E21_kpca with tep data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69099a37",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62cb8c04",
   "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",
    "from sklearn.metrics import pairwise_distances\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7364b34",
   "metadata": {},
   "source": [
    "## The Datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44bbce27",
   "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": "code",
   "execution_count": 3,
   "id": "c05d15ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_normal = pd.read_csv('./data/normal_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58fef862",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_faulty = pd.read_csv('./data/faulty_testing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1050d800",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preparing\n",
    "df_train = train_normal[(train_normal.simulationRun>=1)].iloc[:500,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c9c5b13",
   "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": 7,
   "id": "43da3d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_trn = scl_pick('std', df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f996eac",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.098431</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>495</th>\n",
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       "      <td>-0.333028</td>\n",
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       "      <td>-1.429812</td>\n",
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       "      <td>-0.748315</td>\n",
       "      <td>-0.002052</td>\n",
       "      <td>-1.133966</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>496</th>\n",
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       "      <td>0.006947</td>\n",
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       "      <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",
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       "      <td>-1.668594</td>\n",
       "      <td>0.513518</td>\n",
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       "      <td>-0.596428</td>\n",
       "      <td>0.060264</td>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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       "<p>500 rows × 52 columns</p>\n",
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       "4    1.550795  0.497001 -0.325151 -0.085064 -0.084087  1.404994  0.124570   \n",
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       "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",
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       "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",
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       "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",
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       "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_trn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3fe8b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dt_trn)\n",
    "plt.title('Data Training')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3b1f496",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_tst = scl_pick('std', df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a76867d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.077973</td>\n",
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       "      <td>1.200694</td>\n",
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       "      <td>-0.999194</td>\n",
       "      <td>0.434172</td>\n",
       "      <td>1.529115</td>\n",
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       "      <td>-0.963397</td>\n",
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       "      <td>1.520488</td>\n",
       "      <td>0.478652</td>\n",
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       "      <td>-1.017636</td>\n",
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       "      <td>-2.042094</td>\n",
       "      <td>1.633316</td>\n",
       "      <td>-0.066664</td>\n",
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       "      <td>-0.350185</td>\n",
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       "      <td>1.239561</td>\n",
       "      <td>1.430950</td>\n",
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       "      <td>0.006400</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_tst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "280b78b3",
   "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(dt_tst)\n",
    "plt.title('Data Testing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7a2824d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "nComponent = 16 # 30\n",
    "nSamples = dt_trn.shape[0]\n",
    "conf_T2 = 0.95 # 0.99 or 0.95"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5a07f3",
   "metadata": {},
   "source": [
    "## Training Centraling kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e41e5d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training proccess\n",
    "A = dt_trn\n",
    "# K = np.matmul(A, A.T)\n",
    "K = A@A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4113a73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = round(K, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2774636",
   "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",
       "      <td>2.6487</td>\n",
       "      <td>6.9229</td>\n",
       "      <td>-7.3993</td>\n",
       "      <td>0.6240</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0        1        2        3        4        5       6       7    \\\n",
       "0  11.2546  -1.0061   0.0674  -2.9111   7.1808   2.8010  0.0057  1.4510   \n",
       "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = pd.DataFrame(K)\n",
    "K.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "05139535",
   "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": 18,
   "id": "fe0ab263",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = (nSamples, nSamples)\n",
    "unit = np.ones(s)/nSamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e6ffce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit = pd.DataFrame(unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b7c9f56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "unit = round(unit, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54706540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
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       "      <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>0.002</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",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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>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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3ee4555",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_c = K-unit@K-K@unit+unit@K@unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ffd7183",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_c = round(K_c, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab3cfd59",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-6.1012</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <td>-7.5140</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.3144</td>\n",
       "      <td>-1.2179</td>\n",
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       "       0        1        2        3        4        5       6       7    \\\n",
