492033 -0.282913 -0.758055 -0.740266  1.810132  \n",
       "958 -0.351292 -0.187112  0.118243 -0.102282 -0.787784 -1.105180  1.183593  \n",
       "959 -0.202011 -0.132650  0.204063  0.784301 -0.767910 -0.016029 -0.277056  \n",
       "\n",
       "[960 rows x 52 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "57baa540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BfBFYW1V3tUV3A2vb9DrgYM9qh1rb5G1tT7I3yd6xsbG51i1JmkHf4Z7kqcBHgDdU1fd6l1VVATWXHVfVVVW1uao2r1mzZi6rSpJm0Ve4J3k848F+TVV9tDXfMzHc0n7e29oPAxt6Vl/f2iRJS6Sfq2UCXA3sr6p39iy6AdjWprcB1/e0v6pdNXMW8GDP8I0kaQms6qPPC4FXAl9Nsq+1vRn4Q+DaJJcAdwIXtWWfAs4DDgAPA68ZZMGSpNnNGu5V9QUg0yzeMkX/Ai5dYF2SpAXwG6rSCuKfCVS/DHctmPdTkZYfw12SOshwl1Yg3y1pNoa7JHWQ4S5JHWS4S/Pk0IiWM8NdkjrIcJekDjLcJamDDHdJ6iDDXQN3aMfnp1+48+ilK0QaYYa7hs/AlwbOcNdAXfG6PQta38sL526hz7m6yXDXonvHS88fdgmdN+NQmEaS4S5JHWS4r1Abd3xy2CU4HLCS+TlH5xnuGqoTbto37BJGjs/5aDDcpZXCs23NgeEudV17UZjug+2dO3cuYTFaKoa7tMJ49ZH6YbhLI8A/rD16DHctqolQ8a2/tLRmDfckf5rk3iR/09N2bJIbk3yj/TymtSfJ5UkOJLktyXMXs3h1h5dVSoPVz5n7+4BzJrXtAHZX1SnA7jYPcC5wSntsB64cTJnqktmGCJbDNfzSSjdruFfV54D7JzVvBXa16V3ABT3t769xNwOrk5w4oFolSX2a75j72qq6q03fDaxt0+uAgz39DrW2n5Bke5K9SfaOjY3NswxJ0lQW/IFqVRVQ81jvqqraXFWb16xZs9AyJDWbdm0adglaBuYb7vdMDLe0n/e29sPAhp5+61ubpCHwVgOja77hfgOwrU1vA67vaX9Vu2rmLODBnuEbaUbetlYanFWzdUjyQeDFwPFJDgG/D/whcG2SS4A7gYta908B5wEHgIeB1yxCzZKkWcwa7lX1smkWbZmibwGXLrQorXy795zMlrO/+Zi2E27ax91DqkcaNX5DVZI6yHCXpA4y3KURMvkeP95hsrsMd0nqIMNdQ7GczhhX6u1wp7oHj/fl0QTDXfPityCl5c1wlzpqti+FrdR3LOqP4S5JHWS4S1IHGe5akN63/j8xDr/z6CWuRtIEw13qiN17Th52CVpGDHdJ3hq4gwx3zdmgrqWe/G1JSYNjuEta0RYyHNXldyyG+wjxTHl0XPG6PcMuQUNmuHdYP2clBr7maiG/M10+U15uDPcRNtP9XbxHiWYz+dLX2X5nfvyi4CWyS8JwX8Gmu7/L5LfkvV8zn+7MqXdb8xnD9MVgOJbLPX7mO+7tO8fFY7ivcBNBvnvPyTMG7OSzdO8rovmY6t3eQv+w+Qk37evE2fxy+5zDcB+ixQjYiTO5Gf/DTfqPNHE2v9x+ObWM7Dx6yt/X6X5nDu34/Eh/qWo5vJM13BdoruOOi2EuQb6gbUkdsZz+nsBi6WS4L4dXzceYJWAnjzsasBoFCwnYfj5rWHY5sMQ6Ge5TGtKY3myXfvX+gi+XD8ekQVl272RHyKKEe5JzkvxtkgNJdizGPlYyr/VVV23atWlRP7uZ7uqaiReRyfse5ROmgYd7kiOAK4BzgdOAlyU5bdD7WWkmPozy0i91wVzPyIc9xj2K/+8W48z9TOBAVd1RVT8EPgRsXYT9/NggX53ns63eyxEnt/Xq5xe830sbpZViwVeFtSHVgbxA9AzPzvVLVSvtBSJVNdgNJhcC51TVa9v8K4HnV9VvTOq3HdjeZp8F/O1ACxmc44HvDLuIIfL4R/v4wedgOR//M6pqzVQLVi11JROq6irgqmHtv19J9lbV5mHXMSwe/2gfP/gcrNTjX4xhmcPAhp759a1NkrREFiPcvwSckuSkJEcCFwM3LMJ+JEnTGPiwTFU9kuQ3gL8AjgD+tKpuH/R+ltCyHzpaZB6/Rv05WJHHP/APVCVJwzc631CVpBFiuEtSBxnufUryxiSV5Phh17LUkvzXJF9PcluSjyVZPeyalsIo30YjyYYkNyX5WpLbk7x+2DUNQ5Ijktya5BPDrmWuDPc+JNkA/BLw98OuZUhuBH6mqn4W+Dvgd4dcz6LzNho8Aryxqk4DzgIuHbHjn/B6YP+wi5gPw70/lwFvAkby0+eq+suqeqTN3sz4dxe6bslvo7GcVNVdVfXlNv0Q4wG3brhVLa0k64GXAO8ddi3zYbjPIslW4HBVfWXYtSwT/x7482EXsQTWAQd75g8xYuE2IclG4Azgi0MuZam9i/GTukeHXMe8DO32A8tJks8AJ0yx6PeANzM+JNNpMz0HVXV96/N7jL9dv2Ypa9PwJHkq8BHgDVX1vWHXs1SSnA/cW1W3JHnxkMuZF8MdqKpfnKo9ySbgJOArSWB8OOLLSc6sqruXsMRFN91zMCHJq4HzgS01Gl+OGPnbaCR5POPBfk1VfXTY9SyxFwK/nOQ84InA05J8oKpeMeS6+uaXmOYgybeBzVW1XO8QtyiSnAO8E/iFqhobdj1LIckqxj883sJ4qH8J+LUV/m3rvmX8bGYXcH9VvWHI5QxVO3P/7apaUX941TF39eM9wFHAjUn2JfnjYRe02NoHyBO30dgPXDsqwd68EHglcHb7N9/XzmK1QnjmLkkd5Jm7JHWQ4S5JHWS4S1IHGe6S1EGGuyR1kOEuSR1kuEtSB/1/gfzFMnATz+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_ts)\n",
    "plt.title('data test scaling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7fd230f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt = np.matmul(X_ts, df_scl.T)\n",
    "Kt = X_ts@df_scl.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a031e197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\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>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
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       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.453018</td>\n",
       "      <td>4.305422</td>\n",
       "      <td>1.375915</td>\n",
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       "      <td>-1.429239</td>\n",
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       "      <td>2.390238</td>\n",
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       "      <td>-2.279073</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.853687</td>\n",
       "      <td>9.217334</td>\n",
       "      <td>-0.443192</td>\n",
       "      <td>10.609782</td>\n",
       "      <td>-0.232004</td>\n",
       "      <td>3.378235</td>\n",
       "      <td>4.664616</td>\n",
       "      <td>6.553498</td>\n",
       "      <td>-1.658749</td>\n",
       "      <td>6.017663</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.809356</td>\n",
       "      <td>9.517926</td>\n",
       "      <td>-3.132328</td>\n",
       "      <td>6.855571</td>\n",
       "      <td>-5.591185</td>\n",
       "      <td>-11.505501</td>\n",
       "      <td>9.415442</td>\n",
       "      <td>8.813283</td>\n",
       "      <td>-1.620328</td>\n",
       "      <td>-11.746601</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.622425</td>\n",
       "      <td>-3.488638</td>\n",
       "      <td>-1.575327</td>\n",
       "      <td>-1.679155</td>\n",
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       "      <td>7.089664</td>\n",
       "      <td>1.914781</td>\n",
       "      <td>1.597111</td>\n",
       "      <td>5.454128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.895180</td>\n",
       "      <td>2.773035</td>\n",
       "      <td>11.260796</td>\n",
       "      <td>6.505250</td>\n",
       "      <td>-3.060854</td>\n",
       "      <td>-5.039343</td>\n",
       "      <td>3.520801</td>\n",
       "      <td>8.886146</td>\n",
       "      <td>-1.327425</td>\n",
       "      <td>-5.084270</td>\n",
       "      <td>...</td>\n",
       "      <td>1.513334</td>\n",
       "      <td>0.092079</td>\n",
       "      <td>3.692067</td>\n",
       "      <td>-7.823209</td>\n",
       "      <td>2.271910</td>\n",
       "      <td>-7.101379</td>\n",
       "      <td>5.733558</td>\n",
       "      <td>9.260480</td>\n",
       "      <td>2.929893</td>\n",
       "      <td>-1.965449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.513887</td>\n",
       "      <td>1.322032</td>\n",
       "      <td>2.994476</td>\n",
       "      <td>7.537675</td>\n",
       "      <td>-5.091701</td>\n",
       "      <td>-8.438215</td>\n",
       "      <td>4.811420</td>\n",
       "      <td>6.091585</td>\n",
       "      <td>-1.966096</td>\n",
       "      <td>-8.050859</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.617199</td>\n",
       "      <td>0.605441</td>\n",
       "      <td>1.382119</td>\n",
       "      <td>3.628747</td>\n",
       "      <td>-3.203039</td>\n",
       "      <td>-3.116042</td>\n",
       "      <td>2.786581</td>\n",
       "      <td>7.913995</td>\n",
       "      <td>2.557986</td>\n",
       "      <td>3.740084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.230114</td>\n",
       "      <td>5.419748</td>\n",
       "      <td>3.770302</td>\n",
       "      <td>-0.029823</td>\n",
       "      <td>-4.622934</td>\n",
       "      <td>-4.786231</td>\n",
