fait_dc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaptative Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.feature import hog\n",
    "import pandas as pd\n",
    "import math\n",
    "import glob\n",
    "import os\n",
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROI Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainset_extra():\n",
    "    ROI = []\n",
    "    filenames = []\n",
    "    trainset = pd.read_csv('trainset/trainset.csv')\n",
    "    for e in [r'trainset/*.png']:    \n",
    "        for filepath in glob.iglob(e): \n",
    "\n",
    "            filename= filepath.split(os.path.sep)[-1]\n",
    "            img = cv2.imread('trainset/'+filename)\n",
    "\n",
    "            f = filename.replace(\".png\",\"\")\n",
    "            idx = np.where(trainset[\"filename\"]==f)[0][0]\n",
    "            n_img = img[int(trainset[\" y_24\"][idx]-70):int(trainset[\" y_8\"][idx]+10),int(trainset[\" x_0\"][idx]):int(trainset[\" x_16\"][idx])]\n",
    "\n",
    "            filenames.append(filename)\n",
    "            ROI.append(n_img)\n",
    "    return filenames, ROI\n",
    "\n",
    "def testset_extra():\n",
    "    ROI = []\n",
    "    filenames = []\n",
    "    testset = pd.read_csv('testset/testset.csv')\n",
    "    for e in [r'testset/*.png']:    \n",
    "        for filepath in glob.iglob(e): \n",
    "\n",
    "            filename= filepath.split(os.path.sep)[-1]\n",
    "            img = cv2.imread('testset/'+filename)\n",
    "\n",
    "            f = filename.replace(\".png\",\"\")\n",
    "\n",
    "            idx = np.where(testset[\"filename\"]==f)[0][0]\n",
    "            n_img = img[int(testset[\" y_24\"][idx]-70):int(testset[\" y_8\"][idx]+10),int(testset[\" x_0\"][idx]):int(testset[\" x_16\"][idx])]\n",
    "\n",
    "            filenames.append(filename)\n",
    "            ROI.append(n_img)\n",
    "    return filenames, ROI\n",
    "\n",
    "def ROI_extraction(setT):\n",
    "#    ROI = []\n",
    "#    filenames = []\n",
    "    s = 0\n",
    "    if setT == \"trainset\":\n",
    "        s = 1\n",
    "        print(\"ROI extraction on trainset\")\n",
    "    elif setT == \"testset\":\n",
    "        s = 0\n",
    "        print(\"ROI extraction on testset\")\n",
    "    else : \n",
    "        print(\"parameter must be trainset or testset\")\n",
    "        \n",
    "    if s == 1 :\n",
    "        filenames, ROI = trainset_extra()\n",
    "    elif s == 0 :\n",
    "        filenames, ROI = testset_extra()\n",
    "        \n",
    "    return ROI, filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI extraction on trainset\n"
     ]
    }
   ],
   "source": [
    "ROI, filenames = ROI_extraction('trainset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI extraction on testset\n"
     ]
    }
   ],
   "source": [
    "ROITS, filenamesTS = ROI_extraction('testset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def divisorGenerator(n):\n",
    "    large_divisors = []\n",
    "    for i in range(1, int(math.sqrt(n) + 1)):\n",
    "        if n % i == 0:\n",
    "            yield i\n",
    "            if i*i != n:\n",
    "                large_divisors.append(n / i)\n",
    "    for divisor in reversed(large_divisors):\n",
    "        yield divisor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_extraction(ROI):\n",
    "    if not isinstance(ROI,list):\n",
    "        print(\"Roi type must be list of numpy.array images\") \n",
    "        return 0\n",
    "    fd, hog_img = [],[]\n",
    "    for e in range(len(ROI)):\n",
    "        #print(e)\n",
    "        # Calculate parameters \n",
    "        #alldiv = list(divisorGenerator(ROI[e].shape[0]))\n",
    "        #div = 1\n",
    "        #for i in range(len(alldiv)):\n",
    "        #    if alldiv[i]<3 :\n",
    "        #        div = alldiv[i]\n",
    "        #ppc=math.gcd(round(ROI[e].shape[0]/div),ROI[e].shape[1])\n",
    "\n",
    "        # Implementing HOG feature\n",
    "        #print(\"ppc\",ppc)\n",
    "        ppc = 10\n",
    "        a,b = hog(ROI[e], orientations=9, pixels_per_cell=(ppc,ppc),cells_per_block=(1, 1), visualize=True, multichannel=True,feature_vector=True)\n",
    "        fd.append(a)\n",
    "        hog_img.append(b)\n",
    "    return fd,hog_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b5d652ad88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "img = cv2.imread('trainset/S010_001_00000001.png')\n",
    "n_img = img[190:250,280:350]\n",
    "n_img.shape[1]\n",
    "a = [n_img,n_img]\n",
    "list(divisorGenerator(a[0].shape[0]))\n",
    "plt.imshow(n_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "378\n"
     ]
    },
    {
     "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": [
    "fd,hog_img = feature_extraction(a)\n",
    "plt.imshow(hog_img[1])\n",
    "print(len(fd[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 20s\n"
     ]
    }
   ],
   "source": [