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       "3  -2.9111   5.6558  17.8784  28.0756  -1.2179   1.0285  7.3694  7.8788   \n",
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       "\n",
       "      8       9    ...     490     491     492     493     494      495  \\\n",
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       "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cbad11bb",
   "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": 26,
   "id": "5e7efd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAMBDA\n",
    "lambda_ = s\n",
    "lambd = lambda_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "587e881c",
   "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": 28,
   "id": "0adfab02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eigvls = eigvls.real\n",
    "eigvls = lambd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9723cb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eigvct = eigvct.real\n",
    "eigvct = coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c5b627b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvct = pd.DataFrame(eigvct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ec6e937d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-10.051432</td>\n",
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       "      <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",
       "    </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 -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": "bd7d81d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls = pd.DataFrame(eigvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12b6cde9",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigvls = round(eigvls, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e70745a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0\n",
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f56d9157",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.000891</td>\n",
       "      <td>0.000208</td>\n",
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       "    <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",
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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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       "    <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",
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       "      <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",
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       "    <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",
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       "      <th>...</th>\n",
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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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
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       "    <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",
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       "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": "af491687",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score matrix\n",
    "# score_train = np.matmul(K_c, eigvct.iloc[:,:nComponent])\n",
    "score_train = K_c@eigvct.iloc[:,:nComponent]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "67e26555",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_train = round(score_train, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7c05241a",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_train = pd.DataFrame(score_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "53fe74a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.3890</td>\n",
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       "      <td>0.1123</td>\n",
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       "      <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",
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       "      <td>0.2991</td>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "      <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",
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       "      <td>0.6059</td>\n",
       "      <td>-0.6695</td>\n",
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       "      <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",
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       "      <th>497</th>\n",
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       "      <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",
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       "    <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",
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       "    <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": "8f919297",
   "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": "9cfdfb43",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = a.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "db7433c2",
   "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": 43,
   "id": "eb6d1f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.diag(extract_egv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2bf3c3b0",
   "metadata": {},
   "outputs": [
    {
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       "        0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 1.0814]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "32925dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "invegvldiag = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f9dc0bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "invegvldiag = pd.DataFrame(invegvldiag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "83dac891",
   "metadata": {},
   "outputs": [
    {
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       "      <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>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",
       "      <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>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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invegvldiag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4cee76aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_train = score_train@invegvldiag@score_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9af9801d",
   "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",
       "      <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",
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       "      <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",
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       "  </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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11f1754e",
   "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": 51,
   "id": "72388897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.06410534,  17.13356805,  31.57729109,  17.96724498,\n",
       "        60.01392109,  73.57138485,  20.69971773,  26.75378575,\n",
       "        45.55946068,  89.28672287,  50.20350996,  29.67039613,\n",
       "        23.11270091,  46.31199509,  62.12537431, 123.63951942,\n",
       "        37.79833929,  55.1352752 ,  62.11333084,  36.49453737,\n",
       "        64.72843057,  57.29000657,  55.07678532,  62.31894957,\n",
       "        79.22266171,  76.09582623,  66.93273191,  67.77945238,\n",
       "        60.59398572,  51.43206619,  78.68884007, 104.30333661,\n",
       "       130.42941681, 112.62942674, 124.79532463, 133.11724262,\n",