       "      <td>7.258955</td>\n",
       "      <td>6.467339</td>\n",
       "      <td>-6.655872</td>\n",
       "      <td>-0.595796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322269</td>\n",
       "      <td>-1.427121</td>\n",
       "      <td>-10.295897</td>\n",
       "      <td>-12.400699</td>\n",
       "      <td>3.514553</td>\n",
       "      <td>2.689356</td>\n",
       "      <td>5.046735</td>\n",
       "      <td>-0.775737</td>\n",
       "      <td>1.814049</td>\n",
       "      <td>-3.778696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>1.053375</td>\n",
       "      <td>-1.961520</td>\n",
       "      <td>5.365646</td>\n",
       "      <td>1.894508</td>\n",
       "      <td>4.210785</td>\n",
       "      <td>5.179273</td>\n",
       "      <td>1.847667</td>\n",
       "      <td>-1.866150</td>\n",
       "      <td>2.425911</td>\n",
       "      <td>4.740412</td>\n",
       "      <td>...</td>\n",
       "      <td>1.919743</td>\n",
       "      <td>-2.005987</td>\n",
       "      <td>5.448067</td>\n",
       "      <td>4.448564</td>\n",
       "      <td>3.077036</td>\n",
       "      <td>4.103580</td>\n",
       "      <td>-3.906147</td>\n",
       "      <td>-1.012879</td>\n",
       "      <td>3.320914</td>\n",
       "      <td>-0.528981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>-0.289533</td>\n",
       "      <td>-2.742045</td>\n",
       "      <td>4.178728</td>\n",
       "      <td>5.483557</td>\n",
       "      <td>-1.857380</td>\n",
       "      <td>2.289665</td>\n",
       "      <td>0.773763</td>\n",
       "      <td>2.691441</td>\n",
       "      <td>9.466643</td>\n",
       "      <td>7.055389</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.176170</td>\n",
       "      <td>-4.729038</td>\n",
       "      <td>1.656539</td>\n",
       "      <td>-5.107150</td>\n",
       "      <td>5.978998</td>\n",
       "      <td>6.277063</td>\n",
       "      <td>-5.888306</td>\n",
       "      <td>-8.432910</td>\n",
       "      <td>-1.945377</td>\n",
       "      <td>0.982962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>-1.730654</td>\n",
       "      <td>2.818927</td>\n",
       "      <td>6.550946</td>\n",
       "      <td>4.134668</td>\n",
       "      <td>4.109284</td>\n",
       "      <td>7.831122</td>\n",
       "      <td>0.854377</td>\n",
       "      <td>1.768002</td>\n",
       "      <td>8.951192</td>\n",
       "      <td>3.684457</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.414642</td>\n",
       "      <td>-6.306147</td>\n",
       "      <td>6.673397</td>\n",
       "      <td>-9.410418</td>\n",
       "      <td>7.658920</td>\n",
       "      <td>6.627778</td>\n",
       "      <td>-3.564329</td>\n",
       "      <td>-3.840236</td>\n",
       "      <td>3.764344</td>\n",
       "      <td>6.952407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>1.156418</td>\n",
       "      <td>0.406208</td>\n",
       "      <td>9.640826</td>\n",
       "      <td>6.280594</td>\n",
       "      <td>6.807011</td>\n",
       "      <td>10.648425</td>\n",
       "      <td>3.134946</td>\n",
       "      <td>2.502731</td>\n",
       "      <td>7.556841</td>\n",
       "      <td>-2.313546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250914</td>\n",
       "      <td>-5.209773</td>\n",
       "      <td>6.485563</td>\n",
       "      <td>0.853681</td>\n",
       "      <td>3.347469</td>\n",
       "      <td>-1.190644</td>\n",
       "      <td>-2.905322</td>\n",
       "      <td>0.646179</td>\n",
       "      <td>2.613489</td>\n",
       "      <td>1.801885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>2.681924</td>\n",
       "      <td>0.936539</td>\n",
       "      <td>10.759573</td>\n",
       "      <td>11.881501</td>\n",
       "      <td>1.622419</td>\n",
       "      <td>2.102393</td>\n",
       "      <td>5.090559</td>\n",
       "      <td>6.251665</td>\n",
       "      <td>2.888249</td>\n",
       "      <td>6.153608</td>\n",
       "      <td>...</td>\n",
       "      <td>2.146486</td>\n",
       "      <td>2.181927</td>\n",
       "      <td>3.810648</td>\n",
       "      <td>4.578979</td>\n",
       "      <td>4.768791</td>\n",
       "      <td>3.617600</td>\n",
       "      <td>-6.541249</td>\n",
       "      <td>-5.973259</td>\n",
       "      <td>-3.868518</td>\n",
       "      <td>0.366841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1          2          3         4          5         6    \\\n",
       "0    2.453018  4.305422   1.375915   7.214473 -1.429239  -6.107070  2.390238   \n",
       "1   -0.809356  9.517926  -3.132328   6.855571 -5.591185 -11.505501  9.415442   \n",
       "2    4.895180  2.773035  11.260796   6.505250 -3.060854  -5.039343  3.520801   \n",
       "3    3.513887  1.322032   2.994476   7.537675 -5.091701  -8.438215  4.811420   \n",
       "4    2.230114  5.419748   3.770302  -0.029823 -4.622934  -4.786231  7.258955   \n",
       "..        ...       ...        ...        ...       ...        ...       ...   \n",
       "955  1.053375 -1.961520   5.365646   1.894508  4.210785   5.179273  1.847667   \n",
       "956 -0.289533 -2.742045   4.178728   5.483557 -1.857380   2.289665  0.773763   \n",
       "957 -1.730654  2.818927   6.550946   4.134668  4.109284   7.831122  0.854377   \n",
       "958  1.156418  0.406208   9.640826   6.280594  6.807011  10.648425  3.134946   \n",
       "959  2.681924  0.936539  10.759573  11.881501  1.622419   2.102393  5.090559   \n",
       "\n",
       "          7         8          9    ...       490       491        492  \\\n",
       "0    5.136672 -5.386992  -2.279073  ... -1.853687  9.217334  -0.443192   \n",
       "1    8.813283 -1.620328 -11.746601  ... -1.622425 -3.488638  -1.575327   \n",
       "2    8.886146 -1.327425  -5.084270  ...  1.513334  0.092079   3.692067   \n",
       "3    6.091585 -1.966096  -8.050859  ... -0.617199  0.605441   1.382119   \n",
       "4    6.467339 -6.655872  -0.595796  ...  0.322269 -1.427121 -10.295897   \n",
       "..        ...       ...        ...  ...       ...       ...        ...   \n",
       "955 -1.866150  2.425911   4.740412  ...  1.919743 -2.005987   5.448067   \n",
       "956  2.691441  9.466643   7.055389  ... -3.176170 -4.729038   1.656539   \n",
       "957  1.768002  8.951192   3.684457  ... -0.414642 -6.306147   6.673397   \n",
       "958  2.502731  7.556841  -2.313546  ...  0.250914 -5.209773   6.485563   \n",
       "959  6.251665  2.888249   6.153608  ...  2.146486  2.181927   3.810648   \n",
       "\n",
       "           493       494       495       496       497       498       499  \n",
       "0    10.609782 -0.232004  3.378235  4.664616  6.553498 -1.658749  6.017663  \n",
       "1    -1.679155 -5.066641  7.782569  7.089664  1.914781  1.597111  5.454128  \n",
       "2    -7.823209  2.271910 -7.101379  5.733558  9.260480  2.929893 -1.965449  \n",