    "%time _ = fd, hog_img = feature_extraction(ROI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b587fdd288>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.imshow(hog_img[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9450"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(map(len, fd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6417"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fd[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_size(feat_desc):\n",
    "    n_arr = []\n",
    "    tmp = []\n",
    "    for i in range(len(feat_desc)):\n",
    "        for j in range(max(map(len, feat_desc))):\n",
    "            if j >= len(feat_desc[i]):\n",
    "                tmp.append(np.nan)\n",
    "            else :\n",
    "                tmp.append(feat_desc[i][j])\n",
    "        n_arr.append(tmp)\n",
    "        tmp = []\n",
    "    return n_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd1 = normalize_size(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "722"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fd1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>9440</th>\n",
       "      <th>9441</th>\n",
       "      <th>9442</th>\n",
       "      <th>9443</th>\n",
       "      <th>9444</th>\n",
       "      <th>9445</th>\n",
       "      <th>9446</th>\n",
       "      <th>9447</th>\n",
       "      <th>9448</th>\n",
       "      <th>9449</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.284623</td>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.249077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.376627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "      <td>0.277548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.118614</td>\n",
       "      <td>0.348124</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.306595</td>\n",
       "      <td>0.249046</td>\n",
       "      <td>0.190619</td>\n",
       "      <td>0.528614</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.254788</td>\n",
       "      <td>0.200981</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.317778</td>\n",
       "      <td>0.064201</td>\n",
       "      <td>0.421881</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.438970</td>\n",
       "      <td>0.184419</td>\n",
       "      <td>0.085471</td>\n",
       "      <td>0.026047</td>\n",
       "      <td>0.519917</td>\n",
       "      <td>0.519917</td>\n",
       "      <td>0.371461</td>\n",
       "      <td>0.166035</td>\n",
       "      <td>0.243172</td>\n",
       "      <td>0.395691</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>717</th>\n",
       "      <td>0.444697</td>\n",
       "      <td>0.444697</td>\n",
       "      <td>0.444697</td>\n",
       "      <td>0.444697</td>\n",
       "      <td>0.326033</td>\n",
       "      <td>0.196110</td>\n",
       "      <td>0.028727</td>\n",
       "      <td>0.175723</td>\n",
       "      <td>0.180331</td>\n",
       "      <td>0.493773</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.193933</td>\n",
       "      <td>0.368034</td>\n",
       "      <td>0.119357</td>\n",
       "      <td>0.411111</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>0.389922</td>\n",
       "      <td>0.389922</td>\n",
       "      <td>0.389922</td>\n",
       "      <td>0.389922</td>\n",
       "      <td>0.389922</td>\n",
       "      <td>0.316540</td>\n",
       "      <td>0.149068</td>\n",
       "      <td>0.123971</td>\n",
       "      <td>0.319401</td>\n",
       "      <td>0.437585</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.225384</td>\n",
       "      <td>0.157325</td>\n",
       "      <td>0.211601</td>\n",
       "      <td>0.476250</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>0.403169</td>\n",
       "      <td>0.403169</td>\n",
       "      <td>0.403169</td>\n",
       "      <td>0.274981</td>\n",
       "      <td>0.403169</td>\n",
       "      <td>0.095770</td>\n",
       "      <td>0.174818</td>\n",
       "      <td>0.366207</td>\n",
       "      <td>0.316804</td>\n",
       "      <td>0.391430</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>722 rows × 9450 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6     \\\n",
       "0    0.377922  0.284623  0.377922  0.377922  0.377922  0.377922  0.377922   \n",
       "1    0.351057  0.351057  0.351057  0.351057  0.351057  0.351057  0.351057   \n",
       "2    0.366888  0.366888  0.366888  0.366888  0.366888  0.366888  0.306595   \n",
       "3    0.397388  0.254788  0.200981  0.397388  0.397388  0.397388  0.397388   \n",
       "4    0.438970  0.184419  0.085471  0.026047  0.519917  0.519917  0.371461   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "717  0.444697  0.444697  0.444697  0.444697  0.326033  0.196110  0.028727   \n",
       "718  0.368034  0.368034  0.368034  0.368034  0.368034  0.368034  0.193933   \n",
       "719  0.389922  0.389922  0.389922  0.389922  0.389922  0.316540  0.149068   \n",
       "720  0.382900  0.382900  0.382900  0.382900  0.382900  0.382900  0.225384   \n",
       "721  0.403169  0.403169  0.403169  0.274981  0.403169  0.095770  0.174818   \n",
       "\n",