       "       144.07527715, 118.88065709, 139.34458467, 162.44388393,\n",
       "       140.73309231, 154.34073166,  83.61047055,  89.51163164,\n",
       "        78.12363967, 188.4880916 , 129.97576039, 185.28078684,\n",
       "        79.26185343, 100.64652289, 136.32125441, 124.85116109,\n",
       "       121.85457456, 114.8956619 , 110.93928462, 116.65978355,\n",
       "        87.99168579,  63.87952353, 116.86930556, 127.71046859,\n",
       "       153.83640953, 179.95248007, 116.80480894, 136.71247129,\n",
       "       164.37578333, 217.28342828,  44.23027638,  40.4331888 ,\n",
       "        60.57462404,  69.62039014,  85.82227776,  77.42486854,\n",
       "        41.25566713,  45.57453962,  43.42026908,  21.25120106,\n",
       "        61.96183927,  25.37517827,  83.69465295,  57.44233958,\n",
       "        77.11942227,  84.65105363,  37.81044294,  30.91537607,\n",
       "        37.28440259,  51.83617719,  85.8552906 , 106.35514995,\n",
       "       146.07700979,  77.50226765,  28.29923941,  55.65898309,\n",
       "        55.14593251,  48.16584302,  60.82484973,  41.00701747,\n",
       "        17.25265795,  14.44042316,  20.76003263,  10.18537162,\n",
       "        24.3957861 ,  32.00428997,  41.53374288,  26.26382982,\n",
       "        61.69253718,  40.68637854,  66.41042174,  74.10468144,\n",
       "        61.4910692 ,  33.96026431,  53.51540855,  74.36072941,\n",
       "        61.81557015,  51.9358967 ,  76.85407889,  89.32935786,\n",
       "       135.87685395, 142.28458802, 198.53696401, 259.08648816,\n",
       "       160.76620034, 170.53525588, 121.57335476, 110.8254615 ,\n",
       "       109.2818748 , 212.48922883, 209.21309225, 164.98234526,\n",
       "       128.59757574, 110.67258692, 132.89031402, 137.161794  ,\n",
       "       100.3104487 , 132.11964471,  49.13264247,  54.40357979,\n",
       "        50.32344944,  73.44035164, 107.28129457,  51.4585893 ,\n",
       "        64.38924536,  32.11008238, 119.37991677,  56.68448817,\n",
       "        36.2805667 ,  45.89408725,  80.62289371,  64.00756778,\n",
       "        54.07167275,  38.41410156,  32.06863681,  27.97914437,\n",
       "        26.9120688 ,  36.19542659,  36.22732298,  85.5406473 ,\n",
       "        48.67296918,  74.28440645,  43.82896407,  60.61056681,\n",
       "       163.08016737, 140.52331353, 172.31829449, 176.66044484,\n",
       "        65.13541293, 108.78931937, 177.12571063, 150.38827636,\n",
       "       175.50183155, 185.8370921 , 151.78693897, 176.45912205,\n",
       "       104.54902594,  95.93531019, 187.20421501, 167.22978691,\n",
       "       159.77475005, 178.86581298, 147.4280217 , 140.17421641,\n",
       "       111.25616035, 100.69616993,  89.88202406,  96.12888629,\n",
       "        94.35713256,  86.92753501,  69.36048008,  87.28399952,\n",
       "       111.33639864, 100.38009334, 108.96896925,  99.24068932,\n",
       "        45.62476914,  86.20963398, 115.94379812,  97.13840952,\n",
       "        43.00011576,  51.78012059,  74.39067068,  64.1842816 ,\n",
       "        77.11143174, 118.37337606,  91.12103052,  46.9763654 ,\n",
       "        75.96294889, 113.37838781,  54.35209479,  46.09918746,\n",
       "        76.26977439,  78.89929449,  62.41090379,  51.74886528,\n",
       "        58.67272017,  64.52007078,  31.83194282,  51.26543099,\n",
       "        40.50532746,  48.16182889,  58.82254535,  59.18945883,\n",
       "        58.21706377,  52.15093678,  63.57424103,  71.19206026,\n",
       "        67.58965558,  85.63725033,  44.39764714,  29.45021001,\n",
       "        44.39570481,  45.58163406,  45.18673908,  29.74430807,\n",
       "        41.26013018,  35.71654708,  35.3691523 ,  17.61087976,\n",
       "        44.95434727,  55.0744068 ,  59.4201854 ,  70.19090875,\n",
       "        39.12597185,  51.53263674,  79.33804782,  49.82018129,\n",
       "        46.79984457,  44.03199633, 115.29859822, 143.63934199,\n",
       "       141.27201723, 140.73635835,  77.06041105,  91.88411996,\n",
       "       141.93148606, 204.52173251, 177.07271948, 136.48978325,\n",
       "       132.40994174, 110.41143068, 152.6038051 , 162.58424426,\n",
       "       165.31809787, 167.93290532, 225.96976662, 241.84099109,\n",
       "       298.42584078, 251.96309203, 221.8592542 , 194.11842118,\n",
       "       160.05877586, 175.23285925, 164.36907235, 134.53840486,\n",
       "       140.34259663, 125.95290108, 111.48866627, 118.77146029,\n",
       "       132.60862746,  86.44909968,  91.8617164 ,  90.66530241,\n",
       "       107.69476829, 115.11040706,  38.24428873,  84.68403227,\n",
       "        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",
       "       120.74649196, 156.93698043, 201.21860781, 220.039321  ,\n",
       "       201.91900004, 216.45351033, 220.89494155, 213.86770407,\n",
       "       188.9213724 , 212.37232994, 125.82671085, 111.61426357,\n",
       "       112.51191622, 112.55780308, 120.51283995, 145.42675426,\n",
       "        89.77982008,  74.87081034, 128.59254813, 104.05729022,\n",
       "        65.4820171 , 164.71468837, 115.08442938, 135.94409986,\n",
       "       151.34341658, 187.45544937, 266.07018231, 269.81806963,\n",
       "       251.54929933, 292.50805903, 297.83401328, 332.0371233 ,\n",
       "       454.67935173, 306.45457541, 399.0990504 , 390.65404711,\n",
       "       278.89392433, 346.63791987, 321.54039041, 321.13137513,\n",
       "       258.23660148, 282.82518118, 230.83983496, 224.38063621,\n",
       "       225.45635754, 236.63122566, 121.39198108, 129.75843793,\n",
       "       145.32932881, 156.13416631, 150.74859879, 130.93749226,\n",
       "       164.83960983, 139.05419812, 150.35597248, 185.0699513 ,\n",
       "       152.35314293, 156.02765851, 178.94892689, 120.25892752,\n",
       "       127.41816082, 106.5707443 , 126.6573094 ,  99.9224488 ,\n",
       "        67.37504205, 113.25822998, 172.78762278, 100.12924452,\n",
       "       103.26499058,  99.43516263, 191.00531766, 176.31352545,\n",
       "       341.92195949, 320.77879336, 175.05539299, 159.06169705,\n",
       "       177.04506624, 174.86363311, 277.9041378 , 407.39802211,\n",
       "       231.84462255, 242.60746469, 177.57987782, 177.2801477 ,\n",
       "       134.62919001, 195.13801071, 242.8670535 , 186.80684752,\n",
       "       308.20255806, 320.71236286, 228.42733222, 191.9990929 ,\n",
       "       167.3708611 , 152.51613324, 280.97591856, 247.34090494,\n",
       "       267.21757059, 291.29874379, 245.02173237, 205.93729024,\n",
       "       189.09737185, 209.76417367, 223.59196267, 236.83009377,\n",
       "       141.77594667, 159.42421182, 165.64963206, 202.62176266,\n",
       "       243.42764561, 247.03215699, 187.39860877, 155.88355284,\n",
       "       116.74990976, 119.2751313 , 236.40026297, 265.70510424,\n",
       "       315.43961797, 265.76583874, 171.27780194, 133.67698953,\n",
       "       148.14454431, 140.8189391 ,  87.18692909,  75.06523509,\n",
       "       108.14493541, 107.41432169, 121.93252319,  93.63995124,\n",
       "       138.37713557, 108.48397347,  81.81785259,  70.22525517,\n",
       "       171.95237253, 155.45812669,  97.40681817, 126.06663872,\n",
       "        97.46408506,  94.67272091, 210.28017443, 189.65743338,\n",
       "       132.13222607, 178.94602237, 108.78943475, 113.98175946,\n",
       "       151.80266058, 135.11271814, 175.72188645, 139.28985154,\n",
       "       137.49621111, 133.68019766, 139.27226056, 153.45732889,\n",
       "       160.95130438, 180.28538405, 184.83235607, 180.13461618,\n",