       "3     3.628747 -3.203039 -3.116042  2.786581  7.913995  2.557986  3.740084  \n",
       "4   -12.400699  3.514553  2.689356  5.046735 -0.775737  1.814049 -3.778696  \n",
       "..         ...       ...       ...       ...       ...       ...       ...  \n",
       "955   4.448564  3.077036  4.103580 -3.906147 -1.012879  3.320914 -0.528981  \n",
       "956  -5.107150  5.978998  6.277063 -5.888306 -8.432910 -1.945377  0.982962  \n",
       "957  -9.410418  7.658920  6.627778 -3.564329 -3.840236  3.764344  6.952407  \n",
       "958   0.853681  3.347469 -1.190644 -2.905322  0.646179  2.613489  1.801885  \n",
       "959   4.578979  4.768791  3.617600 -6.541249 -5.973259 -3.868518  0.366841  \n",
       "\n",
       "[960 rows x 500 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5ca70e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Kt)\n",
    "plt.title('Kt')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b66ba62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centralize the kernel\n",
    "st = (len(X_ts),len(df_train)) # 2000 or 960\n",
    "unit_t = np.ones(st)/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4126fcfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit_t = pd.DataFrame(unit_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d1456e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>491</th>\n",
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       "      <th>493</th>\n",
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       "      <th>497</th>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
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       "    <tr>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      1      2      3      4      5      6      7      8      9    ...  \\\n",
       "0  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  ...   \n",
       "1  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  ...   \n",
       "2  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  ...   \n",
       "3  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  ...   \n",
       "4  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  ...   \n",
       "\n",
       "     490    491    492    493    494    495    496    497    498    499  \n",
       "0  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "1  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "2  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "3  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "4  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d1a7b3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960, 500)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "99f90ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = Kt-unit_t@K-Kt@unit+unit_t@K@unit\n",
    "# the result are NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "772b0de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = round(Kt_c, 4)\n",
    "# Kt_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "75f64427",
   "metadata": {},
   "outputs": [
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       "      <td>2.2719</td>\n",
       "      <td>-7.1014</td>\n",
       "      <td>5.7336</td>\n",
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       "      <td>2.9299</td>\n",
       "      <td>-1.9654</td>\n",
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       "      <td>3.5139</td>\n",
       "      <td>1.3220</td>\n",
       "      <td>2.9945</td>\n",
       "      <td>7.5377</td>\n",
       "      <td>-5.0917</td>\n",
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       "      <td>6.0916</td>\n",
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       "      <td>-8.0509</td>\n",
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       "      <td>2.5580</td>\n",
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       "      <td>6.6278</td>\n",
       "      <td>-3.5643</td>\n",
       "      <td>-3.8402</td>\n",
       "      <td>3.7643</td>\n",
       "      <td>6.9524</td>\n",
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       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>1.1564</td>\n",
       "      <td>0.4062</td>\n",
       "      <td>9.6408</td>\n",
       "      <td>6.2806</td>\n",
       "      <td>6.8070</td>\n",
       "      <td>10.6484</td>\n",
       "      <td>3.1349</td>\n",
       "      <td>2.5027</td>\n",
       "      <td>7.5568</td>\n",
       "      <td>-2.3135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2509</td>\n",
       "      <td>-5.2098</td>\n",
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       "      <td>0.8537</td>\n",
       "      <td>3.3475</td>\n",
       "      <td>-1.1906</td>\n",
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       "      <td>0.6462</td>\n",
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       "      <th>959</th>\n",
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       "      <td>10.7596</td>\n",
       "      <td>11.8815</td>\n",
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       "      <td>2.1024</td>\n",
       "      <td>5.0906</td>\n",
       "      <td>6.2517</td>\n",
       "      <td>2.8882</td>\n",
       "      <td>6.1536</td>\n",
       "      <td>...</td>\n",
       "      <td>2.1465</td>\n",
       "      <td>2.1819</td>\n",
       "      <td>3.8106</td>\n",
       "      <td>4.5790</td>\n",
       "      <td>4.7688</td>\n",
       "      <td>3.6176</td>\n",
       "      <td>-6.5412</td>\n",
       "      <td>-5.9733</td>\n",
       "      <td>-3.8685</td>\n",
       "      <td>0.3668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 500 columns</p>\n",
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      "text/plain": [
       "        0       1        2        3       4        5       6       7    \\\n",
       "0    2.4530  4.3054   1.3759   7.2145 -1.4292  -6.1071  2.3902  5.1367   \n",
       "1   -0.8094  9.5179  -3.1323   6.8556 -5.5912 -11.5055  9.4154  8.8133   \n",
       "2    4.8952  2.7730  11.2608   6.5052 -3.0609  -5.0393  3.5208  8.8861   \n",
       "3    3.5139  1.3220   2.9945   7.5377 -5.0917  -8.4382  4.8114  6.0916   \n",
       "4    2.2301  5.4197   3.7703  -0.0298 -4.6229  -4.7862  7.2590  6.4673   \n",
       "..      ...     ...      ...      ...     ...      ...     ...     ...   \n",
       "955  1.0534 -1.9615   5.3656   1.8945  4.2108   5.1793  1.8477 -1.8662   \n",
       "956 -0.2895 -2.7420   4.1787   5.4836 -1.8574   2.2897  0.7738  2.6914   \n",
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       "959  2.6819  0.9365  10.7596  11.8815  1.6224   2.1024  5.0906  6.2517   \n",
       "\n",
       "        8        9    ...     490     491      492      493     494     495  \\\n",
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       "2   -1.3274  -5.0843  ...  1.5133  0.0921   3.6921  -7.8232  2.2719 -7.1014   \n",
       "3   -1.9661  -8.0509  ... -0.6172  0.6054   1.3821   3.6287 -3.2030 -3.1160   \n",