       "         7         8         9     ...      9440      9441      9442  \\\n",
       "0    0.249077  0.000000  0.376627  ...  0.225635  0.463673  0.128284   \n",
       "1    0.351057  0.118614  0.348124  ...       NaN       NaN       NaN   \n",
       "2    0.249046  0.190619  0.528614  ...       NaN       NaN       NaN   \n",
       "3    0.317778  0.064201  0.421881  ...       NaN       NaN       NaN   \n",
       "4    0.166035  0.243172  0.395691  ...       NaN       NaN       NaN   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "717  0.175723  0.180331  0.493773  ...       NaN       NaN       NaN   \n",
       "718  0.368034  0.119357  0.411111  ...       NaN       NaN       NaN   \n",
       "719  0.123971  0.319401  0.437585  ...       NaN       NaN       NaN   \n",
       "720  0.157325  0.211601  0.476250  ...       NaN       NaN       NaN   \n",
       "721  0.366207  0.316804  0.391430  ...       NaN       NaN       NaN   \n",
       "\n",
       "         9443      9444      9445     9446      9447     9448      9449  \n",
       "0    0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  0.277548  \n",
       "1         NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "2         NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "3         NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "4         NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "..        ...       ...       ...      ...       ...      ...       ...  \n",
       "717       NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "718       NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "719       NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "720       NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "721       NaN       NaN       NaN      NaN       NaN      NaN       NaN  \n",
       "\n",
       "[722 rows x 9450 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array(fd1)\n",
    "dataf = pd.DataFrame(arr)\n",
    "dataf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors=5)\n",
    "arr = imputer.fit_transform(dataf[dataf.columns[0:9449]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf = pd.DataFrame(arr)\n",
    "dataf.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>9439</th>\n",
       "      <th>9440</th>\n",
       "      <th>9441</th>\n",
       "      <th>9442</th>\n",
       "      <th>9443</th>\n",
       "      <th>9444</th>\n",
       "      <th>9445</th>\n",
       "      <th>9446</th>\n",
       "      <th>9447</th>\n",
       "      <th>9448</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.377922</td>\n",
       "      <td>0.284623</td>\n",
       "      <td>0.377922</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.376627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308336</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.351057</td>\n",
       "      <td>0.118614</td>\n",
       "      <td>0.348124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308336</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.366888</td>\n",
       "      <td>0.306595</td>\n",
       "      <td>0.249046</td>\n",
       "      <td>0.190619</td>\n",
       "      <td>0.528614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308336</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.254788</td>\n",
       "      <td>0.200981</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.397388</td>\n",
       "      <td>0.317778</td>\n",
       "      <td>0.064201</td>\n",
       "      <td>0.421881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308336</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.438970</td>\n",
       "      <td>0.184419</td>\n",
       "      <td>0.085471</td>\n",
       "      <td>0.026047</td>\n",
       "      <td>0.519917</td>\n",
       "      <td>0.519917</td>\n",
       "      <td>0.371461</td>\n",
       "      <td>0.166035</td>\n",
       "      <td>0.243172</td>\n",
       "      <td>0.395691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308336</td>\n",
       "      <td>0.225635</td>\n",
       "      <td>0.463673</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.251206</td>\n",
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       "      <td>0.463673</td>\n",
       "      <td>0.15093</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.25934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9449 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0         1         2         3         4         5         6     \\\n",
       "0  0.377922  0.284623  0.377922  0.377922  0.377922  0.377922  0.377922   \n",
       "1  0.351057  0.351057  0.351057  0.351057  0.351057  0.351057  0.351057   \n",
       "2  0.366888  0.366888  0.366888  0.366888  0.366888  0.366888  0.306595   \n",
       "3  0.397388  0.254788  0.200981  0.397388  0.397388  0.397388  0.397388   \n",
       "4  0.438970  0.184419  0.085471  0.026047  0.519917  0.519917  0.371461   \n",
       "\n",
       "       7         8         9     ...      9439      9440      9441      9442  \\\n",