       "       164.52274116, 159.02844129, 148.50207174, 150.94234145,\n",
       "        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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_tfinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "376cf290",
   "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": 53,
   "id": "7a0ddbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.511202863723632"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "id": "3bc86782",
   "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": [
    "# 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": 55,
   "id": "75ab2c62",
   "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": 56,
   "id": "8f5237c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.962\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": "cc6dfa79",
   "metadata": {},
   "source": [
    "## Testing Centraling kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aa37823f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kt = np.matmul(dt_tst, dt_trn.T)\n",
    "Kt = dt_tst@dt_trn.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dccef119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960, 500)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a680390a",
   "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": 60,
   "id": "1ccc6ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centralize the kernel\n",
    "st = (len(dt_tst),len(dt_trn)) # 2000 or 960\n",
    "unit_t = np.ones(st)/len(dt_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6968b9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######\n",
    "unit_t = pd.DataFrame(unit_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5dd2d4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "unit_t = round(unit_t, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e77ce382",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>955</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",
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       "    <tr>\n",
       "      <th>956</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",
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       "      <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>957</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>958</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",
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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>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</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>960 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",
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       "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",
       "955  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "956  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "957  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "958  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002   \n",
       "959  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",
       "955  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "956  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "957  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "958  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "959  ...  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  0.002  \n",
       "\n",
       "[960 rows x 500 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7d1a3092",
   "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": 65,
   "id": "24d0cbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kt_c = round(Kt_c, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "603cbc1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.0897</td>\n",
       "      <td>1.9148</td>\n",
       "      <td>1.5971</td>\n",
       "      <td>5.4541</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.8952</td>\n",
       "      <td>2.7730</td>\n",
       "      <td>11.2608</td>\n",
       "      <td>6.5052</td>\n",
       "      <td>-3.0609</td>\n",
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       "      <td>-1.3274</td>\n",
       "      <td>-5.0843</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5133</td>\n",
       "      <td>0.0921</td>\n",
       "      <td>3.6921</td>\n",
       "      <td>-7.8232</td>\n",
       "      <td>2.2719</td>\n",
       "      <td>-7.1014</td>\n",
       "      <td>5.7336</td>\n",
       "      <td>9.2605</td>\n",
       "      <td>2.9299</td>\n",
       "      <td>-1.9654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <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",
       "      <td>-8.4382</td>\n",
       "      <td>4.8114</td>\n",
       "      <td>6.0916</td>\n",
       "      <td>-1.9661</td>\n",
       "      <td>-8.0509</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6172</td>\n",
       "      <td>0.6054</td>\n",
       "      <td>1.3821</td>\n",
       "      <td>3.6287</td>\n",
       "      <td>-3.2030</td>\n",
       "      <td>-3.1160</td>\n",
       "      <td>2.7866</td>\n",
       "      <td>7.9140</td>\n",
       "      <td>2.5580</td>\n",
       "      <td>3.7401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2301</td>\n",
       "      <td>5.4197</td>\n",
       "      <td>3.7703</td>\n",
       "      <td>-0.0298</td>\n",
       "      <td>-4.6229</td>\n",
       "      <td>-4.7862</td>\n",
       "      <td>7.2590</td>\n",
       "      <td>6.4673</td>\n",
       "      <td>-6.6559</td>\n",
       "      <td>-0.5958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3223</td>\n",
       "      <td>-1.4271</td>\n",
       "      <td>-10.2959</td>\n",
       "      <td>-12.4007</td>\n",
       "      <td>3.5146</td>\n",
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       "      <td>-0.7757</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>1.0534</td>\n",
       "      <td>-1.9615</td>\n",
       "      <td>5.3656</td>\n",
       "      <td>1.8945</td>\n",
       "      <td>4.2108</td>\n",
       "      <td>5.1793</td>\n",
       "      <td>1.8477</td>\n",
       "      <td>-1.8662</td>\n",
       "      <td>2.4259</td>\n",
       "      <td>4.7404</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9197</td>\n",
       "      <td>-2.0060</td>\n",
       "      <td>5.4481</td>\n",
       "      <td>4.4486</td>\n",
       "      <td>3.0770</td>\n",
       "      <td>4.1036</td>\n",
       "      <td>-3.9061</td>\n",
       "      <td>-1.0129</td>\n",
       "      <td>3.3209</td>\n",
       "      <td>-0.5290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>-0.2895</td>\n",
       "      <td>-2.7420</td>\n",
       "      <td>4.1787</td>\n",
       "      <td>5.4836</td>\n",
       "      <td>-1.8574</td>\n",
       "      <td>2.2897</td>\n",
       "      <td>0.7738</td>\n",
       "      <td>2.6914</td>\n",
       "      <td>9.4666</td>\n",
       "      <td>7.0554</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.1762</td>\n",
       "      <td>-4.7290</td>\n",
       "      <td>1.6565</td>\n",
       "      <td>-5.1071</td>\n",
       "      <td>5.9790</td>\n",
       "      <td>6.2771</td>\n",
       "      <td>-5.8883</td>\n",
       "      <td>-8.4329</td>\n",
       "      <td>-1.9454</td>\n",
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       "    <tr>\n",
       "      <th>957</th>\n",
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       "      <td>6.5509</td>\n",
       "      <td>4.1347</td>\n",
       "      <td>4.1093</td>\n",