       "4   -6.6559  -0.5958  ...  0.3223 -1.4271 -10.2959 -12.4007  3.5146  2.6894   \n",
       "..      ...      ...  ...     ...     ...      ...      ...     ...     ...   \n",
       "955  2.4259   4.7404  ...  1.9197 -2.0060   5.4481   4.4486  3.0770  4.1036   \n",
       "956  9.4666   7.0554  ... -3.1762 -4.7290   1.6565  -5.1071  5.9790  6.2771   \n",
       "957  8.9512   3.6845  ... -0.4146 -6.3061   6.6734  -9.4104  7.6589  6.6278   \n",
       "958  7.5568  -2.3135  ...  0.2509 -5.2098   6.4856   0.8537  3.3475 -1.1906   \n",
       "959  2.8882   6.1536  ...  2.1465  2.1819   3.8106   4.5790  4.7688  3.6176   \n",
       "\n",
       "        496     497     498     499  \n",
       "0    4.6646  6.5535 -1.6587  6.0177  \n",
       "1    7.0897  1.9148  1.5971  5.4541  \n",
       "2    5.7336  9.2605  2.9299 -1.9654  \n",
       "3    2.7866  7.9140  2.5580  3.7401  \n",
       "4    5.0467 -0.7757  1.8140 -3.7787  \n",
       "..      ...     ...     ...     ...  \n",
       "955 -3.9061 -1.0129  3.3209 -0.5290  \n",
       "956 -5.8883 -8.4329 -1.9454  0.9830  \n",
       "957 -3.5643 -3.8402  3.7643  6.9524  \n",
       "958 -2.9053  0.6462  2.6135  1.8019  \n",
       "959 -6.5412 -5.9733 -3.8685  0.3668  \n",
       "\n",
       "[960 rows x 500 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4f84960a",
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = eigvct.iloc[:,:nComponent]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d0be7bca",
   "metadata": {},
   "outputs": [
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       "      <td>-0.001505</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>-0.001167</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>-0.000325</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>-0.000408</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>-0.000891</td>\n",
       "      <td>0.000208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000178</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.001443</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>-0.001811</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>-0.000407</td>\n",
       "      <td>-0.000771</td>\n",
       "      <td>-0.000451</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>-0.001233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>-0.000370</td>\n",
       "      <td>0.001192</td>\n",
       "      <td>-0.001477</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>-0.000898</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.001428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000174</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000804</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>-0.002369</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.001115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000287</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>-0.001292</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>-0.001864</td>\n",
       "      <td>-0.000639</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>-0.003548</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>-0.001548</td>\n",
       "      <td>-0.001637</td>\n",
       "      <td>0.001718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>-0.000256</td>\n",
       "      <td>-0.000719</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>0.002197</td>\n",
       "      <td>0.001234</td>\n",
       "      <td>0.002049</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>-0.003077</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.001117</td>\n",
       "      <td>0.002107</td>\n",
       "      <td>-0.001239</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>-0.001238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000723</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>-0.002209</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>-0.001866</td>\n",
       "      <td>-0.001186</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>-0.000153</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>-0.002007</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.000190</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.003574</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.001349</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>0.000764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>-0.001113</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>-0.000604</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>-0.000127</td>\n",
       "      <td>-0.002323</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000912</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>-0.000787</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>-0.001817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000172</td>\n",
       "      <td>-0.000627</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>0.001738</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>-0.001889</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>-0.000484</td>\n",
       "      <td>-0.001028</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>-0.002046</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>-0.002196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -0.000105  0.000477 -0.000122  0.000811 -0.001505  0.000064 -0.001167   \n",
       "1   -0.000178  0.000106  0.000391 -0.001443 -0.000515  0.000089  0.000312   \n",
       "2   -0.000149  0.000125  0.000719  0.001335 -0.000942 -0.002309  0.000356   \n",
       "3   -0.000174  0.000067  0.000574  0.000236 -0.000009 -0.000804  0.000018   \n",
       "4   -0.000287  0.000859  0.000617  0.000668 -0.001292  0.001648 -0.001265   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495 -0.000256 -0.000719  0.000564 -0.001085  0.002197  0.001234  0.002049   \n",
       "496 -0.000051 -0.000723  0.000010 -0.002209 -0.000266 -0.001866 -0.001186   \n",
       "497  0.000190 -0.000349 -0.000977 -0.000453 -0.000839 -0.001561 -0.003574   \n",
       "498 -0.000042 -0.000170 -0.001113  0.000964  0.000718 -0.000604 -0.000384   \n",
       "499 -0.000185 -0.000172 -0.000627  0.002653  0.001738  0.000828 -0.001889   \n",
       "\n",
       "           7         8         9         10        11        12        13  \\\n",
       "0    0.000629 -0.000325  0.001204 -0.000408  0.001098 -0.000510  0.000348   \n",
       "1   -0.000700  0.001253 -0.001811  0.000260 -0.000407 -0.000771 -0.000451   \n",
       "2   -0.000370  0.001192 -0.001477  0.001862 -0.000898 -0.000665  0.001869   \n",
       "3    0.000073  0.002019 -0.002369  0.001528  0.000125  0.000476  0.001006   \n",
       "4    0.001282 -0.001864 -0.000639  0.000299 -0.003548  0.000587 -0.001548   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "495  0.000149  0.001369 -0.003077  0.000878  0.001117  0.002107 -0.001239   \n",
       "496 -0.001148  0.000779 -0.000153  0.000680  0.000533  0.000979 -0.002007   \n",
       "497  0.000513 -0.001148  0.000184  0.001021  0.000055  0.001349 -0.002050   \n",
       "498 -0.000127 -0.002323  0.000124  0.000912  0.001587  0.002913 -0.000787   \n",
       "499  0.000525 -0.000484 -0.001028  0.002731  0.000201  0.003199 -0.002046   \n",
       "\n",
       "           14        15  \n",
       "0   -0.000891  0.000208  \n",
       "1    0.001210 -0.001233  \n",
       "2    0.001183  0.001428  \n",
       "3    0.001898  0.001115  \n",
       "4   -0.001637  0.001718  \n",
       "..        ...       ...  \n",
       "495  0.001061 -0.001238  \n",
       "496  0.000251  0.000520  \n",
       "497 -0.002169  0.000764  \n",
       "498  0.000197 -0.001817  \n",
       "499  0.003460 -0.002196  \n",
       "\n",
       "[500 rows x 16 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7ed9703e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score_test = np.matmul(Kt_c, eigvct.iloc[:,:nComponent])\n",
    "score_test = Kt_c@coeff\n",
    "score_test = round(score_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e6ef5ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<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>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.3604</td>\n",
       "      <td>-0.2531</td>\n",
       "      <td>-0.6993</td>\n",
       "      <td>-0.8177</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.6856</td>\n",
       "      <td>-2.7665</td>\n",
       "      <td>0.9577</td>\n",
       "      <td>2.2987</td>\n",
       "      <td>-0.9493</td>\n",
       "      <td>-0.3551</td>\n",
       "      <td>0.8932</td>\n",
       "      <td>-1.1865</td>\n",
       "      <td>0.9162</td>\n",
       "      <td>1.6474</td>\n",
       "      <td>0.3252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.3918</td>\n",
       "      <td>-0.1098</td>\n",
       "      <td>-1.6372</td>\n",
       "      <td>-1.7064</td>\n",
       "      <td>1.0007</td>\n",
       "      <td>-0.6040</td>\n",
       "      <td>-1.1605</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.7224</td>\n",
       "      <td>-1.0358</td>\n",
       "      <td>-1.1129</td>\n",
       "      <td>0.6940</td>\n",
       "      <td>-0.3363</td>\n",
       "      <td>0.9507</td>\n",
       "      <td>1.8977</td>\n",
       "      <td>0.1879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.2030</td>\n",
       "      <td>-0.2892</td>\n",
       "      <td>-1.3112</td>\n",
       "      <td>0.9014</td>\n",
       "      <td>-1.8566</td>\n",
       "      <td>-3.2839</td>\n",
       "      <td>-0.8338</td>\n",
       "      <td>0.3293</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>-0.6357</td>\n",
       "      <td>-1.0387</td>\n",
       "      <td>0.3357</td>\n",
       "      <td>-1.2612</td>\n",
       "      <td>0.2772</td>\n",
       "      <td>1.4411</td>\n",
       "      <td>-0.1846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.1054</td>\n",
       "      <td>-0.1619</td>\n",
       "      <td>-1.6170</td>\n",
       "      <td>0.3136</td>\n",
       "      <td>-0.0617</td>\n",
       "      <td>-0.7991</td>\n",
       "      <td>-2.2887</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>0.5040</td>\n",
       "      <td>-0.5606</td>\n",
       "      <td>-1.4122</td>\n",
       "      <td>1.0158</td>\n",
       "      <td>-0.8953</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>1.5574</td>\n",
       "      <td>0.1805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.7028</td>\n",
       "      <td>-0.3422</td>\n",
       "      <td>-1.4123</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>-0.2329</td>\n",
       "      <td>-1.5461</td>\n",
       "      <td>0.4154</td>\n",
       "      <td>-0.1505</td>\n",
       "      <td>1.4497</td>\n",
       "      <td>-0.8796</td>\n",
       "      <td>-0.5451</td>\n",
       "      <td>0.8385</td>\n",
       "      <td>-1.5455</td>\n",
       "      <td>1.8495</td>\n",
       "      <td>0.4357</td>\n",
       "      <td>-0.6227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.8878</td>\n",
       "      <td>-0.1162</td>\n",
       "      <td>1.2181</td>\n",
       "      <td>0.6203</td>\n",
       "      <td>0.0328</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.0755</td>\n",
       "      <td>-0.2361</td>\n",
       "      <td>-0.4406</td>\n",
       "      <td>-0.1801</td>\n",
       "      <td>-0.0222</td>\n",
       "      <td>0.3148</td>\n",
       "      <td>0.9072</td>\n",
       "      <td>-0.6936</td>\n",
       "      <td>0.0421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0.0374</td>\n",
       "      <td>1.1344</td>\n",
       "      <td>0.9616</td>\n",
       "      <td>0.5681</td>\n",
       "      <td>1.0508</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>1.6541</td>\n",
       "      <td>0.5334</td>\n",
       "      <td>0.2403</td>\n",
       "      <td>0.6899</td>\n",
       "      <td>0.2288</td>\n",
       "      <td>0.7499</td>\n",
       "      <td>-0.0726</td>\n",
       "      <td>-0.6365</td>\n",
       "      <td>0.8707</td>\n",
       "      <td>1.1648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>0.1063</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>0.2034</td>\n",
       "      <td>1.4146</td>\n",
       "      <td>0.2208</td>\n",
       "      <td>-1.3191</td>\n",
       "      <td>2.0086</td>\n",
       "      <td>-0.1075</td>\n",
       "      <td>-0.4928</td>\n",
       "      <td>-1.2571</td>\n",
       "      <td>0.3347</td>\n",
       "      <td>-1.3224</td>\n",
       "      <td>0.5357</td>\n",
       "      <td>-0.5876</td>\n",
       "      <td>0.2510</td>\n",
       "      <td>0.0718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>0.5665</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>-0.7404</td>\n",
       "      <td>1.1014</td>\n",
       "      <td>-0.5282</td>\n",
       "      <td>-1.0038</td>\n",
       "      <td>0.4503</td>\n",
       "      <td>-0.7093</td>\n",
       "      <td>-1.0642</td>\n",
       "      <td>-1.3173</td>\n",
       "      <td>1.3607</td>\n",
       "      <td>-1.0579</td>\n",
       "      <td>-0.3410</td>\n",
       "      <td>0.7862</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2647</td>\n",
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       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.7182</td>\n",
       "      <td>0.6005</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>-0.4035</td>\n",
       "      <td>0.6896</td>\n",
       "      <td>-0.0875</td>\n",
       "      <td>-0.0862</td>\n",
       "      <td>0.6301</td>\n",
       "      <td>-0.0411</td>\n",
       "      <td>1.4893</td>\n",
       "      <td>0.9483</td>\n",
       "      <td>-1.0853</td>\n",
       "      <td>0.8621</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.4048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1       2       3       4       5       6       7       8   \\\n",