       "      <td>7.8311</td>\n",
       "      <td>0.8544</td>\n",
       "      <td>1.7680</td>\n",
       "      <td>8.9512</td>\n",
       "      <td>3.6845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4146</td>\n",
       "      <td>-6.3061</td>\n",
       "      <td>6.6734</td>\n",
       "      <td>-9.4104</td>\n",
       "      <td>7.6589</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "      <td>6.4856</td>\n",
       "      <td>0.8537</td>\n",
       "      <td>3.3475</td>\n",
       "      <td>-1.1906</td>\n",
       "      <td>-2.9053</td>\n",
       "      <td>0.6462</td>\n",
       "      <td>2.6135</td>\n",
       "      <td>1.8019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>2.6819</td>\n",
       "      <td>0.9365</td>\n",
       "      <td>10.7596</td>\n",
       "      <td>11.8815</td>\n",
       "      <td>1.6224</td>\n",
       "      <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",
       "</div>"
      ],
      "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",
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       "957 -1.7307  2.8189   6.5509   4.1347  4.1093   7.8311  0.8544  1.7680   \n",
       "958  1.1564  0.4062   9.6408   6.2806  6.8070  10.6484  3.1349  2.5027   \n",
       "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kt_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6d328ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score_test = np.matmul(Kt_c, eigvct.iloc[:,:nComponent])\n",
    "score_test = Kt_c@eigvct.iloc[:,:nComponent]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "108d1a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_test = round(score_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "61b4e290",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.3607</td>\n",
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       "      <td>-0.3410</td>\n",
       "      <td>0.7862</td>\n",
       "      <td>0.2145</td>\n",
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       "      <th>959</th>\n",
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       "      <td>0.4048</td>\n",
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       "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",
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       "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "76e6d5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = score_test@invegvldiag@score_test.T\n",
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a9b6bbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test = round(T2_test, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3c470906",
   "metadata": {},
   "outputs": [
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       "      <td>31.2285</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2117</td>\n",
       "      <td>2.7787</td>\n",
       "      <td>-6.2690</td>\n",
       "      <td>-9.0105</td>\n",
       "      <td>10.4727</td>\n",
       "      <td>-6.3687</td>\n",
       "      <td>-10.2930</td>\n",
       "      <td>-20.4671</td>\n",
       "      <td>-14.3777</td>\n",
       "      <td>0.3874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.5622</td>\n",
       "      <td>44.1408</td>\n",
       "      <td>24.1723</td>\n",
       "      <td>33.0329</td>\n",
       "      <td>32.2888</td>\n",
       "      <td>18.2509</td>\n",
       "      <td>38.1416</td>\n",
       "      <td>31.4382</td>\n",
       "      <td>19.7156</td>\n",
       "      <td>31.2420</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.1236</td>\n",
       "      <td>-4.8017</td>\n",
       "      <td>-3.8255</td>\n",
       "      <td>-7.3047</td>\n",
       "      <td>3.5923</td>\n",
       "      <td>-5.2065</td>\n",
       "      <td>-8.3361</td>\n",
       "      <td>-11.1559</td>\n",
       "      <td>-8.6616</td>\n",
       "      <td>-9.2463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0911</td>\n",
       "      <td>24.1723</td>\n",
       "      <td>54.5690</td>\n",
       "      <td>33.6718</td>\n",
       "      <td>37.7798</td>\n",
       "      <td>52.6992</td>\n",
       "      <td>38.5817</td>\n",
       "      <td>36.2571</td>\n",
       "      <td>51.2329</td>\n",
       "      <td>36.7648</td>\n",
       "      <td>...</td>\n",
       "      <td>4.9368</td>\n",
       "      <td>3.6532</td>\n",
       "      <td>-14.9384</td>\n",
       "      <td>15.6086</td>\n",
       "      <td>-7.3106</td>\n",
       "      <td>-3.2213</td>\n",
       "      <td>-10.8904</td>\n",
       "      <td>3.4600</td>\n",
       "      <td>5.5811</td>\n",
       "      <td>-6.5231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.9764</td>\n",
       "      <td>33.0329</td>\n",
       "      <td>33.6718</td>\n",
       "      <td>38.9320</td>\n",
       "      <td>29.5791</td>\n",
       "      <td>31.8670</td>\n",
       "      <td>39.0334</td>\n",
       "      <td>31.6649</td>\n",
       "      <td>26.8640</td>\n",
       "      <td>29.4202</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6656</td>\n",
       "      <td>8.3199</td>\n",
       "      <td>-0.6634</td>\n",
       "      <td>2.0328</td>\n",
       "      <td>2.6901</td>\n",
       "      <td>-1.5802</td>\n",
       "      <td>-10.7621</td>\n",
       "      <td>-10.9529</td>\n",
       "      <td>-4.5145</td>\n",
       "      <td>-4.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.9388</td>\n",
       "      <td>32.2888</td>\n",
       "      <td>37.7798</td>\n",
       "      <td>29.5791</td>\n",
       "      <td>46.2146</td>\n",
       "      <td>47.2491</td>\n",
       "      <td>36.0418</td>\n",
       "      <td>37.0339</td>\n",
       "      <td>38.2983</td>\n",
       "      <td>35.1386</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.2494</td>\n",
       "      <td>-8.0904</td>\n",
       "      <td>-13.0816</td>\n",
       "      <td>-3.3369</td>\n",
       "      <td>-4.3546</td>\n",
       "      <td>-1.3246</td>\n",
       "      <td>-7.0129</td>\n",
       "      <td>-1.1959</td>\n",
       "      <td>-2.1227</td>\n",
       "      <td>-4.0005</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>-6.3687</td>\n",
       "      <td>-5.2065</td>\n",
       "      <td>-3.2213</td>\n",
       "      <td>-1.5802</td>\n",
       "      <td>-1.3246</td>\n",
       "      <td>2.2256</td>\n",
       "      <td>-3.4014</td>\n",
       "      <td>-7.0965</td>\n",
       "      <td>1.9118</td>\n",
       "      <td>-6.2862</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4704</td>\n",
       "      <td>7.4804</td>\n",
       "      <td>11.6476</td>\n",
       "      <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",
       "      <td>8.9347</td>\n",
       "      <td>4.3955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>-10.2930</td>\n",
       "      <td>-8.3361</td>\n",
       "      <td>-10.8904</td>\n",
       "      <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",
       "      <td>...</td>\n",
       "      <td>-0.9606</td>\n",
       "      <td>0.8702</td>\n",
       "      <td>8.2960</td>\n",
       "      <td>-8.0097</td>\n",
       "      <td>9.4641</td>\n",
       "      <td>6.4568</td>\n",
       "      <td>22.1056</td>\n",
       "      <td>12.5599</td>\n",
       "      <td>2.1548</td>\n",
       "      <td>7.1952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>-20.4671</td>\n",
       "      <td>-11.1559</td>\n",
       "      <td>3.4600</td>\n",
       "      <td>-10.9529</td>\n",
       "      <td>-1.1959</td>\n",