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       "3   -1.1054 -0.1619 -1.6170  0.3136 -0.0617 -0.7991 -2.2887  0.8294  0.5040   \n",
       "4   -1.7028 -0.3422 -1.4123  0.1468 -0.2329 -1.5461  0.4154 -0.1505  1.4497   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
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       "957  0.1063  0.9899  0.2034  1.4146  0.2208 -1.3191  2.0086 -0.1075 -0.4928   \n",
       "958  0.5665  0.9899 -0.7404  1.1014 -0.5282 -1.0038  0.4503 -0.7093 -1.0642   \n",
       "959  0.2917  0.7182  0.6005  0.7027 -0.4035  0.6896 -0.0875 -0.0862  0.6301   \n",
       "\n",
       "         9       10      11      12      13      14      15  \n",
       "0   -0.9493 -0.3551  0.8932 -1.1865  0.9162  1.6474  0.3252  \n",
       "1   -1.0358 -1.1129  0.6940 -0.3363  0.9507  1.8977  0.1879  \n",
       "2   -0.6357 -1.0387  0.3357 -1.2612  0.2772  1.4411 -0.1846  \n",
       "3   -0.5606 -1.4122  1.0158 -0.8953  0.8850  1.5574  0.1805  \n",
       "4   -0.8796 -0.5451  0.8385 -1.5455  1.8495  0.4357 -0.6227  \n",
       "..      ...     ...     ...     ...     ...     ...     ...  \n",
       "955 -0.4406 -0.1801 -0.0222  0.3148  0.9072 -0.6936  0.0421  \n",
       "956  0.6899  0.2288  0.7499 -0.0726 -0.6365  0.8707  1.1648  \n",
       "957 -1.2571  0.3347 -1.3224  0.5357 -0.5876  0.2510  0.0718  \n",
       "958 -1.3173  1.3607 -1.0579 -0.3410  0.7862  0.2145  0.2647  \n",
       "959 -0.0411  1.4893  0.9483 -1.0853  0.8621  0.1500  0.4048  \n",
       "\n",
       "[960 rows x 16 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "04ee9b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = score_test@invegvldiag@score_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "46a9f406",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = round(T2_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8b99f7d0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-7.3106</td>\n",
       "      <td>-3.2213</td>\n",
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       "      <td>3.4600</td>\n",
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       "      <td>33.6718</td>\n",
       "      <td>38.9320</td>\n",
       "      <td>29.5791</td>\n",
       "      <td>31.8670</td>\n",
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       "      <td>26.8640</td>\n",
       "      <td>29.4202</td>\n",
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       "      <td>29.5791</td>\n",
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       "      <td>0.3995</td>\n",
       "      <td>1.7287</td>\n",
       "      <td>10.3191</td>\n",
       "      <td>6.4568</td>\n",
       "      <td>9.6505</td>\n",
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       "      <th>956</th>\n",
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       "      <td>-10.7621</td>\n",
       "      <td>-7.0129</td>\n",
       "      <td>-6.2673</td>\n",
       "      <td>-5.1080</td>\n",
       "      <td>-9.7680</td>\n",
       "      <td>-2.5468</td>\n",
       "      <td>-11.2211</td>\n",
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       "      <td>-0.9606</td>\n",
       "      <td>0.8702</td>\n",
       "      <td>8.2960</td>\n",
       "      <td>-8.0097</td>\n",
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       "      <td>6.4568</td>\n",
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       "      <td>17.4038</td>\n",
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       "      <th>958</th>\n",
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       "      <td>-4.5145</td>\n",
       "      <td>-2.1227</td>\n",
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       "      <td>-8.5740</td>\n",
       "      <td>-15.9390</td>\n",
       "      <td>10.2292</td>\n",
       "      <td>-2.1435</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2621</td>\n",
       "      <td>10.9146</td>\n",
       "      <td>7.1344</td>\n",
       "      <td>15.1896</td>\n",
       "      <td>-3.9533</td>\n",
       "      <td>8.9347</td>\n",
       "      <td>2.1548</td>\n",
       "      <td>17.4038</td>\n",
       "      <td>24.3462</td>\n",
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       "      <th>959</th>\n",
       "      <td>0.3874</td>\n",
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       "      <td>-4.6875</td>\n",
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       "      <td>-6.4660</td>\n",
       "      <td>-5.7386</td>\n",
       "      <td>-4.2846</td>\n",
       "      <td>-3.9078</td>\n",
       "      <td>...</td>\n",
       "      <td>9.6306</td>\n",
       "      <td>8.2688</td>\n",
       "      <td>4.8996</td>\n",
       "      <td>1.5226</td>\n",
       "      <td>6.5948</td>\n",
       "      <td>4.3955</td>\n",
       "      <td>7.1952</td>\n",
       "      <td>1.4517</td>\n",
       "      <td>6.1000</td>\n",
       "      <td>13.8034</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>960 rows × 960 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         0        1        2        3        4        5        6        7    \\\n",
       "0    50.5085  36.5622  21.0911  35.9764  27.9388  24.2821  38.8713  40.2541   \n",
       "1    36.5622  44.1408  24.1723  33.0329  32.2888  18.2509  38.1416  31.4382   \n",
       "2    21.0911  24.1723  54.5690  33.6718  37.7798  52.6992  38.5817  36.2571   \n",
       "3    35.9764  33.0329  33.6718  38.9320  29.5791  31.8670  39.0334  31.6649   \n",
       "4    27.9388  32.2888  37.7798  29.5791  46.2146  47.2491  36.0418  37.0339   \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
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       "3    26.8640  29.4202  ...  3.6656   8.3199  -0.6634   2.0328   2.6901   \n",
       "4    38.2983  35.1386  ... -5.2494  -8.0904 -13.0816  -3.3369  -4.3546   \n",
       "..       ...      ...  ...     ...      ...      ...      ...      ...   \n",
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       "\n",
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       "..       ...      ...      ...      ...      ...  \n",
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       "956   6.4568  22.1056  12.5599   2.1548   7.1952  \n",
       "957   9.6505  12.5599  26.6071  17.4038   1.4517  \n",
       "958   8.9347   2.1548  17.4038  24.3462   6.1000  \n",
       "959   4.3955   7.1952   1.4517   6.1000  13.8034  \n",
       "\n",
       "[960 rows x 960 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8625b5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cabfbdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960,)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0bc9cbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         53.8104,   65.2114,   57.7364,   41.7339,   34.5675,   48.7248,\n",
       "         51.7116,   54.5025,   42.833 ,   48.1341,   41.9566,   38.4563,\n",
       "         69.1101,   37.982 ,   57.8793,   73.9602,   56.2166,   50.6768,\n",
       "         65.3536,   35.4015,   51.4988,   51.0105,   52.8834,   66.0341,\n",
       "         47.4038,   44.8141,   55.5371,   36.0899,   46.3372,   50.4559,\n",
       "         51.9085,   42.7719,   24.61  ,   40.1115,   56.5666,   71.6126,\n",
       "         31.5596,   37.4113,   49.8163,   26.2395,   58.2108,   41.8517,\n",
       "         30.7517,   38.5998,   31.4436,   41.8592,   52.6668,   44.0196,\n",
       "         51.8414,   54.733 ,   85.8205,   54.8576,   45.565 ,   46.7778,\n",
       "         44.7996,   52.9644,   68.1844,   46.7768,   60.9264,   52.1995,\n",
       "         39.837 ,   72.1015,   46.3527,   61.958 ,   59.3382,   98.249 ,\n",
       "         44.687 ,   46.8891,   39.6582,   49.9282,   62.4079,   51.2108,\n",
       "         57.7467,   48.8893,   66.8558,   45.9179,   40.6318,   36.5645,\n",
       "         31.8694,   62.585 ,   47.0298,   58.2763,   40.5004,   57.1787,\n",
       "         45.8166,   33.3926,   30.9831,   29.9702,   32.1411,   42.4655,\n",
       "         48.1982,   91.0252,   74.926 ,   78.454 ,   57.1145,   52.0967,\n",
       "         90.6599,   72.4402,   96.1757,   97.5893,  102.7958,  123.2399,\n",
       "        133.1975,  140.8442,  162.2456,  146.7144,  141.1724,  130.9896,\n",
       "         90.9238,  116.5485,  125.1005,  112.3489,  106.6036,  123.0006,\n",
       "        109.3027,  101.5299,   88.7629,   83.2403,   63.0587,   70.2978,\n",
       "         57.2865,   49.526 ,   48.9312,   56.8169,   68.2271,   46.5632,\n",
       "         45.0091,   47.1393,   63.6609,   42.78  ,   39.9999,   42.8037,\n",
       "         48.6455,   50.0219,   46.6125,   59.3049,   48.2736,   43.9844,\n",
       "         30.4642,   60.3833,   58.836 ,   52.3023,   36.1834,   78.2786,\n",
       "         34.6158,   44.7469,   35.5731,   32.5736,   43.9461,   46.5779,\n",
       "         38.973 ,   51.2943,   57.2559,   46.5803,   39.2233,   61.9987,\n",
       "         58.1861,   43.507 ,   45.682 ,   76.144 ,   78.994 ,   72.6732,\n",
       "        103.0202,  101.8015,  126.0641,  122.0235,  191.0838,  209.0093,\n",
       "        260.0472,  293.1453,  359.1359,  409.9494,  417.5396,  467.5622,\n",
       "        536.0637,  592.5203,  721.6129,  739.107 ,  744.1102,  806.7117,\n",
       "        879.9688,  898.2324, 1002.1271, 1047.2448, 1062.2838, 1032.5122,\n",
       "       1075.4677, 1093.4238, 1025.673 , 1027.2069, 1088.8283, 1069.546 ,\n",
       "       1010.2153, 1019.4895,  906.8268,  891.9188,  765.9063,  714.9228,\n",
       "        635.0909,  601.9699,  620.6621,  557.0359,  445.1187,  428.7546,\n",
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       "        390.0287,  410.0015,  497.1228,  546.702 ,  614.9495,  621.705 ,\n",
       "        807.6194,  859.9906,  874.5671,  846.5581,  866.1462,  920.7895,\n",
       "        840.9693,  899.1962,  731.3469,  692.3425,  690.6209,  657.079 ,\n",
       "        657.522 ,  615.9986,  519.0108,  506.0945,  423.0702,  418.3398,\n",
       "        397.2885,  336.5815,  280.7078,  268.315 ,  295.1709,  259.4048,\n",
       "        359.4059,  283.9217,  296.4884,  316.2221,  458.4844,  429.0784,\n",
       "        398.7079,  500.4591,  526.5372,  539.674 ,  656.2714,  694.515 ,\n",
       "        776.124 ,  725.0063,  719.0312,  752.312 ,  793.2116,  808.2583,\n",
       "        914.8213,  940.0524,  800.3143,  805.8106,  817.8754,  745.7711,\n",
       "        819.5775,  822.6582,  826.2232,  738.6269,  597.2878,  556.0152,\n",
       "        414.8594,  391.6983,  300.8784,  230.935 ,  189.016 ,  159.9198,\n",
       "        152.5609,   90.1083,   67.1419,   47.2415,   63.3573,   63.766 ,\n",
       "        119.8521,  128.734 ,  210.1361,  149.2812,  237.153 ,  196.3257,\n",
       "        274.8989,  313.3286,  296.6925,  293.0592,  325.582 ,  374.1474,\n",
       "        389.2012,  484.1118,  541.3451,  576.1483,  631.9184,  631.9609,\n",
       "        667.2633,  638.3271,  640.0954,  597.5482,  656.0529,  624.5119,\n",
       "        578.5912,  561.1565,  485.8954,  519.8142,  539.0979,  543.4792,\n",
       "        470.0004,  408.7911,  362.0764,  352.9459,  317.4235,  332.3136,\n",
       "        289.8404,  273.1627,  202.3081,  205.1239,  194.2486,  274.0293,\n",
       "        222.1401,  224.5757,  149.656 ,  155.1139,  181.7799,  179.0114,\n",
       "        236.2341,  230.6687,  260.6526,  324.4169,  237.0896,  216.0542,\n",
       "        258.4264,  214.9115,  236.8159,  214.5441,  216.6659,  246.8012,\n",
       "        254.164 ,  245.2932,  314.3279,  311.0794,  237.7911,  283.8224,\n",
       "        213.9643,  215.3474,  148.5738,  159.0559,  104.1513,   90.0167,\n",
       "        102.3461,   64.3519,   68.7673,   81.838 ,   71.9487,   92.3312,\n",
       "         94.5785,   63.1914,   40.6524,   53.8976,   56.5989,  100.5148,\n",
       "         69.9184,   74.4531,  118.5   ,  114.9164,   68.5492,   78.4983,\n",
       "         69.1373,  112.6577,   97.3792,   94.1758,  150.5258,  103.5565,\n",
       "        175.2217,  148.3202,  160.1029,  138.6826,  215.3189,  195.1561,\n",
       "        195.3966,  171.7973,  134.6736,  133.6079,  120.7041,  133.2899,\n",
       "        175.9212,  181.1594,  191.3905,  150.7619,  146.0092,  116.6642,\n",
       "        146.38  ,  121.4297,   96.1061,   73.5051,   58.9427,   49.2139,\n",
       "         64.4559,   64.976 ,   32.3237,   42.4399,   29.3204,   24.2386,\n",
       "         52.1806,   40.9339,   51.6181,   37.002 ,   30.8131,   33.5543,\n",
       "         72.7608,   82.3572,  103.5918,  106.6142,   59.4917,   52.3498,\n",
       "         72.304 ,   58.7582,   67.2843,   69.4449,   73.9421,   69.8123,\n",
       "         86.4851,   95.6919,   85.0219,  113.2622,  118.9352,  113.5227,\n",
       "         73.8967,   79.0564,   81.0929,   79.3723,   73.4573,  105.2299,\n",
       "         78.2912,   67.2026,   72.06  ,  102.9606,   65.5171,   53.7331,\n",
       "         48.2688,   46.4347,   43.4687,   52.4587,   43.5228,   34.9801,\n",
       "         44.3275,   45.2465,   43.9487,   83.2716,   52.1491,   62.4081,\n",
       "         54.111 ,   57.9999,   52.35  ,   52.7444,   57.1514,   84.4918,\n",
       "         80.4555,   89.2648,   73.9363,   68.1161,   77.74  ,   72.537 ,\n",
       "         83.0669,   64.3961,   83.266 ,   93.323 ,   81.951 ,   79.9086,\n",
       "         60.8234,   49.8514,   75.3062,  105.3918,  106.9411,  131.055 ,\n",
       "        126.2514,  160.0186,  136.065 ,  111.8726,  110.2772,  121.3322,\n",
       "         91.9546,   82.8557,  125.2094,  123.5539,   95.6238,  106.8373,\n",
       "        108.6378,  103.3989,  160.9245,  127.7284,  102.9817,   87.1577,\n",
       "         70.5467,   60.3466,   47.8058,   39.7309,   56.6073,  106.5736,\n",
       "         95.8866,   87.3823,   67.6708,   64.3053,   58.7341,   71.2058,\n",