       "      <td>7.6370</td>\n",
       "      <td>-8.2233</td>\n",
       "      <td>-11.2499</td>\n",
       "      <td>12.5971</td>\n",
       "      <td>-7.3740</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.3549</td>\n",
       "      <td>2.0517</td>\n",
       "      <td>3.3167</td>\n",
       "      <td>4.8509</td>\n",
       "      <td>-1.7789</td>\n",
       "      <td>9.6505</td>\n",
       "      <td>12.5599</td>\n",
       "      <td>26.6071</td>\n",
       "      <td>17.4038</td>\n",
       "      <td>1.4517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>-14.3777</td>\n",
       "      <td>-8.6616</td>\n",
       "      <td>5.5811</td>\n",
       "      <td>-4.5145</td>\n",
       "      <td>-2.1227</td>\n",
       "      <td>4.4683</td>\n",
       "      <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",
       "      <td>6.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>0.3874</td>\n",
       "      <td>-9.2463</td>\n",
       "      <td>-6.5231</td>\n",
       "      <td>-4.6875</td>\n",
       "      <td>-4.0005</td>\n",
       "      <td>0.0996</td>\n",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 960 columns</p>\n",
       "</div>"
      ],
      "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",
       "955  -6.3687  -5.2065  -3.2213  -1.5802  -1.3246   2.2256  -3.4014  -7.0965   \n",
       "956 -10.2930  -8.3361 -10.8904 -10.7621  -7.0129  -6.2673  -5.1080  -9.7680   \n",
       "957 -20.4671 -11.1559   3.4600 -10.9529  -1.1959   7.6370  -8.2233 -11.2499   \n",
       "958 -14.3777  -8.6616   5.5811  -4.5145  -2.1227   4.4683  -8.5740 -15.9390   \n",
       "959   0.3874  -9.2463  -6.5231  -4.6875  -4.0005   0.0996  -6.4660  -5.7386   \n",
       "\n",
       "         8        9    ...     950      951      952      953      954  \\\n",
       "0    13.0264  31.2285  ...  3.2117   2.7787  -6.2690  -9.0105  10.4727   \n",
       "1    19.7156  31.2420  ... -9.1236  -4.8017  -3.8255  -7.3047   3.5923   \n",
       "2    51.2329  36.7648  ...  4.9368   3.6532 -14.9384  15.6086  -7.3106   \n",
       "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",
       "955   1.9118  -6.2862  ...  2.4704   7.4804  11.6476   0.3995   1.7287   \n",
       "956  -2.5468 -11.2211  ... -0.9606   0.8702   8.2960  -8.0097   9.4641   \n",
       "957  12.5971  -7.3740  ... -2.3549   2.0517   3.3167   4.8509  -1.7789   \n",
       "958  10.2292  -2.1435  ...  4.2621  10.9146   7.1344  15.1896  -3.9533   \n",
       "959  -4.2846  -3.9078  ...  9.6306   8.2688   4.8996   1.5226   6.5948   \n",
       "\n",
       "         955      956      957      958      959  \n",
       "0    -6.3687 -10.2930 -20.4671 -14.3777   0.3874  \n",
       "1    -5.2065  -8.3361 -11.1559  -8.6616  -9.2463  \n",
       "2    -3.2213 -10.8904   3.4600   5.5811  -6.5231  \n",
       "3    -1.5802 -10.7621 -10.9529  -4.5145  -4.6875  \n",
       "4    -1.3246  -7.0129  -1.1959  -2.1227  -4.0005  \n",
       "..       ...      ...      ...      ...      ...  \n",
       "955  10.3191   6.4568   9.6505   8.9347   4.3955  \n",
       "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7a571ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_test_final = np.diag(T2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4136322d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  50.5085,   44.1408,   54.569 ,   38.932 ,   46.2146,   72.6059,\n",
       "         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",
       "        331.2398,  318.0227,  367.2337,  304.6193,  276.3634,  272.0779,\n",
       "        243.8567,  246.1702,  341.4896,  303.6941,  375.7683,  373.9432,\n",
       "        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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dc261985",
   "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": 76,
   "id": "1c48ae35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lLSRg1T5ky3BSFKW4UXEoYhzLwe+x2mqZ35cxyOzs5/MJVXbBXFsWyyEaT+/8qjEHRSltVByKmHjcIJJcwewQ9EvGOgTnht8nQpUdT0hdAtSNs061G79PxUFRShkVhyImGjeeVgNYbqVwJssh7rijLAtjSHUZr3+ceUnWjkg8yaUElrCoNihK6aLiUMTE7CpnL8ryijlYRXAnTRzKluaOjK/TFo5RGUzOiAr4RVt2K0oJo+JQxMRimcUhkMWt5NzxO9XRtZVBWrMEpNvC0UQ6q4NPJOvqcYqiFDcqDkVMzGR2KwWzBKSd+gQnxlxdHqA1FCXTkt6toVhaIZzfl3vdaUVRihcVhyImFjf4/ZnEQQjncivZVkdNeYBIzGSsi2gPRxP1EA75Zis1tYU58/Y3WbVtd865iqL0H1QciphYloB00O/L6FaKuWIOYIkDdFZcuzHG0BaJUZ0akHZafecQiJnLtrFs8y7+MmtV1nmKovQvVByKmFg8S8zBl9mt5E5lhU5xeGrRprS5HZE4xkBlilvJSW3NVSXtrDT3xMKNPPdB+vEVRemfqDgUMdnEIRjI7VZy1gj63KEjAGjpiKTNDduuptTV5nx5LBIEye2+v//AwqxzFUXpP6g4FDG5UlkzupXsu/1EtlJFkIqgz7P5npOumvo6/jzWgQCI6mpxilKUqDgUMbncSnHjffFOdSsBVAb9ni00Ogvm0iukIXfGUrYUWUVR+i8qDkVMLrcS4FkIl+pWAmvNBk9xyND51RGWXAHp9z5pzLpdUZT+iYpDEZMtWylbQz3HGkiyHMr8tEfS7/KjGTq/Bvz5uZWWbtqVdbuiKP0TFYciJpvlUG1nIHnVF8S74FYK+L0th5wxB23ApChFiYpDEZNNHAbYq8Nd+Nd307Ztbm4HoK4qmBirLPNnCEg7lkPyVyXfmEM4Q2Gdoij9m26Jg4jUichjIrJcRJaJyBQRGSwir4jISvv3IHuuiMgfRWSViHwgIkf3zFvYe8mWreRYDl58UN8MwFH7DkqMVQT9dHhcyBMBaelatlIoyzrWiqL0X7prOdwOvGSMORg4AlgGXA3MNMZMBGbazwHOBCbaP5cDd3bztfd6cmUrZaIjEkOEpKrnoM+7UV+mVNbOCuns55jakkPXgFCU4qDL4iAiA4GTgbsBjDFhY0wTcA5wrz3tXuBc+/E5wD+NxRygTkRGdvX1lVztM7zHAcKxOEG/L1HnAFZMwevC7Vz80xf7sc8hh1spVRw6ImpJKEox0B3LYQLQAPxDRBaKyF0iUg2MMMZstudsAUbYj0cDG1z719tjSYjI5SIyT0TmNTRkXoBGyW45HDPOchlNHF6Tti0cjVPuT/7orXYbWSyH1GVCbcskU6Gdsy0WN0waWZsYa1dxUJSioDviEACOBu40xhwFtNLpQgLAWD2gC/IjGGNmGGMmG2MmDxs2rBunV/pkEwcR4bRDRhDwp3/E4WicYEo7DGvxnvSPKpGtlPI6Ffb+mTq5uredc+Qovn7cvgCeQW9FUfof3RGHeqDeGDPXfv4YllhsddxF9u9t9vaNwFjX/mPsMaWLhGNxygKZP8LygI+wR0A4EotT5k/PPvJqdRHNEJB21nfwSn91CLn6Mh233xB7TMVBUYqBLouDMWYLsEFEDrKHpgFLgWeAi+2xi4Gn7cfPAN+ys5aOB5pd7ielC4Sj6Rd5N2UBn2fzPctySL7YB30+z2U/M7XPcNaUbsvSHsMRgvKgn8osRXmKovQ/Muc75sd/Af8SkTJgDXApluA8IiKXAeuAC+25LwBnAauANnuu0g3C0eyWQ5nf51lnEImZdMshQ0A6UxGcs/hPNjdRR6TTcqi16y6a29M7vyqK0v/oljgYYxYBkz02TfOYa4AruvN6SjKhaJzygD/j9rKAtziEonHKUvYL+sRz/YdYhiK4fCwBZ/Gg6vIAw2srANi2K5RxvqIo/QetkC5iQrkshwziYMUcUnslebf4jmYISCcshyzZR4441JQHGD6gHICtLR0Z5yuK0n9QcShiwtFY2iI8brLFHFJFJeDLlK3kXQRXmYdbyWnXXV0eoLo8QHnAR1ObupUUpRhQcShicmUrBf3WUqEmpVAtYhfBucmUyhrNEJB23FnZso92h6xtNeXW3LKAdy2Foij9DxWHIiZXtlIgsZRnyn4e4uD3+YjF04Uk52I/Wa71ba6YA1gB8s1NHWxsas+8k6Io/QIVhyIlGosTN2S1HJwLeGqKaigSpyKYvF8wMddbHNIX+7G3Z2mfsdsWB6cmIuj38dJHWzjhptcy7qMoSv9AxaFIcWIJ2WIOgcTdffIFvCMaoyKYnK3kVFKnznVST2tSuryKCH6fJGISXjhFcE5mk1cdhaIo/RMVhyLFyULKz3JIEYdIjIqUVFZHSFJjAusb26gq8zO4uiz9+CJZ3Uohu/ur0wRQaxwUpXhQcShS8hGHhOUQSxWHdLeSU+SW2kKjfmc7YwZVJnVwdfD5Otej9sKqw+js/upVR6EoSv9ExaFIcVw22QLSfnubp+WQ6lZyLIcU109TW9jTagDHcsglDpmL9BRF6b+oOBQpoUIsB9cF3BhjXbRTxGFIjVWk1tCSXMHc3B5hYGUQL3y+XOKQvQ5DUZT+i/7nFinhaO6AtFe2UijDfvsOrgJgQ2Nb0ng2cfD7JKdbKZt4KYrSf9H/3CLFyVYq1HJwVmJLdSvtM9DufVSA5ZCfW0m/YopSjOh/bpHSaTlk9ul7ZSs5nVJTA9KOyLh7MUVjcToicWrKM1sOWcUhkjnmkG0FOUVR+h4VhyIlv2yl9NoFJ1U1NZAd9KUHr53HqWs/OOQUh2iM8qD3+WnmkqL0b1QcipRwzHIPZc1W8qWnpzri4NVbyZrrshwyVEc7+ESyVkiHsrT38OoWqyhK/0HFoUgpqM7BwxpIXbzHmRt2CYlTH5G6loOD3yfEs1gOqd1fH/jOcRwxZqD9OioOitKfUXEoUvJJZfX707OVHFFJtRxEhKBfUiwHZ24Wt1IW71A0ntzg7zMHDOVrx+1rnYeKg6L0a1QcipR8iuCyWQ5eF/yAL7mldqZ23Q4+IavlEI2ZNJeUV+BbUZT+h4pDkZLIVsoQ8AXvbCXHMgh4uIqC/uSlQnPFHPw+ydpMLxo3ae4rx5LQdR0UpX+j4lCkZKpXcOOdreQdcwDrwu2+2F/14MKk46RirQGR+RyjsXjavo6lo5aDovRvVByKFMetlNpd1Y2n5RD3jjk4Y5Fo59x563YC3kJiHT97471ILN1ycNxKIRUHRenXqDgUKR2RGD7JHCwGd8zBXdiW2VUU8Eta4z1rbgbLIUeFdCzuEXNQt5KiFAUqDkVKRyRGecDv2UrbwYlHtIc7L8SZ6hycMa/itIwB6Ry9laLxeGIRIQcNSCtKcaDiUKR4rcmQyvABVr+krbs6EmOd2UreAWknYO1eSzpjQDqH5RCJmcTyow4qDopSHKg4FCleazKkMqgqSFnAlyQOjuXgFUewUlmti737op8p5pCrZXcsbtIK6DRbSVGKAxWHIqUjGs8pDiLC0Ooytu8OJ8aci38wYyqrddF2B7EzxRwCOcQhEounxUQSloOKg6L0a1QcihQr5pD74wsGktNTo1ksh9rKIPU72zDGJMUSMsUcrArpbDEHj2wlv2YrKUox0G1xEBG/iCwUkefs5xNEZK6IrBKRh0WkzB4vt5+vsreP7+5r783s2B2iNsM6C24CPkluvJehtxLASROHsrqhlaa2SH5upSwxB2OMp1vJsRzUraQo/ZuesByuApa5nv8OuNUYcwCwE7jMHr8M2GmP32rPU7pANBZnyaZdfGr0wJxzA75kyyGSpe3GiForgL2jNYw7ozXThbymPMDuUNT7HJ3Ad4ZUVg1IK0r/plviICJjgC8Ad9nPBTgVeMyeci9wrv34HPs59vZpki0PU8lIeyRGOBpPrN6WjYA/2XLY2RbGJzCgIt3qGFxdBkBjazjJXdQejnkeu64qSFNbxHNbop4iRYSc9Fp1KylK/6a7lsNtwM8A5z99CNBkjHFuJ+uB0fbj0cAGAHt7sz1fKZBM60B7EfD7Eq4kgIaWEENqyj3jCI447NgdSnIX+TJo+KCqMprawp7N9zJ1dHUqup32H4qi9E+6LA4icjawzRgzvwfPBxG5XETmici8hoaGnjx0yRDKY4lQByujqPMufVtLiOEDyj3n1trWREtHNCEOB46o4ZQDh3nOr6sKEjfW/FSiibUgksXB5xPKAj7aVRwUpV/THcvhBOBLIrIWeAjLnXQ7UCciAXvOGGCj/XgjMBbA3j4Q2JF6UGPMDGPMZGPM5GHDvC9Kezv5LPTjEPAld1rd1tKRURwSLp9YPOFWuuzECfgyZCsNqLA+5pZQumsp23oTlUE/oYi6lRSlP9NlcTDGTDfGjDHGjAe+CrxmjPk6MAs43552MfC0/fgZ+zn29teMyZIHqWQkFLXuuvNzKyVnFG3bFWJYJnGwLZFQJJZwFWVyKUFnQVvUo+XGFrvwzqnSdlMR9GWMYyiK0j/ojTqHnwM/EpFVWDGFu+3xu4Eh9viPgKt74bX3Cpy77vwsB1+ituHjrS1sawkl3EeplLsK1GI5FvoBlzh4NOvb3NQOwKi6dHGoDPrpiKo4KEp/JpB7Sm6MMbOB2fbjNcCxHnM6gAt64vX2dpzq4nxjDjtaw4SiMd5auR2AUXWVnnMTBWqRTrdSdnGw152OplsODbtDQCbLwa+Wg6L0c7RCughxLIdsq8A5BPxC/c52Lv77e4kL/blHjfac6/MJZX4f4Vi8MLeSh+XgXPyrytIFrCLop0NTWRWlX6PiUISEY9aFN9v60Q5OncGcNY2udt2ZL/hlAR/bW0L8+31WElqmjqzuY3sVyXXYAubV/6mmPEBzu3d9hKIo/QMVhyKkEMvB77rzdzKIvNp1O5QHfDw6v54121sBMmYqWcfJ7FbqiMYI+sXTLXXA8Bo+3tKStWmfoih9i4pDEVJInYO7gZ5zh5/N4kjNgPJ30a3UEYllXMJ0/2HVtEdi7GgNZT5xRVH6FBWHIqSQOgf33fkzizcB2a2B1GPmk62Uya1UnqGleHW5lQfRFtKgtKL0V1QcipBC6hzcBXBrGlpzzk+1RrIJiROP8FpaNBSJUVnmfX6OOGRq2qcoSt+j4lCEFNJbKebh8slGahwjm1spW/vtjmhmt1J1mSUOd8xaVdC5KYqy51BxKEKytaZIJVvw2Yu0mEM+RXAeloO1xnUmt5I1/uKSLUnj63e0sWrb7oLOV1GU3qFHiuCUPUsoy5oMqWRqlZGJQmIOjlvJa8nPtnCUygziUOlR+wBw8i2zAFh70xfyOldFUXoPtRyKkHA0TlnARz7LYZw0sbDmhakxh2z6k8mttLGpncUbmhON+VJx3ErZCEfj/PX11bR0aD2EovQFKg5FSCia3/rRAGcctg9HjBmYl5UB6W4lr0WBHBzLIdWtdMJNr9EeiSUCz6mMHVzF6LpKRmVZrGjB+p3c+OJyrnpoUV7nrShKz6LiUISEovG8ahwcJo4Y4On68SLVrTS0JrNbKphjPeiaDJYDWOtVR7MUwTntO9Y0aAxCUfoCFYciJByN5205AFS7fPxD7NXeMpF63LrKzJZD0OeIg/dFPpvTK1fzvVCeYqYoSu+g4lCEhAoUh1rXBf435x6WdW4hdQ5O+4xMlkOm9aXBasiXbTW4iB1012XGFaVvUHEoQtrDsYxpol64A8PZso8gv/RY97FESKwX4bBPrRVLuOykCRn3rSrzE42bREFfKvm6wRRF6R1UHIqQ3aEINRmCvV64F/fJ1pEVChMHESHo8xFOcStF43G+fty+HL3voIz7OrGMhhbv/kqONfLJ9lZWbm3J+5wURekZVByKkN2haNZgbyput1LAl/0jd0vH6z+dmvPYQb+kWQ6tocyZSg4jbOtixZYWz9hD2LXew+wVDTnPQ1GUnkXFoQjJ5+LrZv9hNYnH2dZnABjoEpJ9B1flPHbA70uKORhjaI/kdnsNr7Ush8vunce5d7ydtt1tjWSLeyiK0juoOBQhLR3RgtxKB+0zIPE4kKPe4fAxAxOP8wkGB/3JbiUnPTVXwNxdCLdiawvGJLumIi7LIZegKYrS86g4FCGtoSg15fkHpN0EcsQcPrP/0IKOl+pWcqyIXBf01NiGu+Zh7fbWpIC0Wg6KsudRcSgy4nHLbVOZRwsKL/K5C//l2ZOYOLwm5zywLAe3WylirwqXq+Ff6vY2V9xh6u9n8/j8+sTzbJ1hFUXpHbTxXpHhNN2ryGOJUC9yBaQBvn3iBL59YuY0VDdBvxBx3fVH7BbhwRxupdR2Hqk9lFa6urMW2FhWUZQeQP/tigynLiDTWgmZqLKrpKsydETtKkG/Lyk+4FgRwQLdSi0dmRf+0ZIHRdnzqOVQZHREHMuhsIv8Ted9io82NTNuSO4MpEII+n1J8YLlm1sS49n3SxaPT7ZnXqUuUwW2oii9h1oORUZHJP8lQt186YhRTD/zkB5vRxHwS9LF+9J73gdyu5VSK7W/968FaXO+8KmRQHLNg6IoewYVhyKjw3ErFWg59BZlfh+hSPrFO5dbKR+R+v5nDwC0lYai9AUqDkVGKNK9gHRPU10eoC2SHi8odHnSuqr07q9OB1l1KynKnqfLVxgRGSsis0RkqYh8JCJX2eODReQVEVlp/x5kj4uI/FFEVonIByJydE+9ib2JTrdS/7AcqssDtIbS21/kavCXSlNbJKk6Gzrbfjz/weac+xtjeGDuetrCmQPbiqLkT3duP6PAj40xk4DjgStEZBJwNTDTGDMRmGk/BzgTmGj/XA7c2Y3X3mvp6GYqa09TXeanNWRdkN0dVrtyt3/Y6NrE46E1ZYm4ijutNRNz1jRyzZMf8qtnlhb8uoqipNPlbCVjzGZgs/24RUSWAaOBc4Cp9rR7gdnAz+3xfxqrT8IcEakTkZH2cZQ8abMvxJU9nJLaVSzLwTqnu978JDGeaQGgbNSUB1h70xeIxOIIVlzitEOG8/7anTn3dUIY2bKeFEXJnx65/RSR8cBRwFxghOuCvwUYYT8eDWxw7VZvjykF0LDbanE9bEDm5Tv3JNVlftoiMeJxk+TSicYLtxwcV1nQ70v0gBpVV0k+CVZO3cSujswLDCmKkj/dFgcRqQEeB35gjNnl3mZbCQXdQorI5SIyT0TmNTRoq+ZUtu0K4RMYUt1PxKE8gDHQHonhc13F66qyL0cKVnqtG6+1JMr8vrxSWaO2pbJ8i1Vn0dIRIZZljWpFUbLTLXEQkSCWMPzLGPOEPbxVREba20cC2+zxjcBY1+5j7LEkjDEzjDGTjTGThw0b1p3TK0m27w4xpKa84IBvb+G0Dm8NR5PWgjh5Yu4Gfr8+59CkOINX7UZZwJdX/MI9pz0c4/Dr/49fPftRzv0URfGmO9lKAtwNLDPG/I9r0zPAxfbji4GnXePfsrOWjgeaNd5QOO2RWI+3wOgO1XZ32NZQLKl2IZ86hrqqMv5xybGJ517pr1ZjP0M8hxXgFoeFG6wYRT5ZToqieNOd9hknAN8EPhSRRfbYNcBNwCMichmwDrjQ3vYCcBawCmgDLu3Ga++1RGLxgmsIehNnXYbWUDTJrZQv7gZ8H25sTt9uWxPhWJwKn7coxuKGjU3tiefOynH79nCrEEXZm+hOttJbJK8q6Waax3wDXNHV11MswlGT1tG0L0m4lUJRuuLpCgY6d7py2sS07Y6rKRKLZ6wKv/ml5fz1jTWJ5y8t2QLAuDxWslMUxZv+c5VR8iIci+fsW7QnccShLRzr0qI8bivoqH3rMm7PFpSetWJb0vPNzZYV0R5JL85TFCU/+s9VpkTZ0NjGuXe8zQ47BbW7RKJxyvuR5eCsSLc7FM0r5TQV9+JDNR4LGHWmqGaufK5MsSicGgv3AkK9zYotLSzf0pmst3hDE08vSsu3UJSiof9cZUqUGW+sYdGGJp5ZvKlHjmdZDv0jUwlgkJ2yuqW5o0v7uwPXXpaHs7T0jx5ZlPEYmQoC2/egOHz+tjc447Y3E8/PueNtrnpo0R57fUXpaVQcehnHZx7qobbTkVi8X8UchtSUM2ZQJYvqmxK1Bj1J/c42ABaub/LcboxhzppGz227Q9pnSVG6Sv+5ypQoTqVvc3vPVO6Go/0rWwksgWjpiBK100mvPPWAgvafdvBwpp95sOe2Y8YNAuDYCYM9t9fvbPccB6sg7t/+8nZB59LTuPtNKUoxoSvB9TKOKLyzanuPHC8ci3tWEvcl5QEf4WiMcMxQFvDxo9MPKmj/uy/5dMZt0w4ZwYCKAAcMrynomAPKA7SEohktjt4iHjdJ7rGWjijlNf2nLkVR8qV/XWVKkI+3Wu0clm1pSdxZd4f+5lYCSxxC0TjRWDznIj9dobYimGhVnop7idKxgysZZK8LMax2z7UXMabzHFpSXFm7eshiVJQ9Tf+6ypQYsbhh6aZdDKgIEI7GWd/Y1q3jGWPY0Nje7ywHp/9RNG4SbrSepDzoyxizcbtt3vzZqUzZfwgAo+sqE+Pui3dv8Oi8+sTjprZwkpC1ZMmyUpT+TP+6ypQYqxt20x6JceIBVp+hU//wOj9+ZHHOVhCZcAKvjy+ozzFzz1IWsMShfmdbr3SLrQj4PZciBdLGxw2pBjpXkQNo7eWspaWbO1NYv/a3uRz8i5cSz1UclGJFxaEX2WBbCoeNHpgYe3xBfZctiO12rcQ3jx/f7XPrScoCPsKxOMs2t3DoqNrcOxSIZTkkX+AfnbeB7/5zXpq76djxVuD6lIM6mzY2tYWzHt8Yw1dnvMs9b3+SdV4mnP5SQFIbD+idFuIf1jdz7h1vc+srH+fVsVZRuoIGpHuRHbuti9LYlDYOm5s7GD+0uuDjOReay0/er/sn14M4bqW2cCxR99CTODENN9c98xFt4RivLN2aNP7Zg4fz3jXTGFRdxg8fXgxYS5COGZT5+B9v3c2cNY3MWdPIJSdMKPj8drZlFoCWXhCH/7h/Phub2lm0oYlDR9Vy+qH79PhrKIpaDr3IjlZbHAZVJo13dbWyJvsiVFcVzDFzz+LEBELRmGfb7W4fP+BPE4dMfZYAhtdWEPT7eOTfpwCdfzeHcDTOL55akqho3uS62/94awsf1DcVdH5rGnYzdnCl57Zd7VGeXFjPkwt7zhXobte+raVnKu+VwmkPx5i5bGvuiUWKikOBdERied8Nbmlup6rMn7Ywz6INuZe99OKWl1cA2S+MfUGZ3084GicUjfeKOAR8wuINTby0xGrBvXzLLhpbs7uKoFNEm9qT5z67eBP3zVnHve+sBZJdP6ff+gZf+vPbjL/6+aSV7TIRisaYs6aRKfsN4R8eKbnN7RF++PDihBXTE7gXMeqp+hmlcK5/5iMuu3cey1wxp1JC3UoFcurvZ7OpuYO1N30h67zmtghPLtzIp8cPTrR3CPiEYycMZuW23V1+/dQ+Qv2BsoCP1nAUY7xXc+suTgO9/35hGdtaQvzy6fRFfB66/Pi0sbpKSxxS3T5O7KYyaH39b35phefrNraGqfLo9+SwfkcbEXs51MPH1DGgInluRdDHZldbkY5IrEeEffSgykRsY2ceIqn0DqsbrP/jUk06UHEokE05egjNWbODXz+7lPKgj10dUS6cPDaxOM+wAeXsO7gqzU+eD/G4QQS+e1LhPvHepizgS/RActaB7kmcivANje2ewgB4BsIH2pZDc0pAutF+HvQLkVg8LYjs4BXs3dzczuwVDXz102M5+ZZZVAStc6utCFBb2enum/HNY5jxxho2NnUmH6zcarmftu8OF1zU52Zna5gzDt2HD+qb8rKgioW5a3ZQXR5ISuDozzj2W1caThYD6lbKwssfbWHyb1/JWIDlxa+eXcrSzbsSlbkThlZTXR7gF2dP4qHLj2fiiAHsaA0nMpnyxbkzH1DRv+IN0NmZFaz4Q09z3RcnZd1+17cme/5dygN+qsr8aTGHRjtRoCMSyxr/cbq6rt3eyvtrG4nHDVNufI3pT3zIv+aut49hCUhtZZB9XYkHpx+6D2Ncd/gAK7a28IU/vsVp//N61veTi8bWMINryjhgxAA+2pSfSyObCPYXvjJjDmf/6a2M27e1dOTl6ttTOPUzmdKsix0Vhyz8+tmlbN8dZtuu/IN+AyuTjTHH733ZiRMYN6Saz9hFWgvWZ447vLRkMyvtymqH15Zbaxakui76A4NdMZXeiDnsN6wmqbV3KtmWTa2rDKa5lbbaQdz2SCzrBdMRh6m/n80F//sum5o751771JKkubUVQSqCfnwCn7XTaEcPqmRzU0eior2hJZR4vdUNu1nisfKdQzQW59qnPmT9juSbiFjcsLMtzJDqMiaNrGV1w+68ivxuenE5J9z0WsKlViy0h2MYY2hqC3Psf89M+7sXQjgaL+hGLxfOX90tWGu7mGzSHylpcVi5tYXXP25IGntn1XZueGGZ5/z56xo9C9TCMesLNW+td/dPN3WVyamcqamdIwdWANlbXP/H/Qv43K1vJI057Z+zpU32Fe6Cs95wK0Fym4xUyrP48euqytLqHFbYWUpPLdzED+y/6797pAe3hlNbYWS+a3VuClb+91n83Q5Mj66rIho3hO22KYs3NCXmT/vD61nvkj/c2Mz9c9bz40et83tr5XYeeX8DK7a0EDcwtKacIdVlROMmr+6z767eASRnZvVXLv/nPMCy7A755UtMmP4Cqxusi+5zi7u+LvhZf3wzqUCxuzhfSed78tKSLUz9/WxeLdBt/PSijazY0pJ74h6mpMXhc7e+wcV/fy/R0+iFDzfztbvmMuONNWzfHWLFlhZa7X+sWcu3cd6d73L/3HVpx3n94+3c+srHnP+/7ybGjDEsWL8z0TvJYURKT5/UtQZqbffHjS8u59ZXPmbumh2FvadJIwqavycYlCQOe/4rFcsiHMNry9nY1J64uwtH42y1LcFwLJ7I9rn0hAmcd/QY9qmtSOzbFkq+y0zNenLjfK5+nyTWqBiTksL80kdb8n1LiWOEo3GmP/EB37h7Lj97/APO+qO1ZsTEETWJmEpTW4QfPbyIs//0ZsbjORZsrhhFc3uES/7xXlKjyNeWb92jxXb/Z19c3X2pnIygihS35ebmdh6fX5+X9bSqG4kgXjiv6WSiLbZToN2LPuWiPRzjqocWcdHf5vToufUEJS0ODj977AMAvvevBYmxyb99lc/f9gZXPrgQ6GyBsNGjBfRvnlvK7TNXJo3d9OJyvvyXdzj91je49qkPExcZ99KUXm2o3R07