       "         69.281 ,   71.4287,   72.1232,   88.0513,   63.4166,   55.9024,\n",
       "         64.6504,   45.883 ,   58.5848,   77.6295,   40.4899,   39.2082,\n",
       "         43.2162,   51.1076,   57.9318,   55.025 ,   56.5529,   51.9   ,\n",
       "         50.6258,   58.8811,   49.9073,   32.8618,   37.4446,   27.4253,\n",
       "         19.8265,   46.5309,   12.7239,   24.8121,   34.2412,   24.1578,\n",
       "         20.2824,   25.1716,   17.9355,   40.6953,   23.2727,   37.2   ,\n",
       "         79.2275,   45.8826,   29.0763,   23.5027,   36.6054,   22.5982,\n",
       "         22.1661,   47.417 ,   70.1304,   50.3386,   72.3557,   48.0195,\n",
       "         49.3934,   72.2174,   57.542 ,   41.3188,   43.7689,   56.8728,\n",
       "         67.0689,   76.2156,   95.3291,  117.9793,  102.1556,  112.4046,\n",
       "        117.0894,  115.561 ,   91.5484,   83.7432,   60.0575,   55.1059,\n",
       "         54.1904,   55.4515,   62.8586,   63.4649,   52.6256,   33.7475,\n",
       "         69.7289,   54.931 ,   37.2404,   24.9063,   32.7193,   20.818 ,\n",
       "         43.4831,   33.3519,   49.0513,   34.2464,   20.6195,   32.0056,\n",
       "         29.135 ,   23.9532,   33.6841,   36.3899,   55.4944,   44.3437,\n",
       "         62.4661,   57.0368,   43.3666,   36.8438,   54.0979,   56.6789,\n",
       "         29.9812,   33.7819,   82.0631,   63.2748,   49.0248,   63.5123,\n",
       "         44.5616,   28.9969,   17.9626,   17.8289,   32.4793,   26.8026,\n",
       "         16.7868,   15.855 ,   14.823 ,   29.4801,   34.8298,   21.6121,\n",
       "         16.6091,   21.1535,   30.812 ,   44.2132,   25.4457,   21.2425,\n",
       "         36.0687,   21.6297,   29.8236,   24.7892,   37.4086,   29.3937,\n",
       "         29.3689,   33.7904,   36.4213,   36.4   ,   68.0104,   50.6145,\n",
       "         66.6716,   59.4967,   45.174 ,   32.073 ,   35.1765,   43.721 ,\n",
       "         29.0588,   50.4487,   38.9532,   21.9792,   24.0478,   66.5525,\n",
       "         43.2489,   29.9315,   35.6567,   25.8606,   15.4183,   20.9749,\n",
       "         35.4576,   42.1064,   43.4632,   32.8593,   43.3746,   47.2719,\n",
       "         51.1597,   56.8141,   33.8139,   35.0363,   25.6334,   34.7534,\n",
       "         38.0789,   32.8756,   12.3736,   20.4695,   29.8676,   20.8211,\n",
       "         28.531 ,   47.4466,   14.1674,   17.4184,   48.4962,   28.1089,\n",
       "         16.5062,   24.2138,   38.1072,   24.7613,   33.3535,   31.9592,\n",
       "         17.6464,   24.909 ,   27.1275,   56.3356,   23.0092,   25.6027,\n",
       "         10.6877,   14.5878,   16.7714,   24.8981,   21.5742,   32.7624,\n",
       "         22.1583,   20.9905,   32.8282,   28.0098,   38.6657,   43.2922,\n",
       "         46.5277,   27.3576,   28.3048,   42.2621,   27.494 ,   25.145 ,\n",
       "         25.398 ,   59.5508,   28.769 ,   44.2676,   46.3043,   25.4242,\n",
       "         25.5752,   23.8506,   29.4295,   44.6584,   52.9287,   50.1029,\n",
       "         27.4327,   38.5748,   42.3797,   29.7578,   19.9348,   43.1565,\n",
       "         54.4397,   31.8909,   40.1535,   42.8222,   89.0463,   48.9654,\n",
       "         61.4624,   75.6694,   28.9152,   43.1606,   53.4463,   36.1556,\n",
       "         24.9394,   41.6081,   29.2052,   28.7724,   40.806 ,   32.3778,\n",
       "         41.3673,   75.9784,   60.8077,   73.2603,   54.4673,   33.9823,\n",
       "         52.586 ,   47.3342,   62.4193,   38.394 ,   79.5881,   81.9796,\n",
       "         30.5951,   36.0264,   51.1009,   28.6777,   25.7649,   50.1263,\n",
       "         29.718 ,   29.3444,   29.9804,   21.5944,   20.9491,   36.4994,\n",
       "         30.1737,   27.9598,   43.9824,   32.2288,   67.004 ,   64.8177,\n",
       "         35.0817,   37.2241,   18.8932,   43.5763,   29.4625,   30.1974,\n",
       "         39.3186,   23.2784,   11.8544,   15.2174,   23.7361,   24.1395,\n",
       "         31.9524,   23.4872,   20.6444,   13.4136,   17.6519,   18.9661,\n",
       "         20.393 ,   16.01  ,   28.8554,   15.8156,   28.8173,   46.0097,\n",
       "         24.7545,   40.5919,   32.3541,   31.309 ,   19.7498,   35.8526,\n",
       "         34.5758,   49.4199,   76.0428,   56.3737,   19.6941,   18.3285,\n",
       "         52.0778,   27.9253,   37.2329,   36.2744,   24.0972,   51.2147,\n",
       "         34.1928,   40.6108,   11.507 ,   23.9226,   25.1954,   56.2621,\n",
       "         19.6319,   32.3843,   16.0387,   19.3624,   42.4559,   18.3638,\n",
       "         36.636 ,   41.9682,   14.7018,   30.4276,   27.0198,   16.4863,\n",
       "         34.9343,   46.4011,   41.1679,   29.9707,   17.8168,   23.0206,\n",
       "         24.1312,   31.9546,   33.9966,   34.8681,   61.6865,   63.2379,\n",
       "         59.0701,   48.4887,   40.216 ,   37.3428,   53.1576,   29.2915,\n",
       "         45.5334,   53.499 ,   60.0056,   43.505 ,   29.6204,   46.6379,\n",
       "         42.0646,   55.4087,   57.6127,   46.5645,   63.3181,   62.9964,\n",
       "         42.6857,   48.1669,   52.4086,   35.0481,   58.3028,   69.4966,\n",
       "        105.0649,   65.9483,   70.1879,   44.4256,   17.007 ,   44.556 ,\n",
       "         13.1944,   31.2018,   27.9458,   50.339 ,   24.0726,   30.9457,\n",
       "         52.9032,   76.6183,   47.2915,   57.3239,   34.4416,   20.4045,\n",
       "         48.6679,   31.0711,   24.6573,   27.4029,   23.9218,   15.4287,\n",
       "         24.8714,   17.3257,   19.7397,   28.5145,   36.3062,   33.9125,\n",
       "         39.2812,   48.5777,   21.8143,   28.2378,   13.6969,   36.0031,\n",
       "         24.5632,   23.7769,    8.4027,   35.0895,   22.571 ,   44.6769,\n",
       "         35.1692,   59.3064,   21.0788,   17.7966,   23.7662,   24.7225,\n",
       "         12.6184,   19.076 ,   20.4818,   26.5988,   14.6234,   20.9778,\n",
       "         65.2187,   72.8664,   23.7847,   26.6893,   16.6106,   17.5852,\n",
       "          9.446 ,   32.7002,   35.8492,   38.589 ,   39.9733,   44.2268,\n",
       "         18.3532,   10.3191,   22.1056,   26.6071,   24.3462,   13.8034])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "baf91161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.semilogy(T2_test_final,'.')\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.axvline(160, c='k')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "df4acba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(T2_test_final)\n",
    "plt.axhline(T2_trlim, ls='--',c='r')\n",
    "plt.axvline(160, c='k')\n",
    "plt.title('$T^2$ statistic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "a3535532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.84625\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_test_final[160:]>T2_trlim).sum()/T2_test_final[160:].shape[0]}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}