b5+5kq/MmMNPH12MMYZILM78denuJudu7zsnTuhWILO3SLIceiHm4ObgfQYkHl96wnhOO2QEk8dlrnAbP6Sa5VtamHLja8TjxnMBoJu+fDj7DKzgDxceweyfTmWgHVhOTRN9bZnl2jvzsPSiM3cw2mH0IO/ah3yI2xeemDE8+N6GtO2TRtYmrNIrH1rIEws3smRj+kXpjlmrWLShKSEOl/zj/aztW+5+cw2zVzTwtbvmEo9bN0DfvmceN77obW0XSqbPwAu3RfSXWauA9AWh/jJrNT9+dDFvrsy/63G+vbbmr2vkO/e+z47dIaKxeGL52Wue/JBfPftRWlNF5ybU78v+PxCPm8RnsMwWkmw3OH1FyYqD+wv4xMKNGX3LM5dvY1dHJHEheGrRRi75x3tA9iwE94L2989ZzxG/+j/mr2tMWpoyU9riVFdrB4BH59dz5+urmfj/XuS8O99JjEdjcZZvaUm02+iPwWiAwS5xmDzee92F7vL8lSfy9BUnJC6IXzpiFNd98VDuunhy1rWrxw+xgsTN7RHaIjHaIukumK8eu2/icUXQz4JffI4yv491jcn+47vestprXHPWIcyZPo0HvnNcYpuXxeRu/udkU5139JikOVNvmcX4q5/n4F+8mHSB6LC/R16NfAdXl1FXVZboY+VuS+72qa/f0cYtL6/g3Dve5oUPO62WHVmsB/fKfJ/saGW3nabptpBfXbqVZ7OsbLiluYNH3k8XNIAbXljGIb98Kc0S2dyc/v/Z6rLcnCxBX8o/5Y5WywosxF3mFv2XlmxJpKSmcvvMVby6bBuPzq/n2qeWMOXG13hg7noemLuef7y9NpGmPMp2FTuuz7+9ucbzeA6n/mE2U26aCXSmwVZniZv1Ff0vutkDbGxq54SbXksaS33u5lPX/x9Bv/Wl27orxNZdDdz+6sqsC8l4cd6d73LaIcMTzzOtG/2bcw7jpJtnJY155drPX7czyV9e0w+D0ZAcEK7tJQE7dJSV3rjT/nvsPyw/C2qk6wJ9xb8WpMWg/vcbx6Tt4/cJ44ZU8dfX17C5KT02NLy2nPKAn30GdrqgxONOwn1z8I3jxxHwCWd/alRS48S1dsC5IxLnk+2tCcvQucnwusufYLdeOWxULSMHViTVUmxp7sDvE8YMquTkW2al7QtW1k+mBonuz/KD+qaEJetkZRlj+I4dE/jiEaMSc0PRGAGfD79PuOQf77F8SwufmzQiyeUI8OB7VpbX7lCUJ96t5/WPG/jdeZ/iMyn/n9c/8xGf92gL0tga5p63P+HsI0YxtKY8cZdeyKp/l907j29NGcfwARX8x/3zAbLWLd304vLE42ue/ND1nq2/ya6OKNt2dTDLThppbA3THo4lXMqxuCEUjSVqZta6kgycm1iv709fU5KWQ2sXloeMpCxxeeurH3fptZvbIxw2upaLp4zja8eN85wzZlBl1gwbh6/MSPZD9sdMJdizX+wtu6wL4WcPHpZjpsVElxvOLQzXfXESV556AJ8/1DuGc4LdSde99vdphwznrm9NTgq6HzdhcOLGwgsnU2mfgRVcMHkslWV+LjhmjOfcO2ev5tZXrO+d456MebhAxtnWUMDv46krTkhyXz7/4WZOunkWd7+VuYmgu+VGKBpLypxzB/5/+PBi7p+zPjHPGMOTCzemHe9Lf36Lg659KbHOt3MX73axPjB3PTPeWJ2487/68Q/47fPLeHPlds/11e95Z23G/+Prn13KhXb8z4lFfLK91TPNddGGJma8sTqpceP8dTsz+vkXrN/JL55aQjxu8qo+Hzekit2hKMfeMDPpon/ILzsD3z94eBGTfvkykL6ueWr21IbGtoLT3HuLkhSHiXn65Qfl0aPolAMzX4S+c2J6QdqiDU18Zv+h/Oqcw5LuLN2ICG/87LMZl8bMxKSRPd/xtKf46zeP4cnvfabXX8f55xo3OL/GhfsNq+G35x6WNn7QiAH86PSDMgrb96bun/T8qmkTueviT3NaSkLAA989nuW/OTPj6zsut6GudN8rp01MPB5SXcaCX3wOsDr23j5zJfG44RdPWymbHZEYdVVBTj5wGCdNtARrhCtoPqK2IqkDrdNiJdVCcrNtV6elcd3TH/Hlv7xD/c42QtEYu0NRz7ThtnCMXz27lB890tkGZN2OVmJxwwf1Vkru04s20dgaTqR4Ohf3XR0RrnnyQ254YXliMaT/c2X0uO/M3TgWihdrtreybkdrIsj8r7nrmfTLl9PcS+fe8TY3vLCcJxaki5qb8Vc/z0HXvshVDy3kvjnrWFTfREse4jBqYO64kuOCC0fjaTedjoCKWJ/1STfP4tQ/zM55zD1BSYqDiPDgd612Cld7XICdO/DvnJS9u+m1XziEe799LJ/ceBav/PBkBlQEePNnn+WCY8bw5aNG81+uf3KHSMxw9L51Oc9xaE05/37K/iy+7nSO328w//uNo7POf+HKk/p15ejnD92Ho/bN0vq0h/jnt4/lm8ePS2Tq5INzp+0mNYssleG1Fcz88SmJ59/PsC623ydJjfBSufG8wxk+oJz9hnWK2djBVbzyw5MZWBnk52ccnHaT8sB76xOFe/U722lqi3DkmIHccv4RXHbiBK74bPK5DKtJdxHt2B2mtiLAlP2GpG1btrmFjkiM/319NQ/ZsYETfzeLg659iTtnr/a0atc0tHKP3YvK4ZRbZvPHlESNo3/zSsKP3hKKsqmpnTvsYHI+ZKtnSeWUW2anjV3413fpiMTSgs7Tn/gwbW4qoWicTbYbcc6aHazZ3spBIwakzfvw+tNxTtPdrj2VjkgsKTbzuVtfZ8YbyfEItyXhCHuqF6OvkN5eJas7TB4wwMw7JsUnfOGF8L3vQVsbnHVW+k6XXGL9bN9O/LzzAcv0HD2okpryAJsvuoSab32dP987i+kP3cDuUJTG1nDibupvx/4bMw84jv121PPUh/9M96Ffey2cdhosWgQ/+AHvr20kFjccPLKW5Zt3cfPJF/OH2/6TCR8vhmuuST+/226DI4+EV1+F3/42adPcTxqZfvoVdOx3AIfMf53vvvckV++y/nlnH3mkNem++2DsWHj4YbjzzvTjP/YYDB0K99xj/aTywgtQVQV/+Qs88kj69tmzrd+//z0891zytspKePFF6/FvfgMzZyZvHzIEHn/cejx9Orz7bvL2MWPg/vutxz/4gfU3dHPggTBjhvX48svh4xTX3pFHWn8/gG98A+pTOp1OmQI33mg9Pu882GGlCe8ORVmysZm3xx3Bn064CIDl826nIpoSmD37bPjJT6zHU6cCsHZHKzXlQYbWlBX03eP889O3/+d/wle+Ahs2wDe/iQGcS+GcNTuSvns3vPznpF3HDalm5C2/TfruORis1hM3n3wxC8YcwtH1y/jZG/daf9IRAwjH4qzd3sqvp13Olv0O5pCP3uPKdx9Ou4Be8/nvs2bIGO4YUM/ov9+Z5vL44dk/ZnPtML629l2+9M7T6W/v3OnsrBrI+R++yvkfvpr4n0j8eS64no5gBd9Y8DxnL09Pu/3q124C4Ltzn2Da6vcS47WVQWLlFXzjvOtpbo/wX28/yAnrkhsZ7qys5T//zfp/+9nr93BO+3qG1JSzyHaZbR4wlB9+0fpsf/nqDCZtS75Irxk8mmvO+C8AbnjpT+zXaFkaB+0zgBVbWlg6fD9+fdrlAKytf5COtetZt6OVIdXlrNrWwoLRB3PzKZcAcOeTNzCofReHjhrI+sY2WjoiSd+9ex65jopoiOP2G8KGxjY2NbXzxoHH8bfjvkwkZnj84Ws4ct86/G6rtsDvnrz++nxjzOT0iflTkpaDg0+sn0NH1VJXGSTgE8YOrmJQdRm/+OIkAj6hrjLIYDsDJuha4vKbU8bllR105NhBHDZ6IAMrg4wZVMVXjx2bCBgWyuGjB/K9zx7AO9On8aeLjuLoLCmaSv7UlAcSwcALjhnDbV85Mu8GeOOHVFvC0Au475EPzuEyDGSJawhwvIeFANaFdURtBSNqK/jTRUcl3kumm8Ip+w3hrMNHcsjIWuo81ub46ecP4tdfOjTruTosL7Bb6es/ncrN530qaezw0QOZNLKWw0cP5IHvHue53/DadPftxqb2bndLra0MMqiqjMNGD2SfgRVcNW0iv7/gCAAqAj4OGjEgrdGku4XKR5uas3ZwXri+KeEGi8QMkZihqsxPJBbn/U8ak2I2fUH/thwmTzbz5mX2O/YU4Wic3z6/lO9NPYAtuzoYVVfB8AHe8YI9zVT7Dna2c0evdIkdu61urtd/6dBeWcq0u4SiMQ66Nrl6d1BVZ+uPx//zMxyT42bhjlmrEq4JgHOPHMVtXz0qac65d7zNIleltpuAT1h1Q/Jd6Zf/8jYLXKmy9112LCdNHMa8tY1JRaG5ePQ/prBPbQVPLtzIEWPrePmjLcTjhqvPPJgjf/0K0JkxtDsU5U+vreSHpx2YJuJt4ShPL9rE395cw5qGVmb9ZCqzV2zjV88uzftcAM44dJ+cRYk3ffnwpDRnL+ava+S8O62/w+i6Su7/znF89vezC34th0kjaxM1V9d9cRKXdmHxKQAR6bblsMfTX0TkDOB2wA/cZYy5aU+fQyplAR+/PscKWmYKIivFzZCacu74eva4Tl9SHvCz7+CqRE1LdZmft68+lcqgn8bWMEM84gqpXPHZAzhq37rEcT6z/9C0ORd/ZhyLHm5KGvvHJZ+msszPcRPSa1Tu/fax+H3CRTPmsLi+ORFXmjx+MIOry9Iqrn9w2kTufusTRg2sZMXWFo4YM5CrzzyEyeMGISKJYLw70eOx/5iSSJUFy9KbfuYhnu+xqizARcfuyxmH7sOyLbuYMLSaYQPG8mF9M4Ory7jrrU/Yp7YikdUG1kW2ujzAUWPruPfdtQypLueHnzuQ8Vc/7/kaR46tI24MF0we67ndjbNmOcBTV5zAsAHl3HPpp6kpDyTE8/994ZAkcbjxy4dnjIEcPHJAQhy609q/RzDG7LEfLEFYDewHlAGLgUmZ5h9zzDFmb+eUU04xp5xySl+fhrIH2NDYas66/Q2zuam9119r7pod5p1V282O3aG85je0dJjFG3YmjT23eJOZessss2Bdo7l/zlrT0NKR2Hb9M0vMuJ8/Z7Y09/57cfhgQ5MZ9/PnzLy1O8xPHllkZi3fat78uMHEYnHP+eN+/py57J73zNrtu017OGqeXFBvVm5tMfG49/xMzF/XaK5+fHHafnNWbzetoYgxxph3V283437+nBn38+fMCx9sMrvaw+bf7njL3PbKx2bppmbz/QcWmHE/f8489N66xLwZr6/u2h/CGAPMM928Xu9Rt5KITAGuN8Z83n4+3RaoG73m7ym3Un9G3UpKMdIRidHYGmZUXddbiHQFY0zedTfWRTC9JUdv0dQW5s7Zq/nx6QelxSo6IjFe/mgLXzpiFEs37+LAEQOSYqCF0hNupT0dkB4NuOvq6+2xBCJyuYjME5F5DQ2Zc7UVRem/VAT9e1wYoLCCTBHZY8IAVofg6Wcd4rlaYkXQzzlHjkZEOHTUwG4JQ0/R92eQgjFmhjFmsjFm8rBh+VXBKoqiKD3LnhaHjYA7yjPGHlMURVH6EXtaHN4HJorIBBEpA74KPLOHz0FRFEXJwR5NZTXGREXk+8DLWJlLfzfGeK8YryiKovQZe7zOwRjzAvDCnn5dRVEUJX/6XUBaURRF6XtUHBRFUZQ0VBwURVGUNPp14z0RaQDWdeMQQ4H8Vx4vLfS9773sze9/b37v0Pn+xxljulUo1q/FobuIyLzulpAXK/re9873Dnv3+9+b3zv07PtXt5KiKIqShoqDoiiKkkapi8OMvj6BPkTf+97L3vz+9+b3Dj34/ks65qAoiqJ0jVK3HBRFUZQuUJLiICJniMgKEVklIlf39fn0NCIyVkRmichSEflIRK6yxweLyCsistL+PcgeFxH5o/33+EBE+u96mQUgIn4RWSgiz9nPJ4jIXPt9Pmw3d0REyu3nq+zt4/v0xLuJiNSJyGMislxElonIlL3psxeRH9rf+yUi8qCIVJTqZy8ifxeRbSKyxDVW8GctIhfb81eKyMX5vHbJiYOI+IE7gDOBScBFIjKpb8+qx4kCPzbGTAKOB66w3+PVwExjzERgpv0crL/FRPvncuDOPX/KvcJVwDLX898BtxpjDgB2ApfZ45cBO+3xW+15xcztwEvGmIOBI7D+BnvFZy8io4ErgcnGmMOwGnh+ldL97O8BzkgZK+izFpHBwHXAccCxwHWOoGSlu+uM9rcfYArwsuv5dGB6X59XL7/np4HPASuAkfbYSGCF/fivwEWu+Yl5xfqDtRbITOBU4DlAsIp/AqnfA6wuwFPsxwF7nvT1e+ji+x4IfJJ6/nvLZ0/napKD7c/yOeDzpfzZA+OBJV39rIGLgL+6xpPmZfopOcuBPJYiLSVsM/koYC4wwhiz2d60BRhhPy7Fv8ltwM+AuP18CNBkjInaz93vMfH+7e3N9vxiZALQAPzDdqndJSLV7CWfvTFmI/B7YD2wGeuznM/e8dk7FPpZd+k7UIrisNcgIjXA48APjDG73NuMdYtQkqloInI2sM0YM7+vz6UPCABHA3caY44CWul0KwAl/9kPAs7BEslRQDXpbpe9ht78rEtRHPaKpUhFJIglDP8yxjxhD28VkZH29pHANnu81P4mJwBfEpG1wENYrqXbgToRcdYocb/HxPu3tw8EduzJE+5B6oF6Y8xc+/ljWGKxt3z2pwGfGGMajDER4Ams78Pe8Nk7FPpZd+k7UIriUPJLkYqIAHcDy4wx/+Pa9AzgZCJcjBWLcMa/ZWczHA80u8zSosMYM90YM8YYMx7r833NGPN1YBZwvj0t9f07f5fz7flFeWdtjNkCbBCRg+yhacBS9pLPHsuddLyIVNn/B877L/nP3kWhn/XLwOkiMsi2vE63x7LT18GWXgrgnAV8DKwG/l9fn08vvL8TsUzJD4BF9s9ZWL7UmcBK4FVgsD1fsDK4VgMfYmV69Pn76KG/xVTgOfvxfsB7wCrgUaDcHq+wn6+yt+/X1+fdzfd8JDDP/vyfAgbtTZ898CtgObAEuA8oL9XPHngQK7YSwbIaL+vKZw182/4brAIuzee1tUJaURRFSaMU3UqKoihKN1FxUBRFUdJQcVAURVHSUHFQFEVR0lBxUBRFUdJQcVAURVHSUHFQFEVR0lBxUBRFUdL4/z0fW2W+ZfTBAAAAAElFTkSuQmCC\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": 77,
   "id": "852672cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T2: 0.853125\n"
     ]
    }
   ],
   "source": [
    "print(f'T2: {(T2_test_final>T2_trlim).sum()/T2_test_final.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08173ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}