       "0  0.249077  0.000000  0.376627  ...  0.308336  0.225635  0.463673  0.128284   \n",
       "1  0.351057  0.118614  0.348124  ...  0.308336  0.225635  0.463673  0.128284   \n",
       "2  0.249046  0.190619  0.528614  ...  0.308336  0.225635  0.463673  0.128284   \n",
       "3  0.317778  0.064201  0.421881  ...  0.308336  0.225635  0.463673  0.128284   \n",
       "4  0.166035  0.243172  0.395691  ...  0.308336  0.225635  0.463673  0.128284   \n",
       "\n",
       "       9443      9444      9445     9446      9447     9448  \n",
       "0  0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  \n",
       "1  0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  \n",
       "2  0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  \n",
       "3  0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  \n",
       "4  0.251206  0.463673  0.463673  0.15093  0.329228  0.25934  \n",
       "\n",
       "[5 rows x 9449 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On TESTSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdTS, hog_imgTS = feature_extraction(ROITS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fdTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8100"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(map(len, fdTS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdTS = normalize_size(fdTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fdTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8100\n"
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.309126</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
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       "      <td>0.047454</td>\n",
       "      <td>0.198764</td>\n",
       "      <td>0.273321</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.375563</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.430649</td>\n",
       "      <td>0.430649</td>\n",
       "      <td>0.068555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388424</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
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       "      <td>0.370282</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.186035</td>\n",
       "      <td>0.278141</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.188652</td>\n",
       "      <td>0.092445</td>\n",
       "      <td>0.357068</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.282530</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.131741</td>\n",
       "      <td>0.172750</td>\n",
       "      <td>0.204009</td>\n",
       "      <td>0.407097</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 8100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6     \\\n",
       "0    0.429353  0.054662  0.429353  0.429353  0.429353  0.429353  0.249474   \n",
       "1    0.386141  0.386141  0.379734  0.386141  0.386141  0.342923  0.158125   \n",
       "2    0.397605  0.244609  0.397605  0.397605  0.397605  0.397605  0.337958   \n",
       "3    0.382401  0.382401  0.309126  0.382401  0.382401  0.382401  0.362667   \n",
       "4    0.375563  0.375563  0.375563  0.375563  0.375563  0.030776  0.108251   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "121  0.425844  0.425844  0.425844  0.378459  0.425844  0.068965  0.192560   \n",
       "122  0.262344  0.290235  0.316871  0.430649  0.430649  0.430649  0.430649   \n",
       "123  0.377812  0.377312  0.360442  0.182565  0.377812  0.377812  0.260491   \n",
       "124  0.319051  0.079347  0.186035  0.278141  0.495245  0.495245  0.495245   \n",
       "125  0.407764  0.282530  0.407764  0.407764  0.407764  0.407764  0.131741   \n",
       "\n",
       "         7         8         9     ...  8090  8091  8092  8093  8094  8095  \\\n",
       "0    0.070397  0.090004  0.397933  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1    0.328067  0.095690  0.270517  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2    0.174885  0.070136  0.140787  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3    0.047454  0.198764  0.273321  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4    0.375563  0.375563  0.320703  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "..        ...       ...       ...  ...   ...   ...   ...   ...   ...   ...   \n",
       "121  0.252441  0.160732  0.497446  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "122  0.068555  0.000000  0.388424  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "123  0.361990  0.259365  0.370282  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "124  0.188652  0.092445  0.357068  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "125  0.172750  0.204009  0.407097  ...   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "     8096  8097  8098  8099  \n",
       "0     NaN   NaN   NaN   NaN  \n",
       "1     NaN   NaN   NaN   NaN  \n",
       "2     NaN   NaN   NaN   NaN  \n",
       "3     NaN   NaN   NaN   NaN  \n",
       "4     NaN   NaN   NaN   NaN  \n",
       "..    ...   ...   ...   ...  \n",
       "121   NaN   NaN   NaN   NaN  \n",
       "122   NaN   NaN   NaN   NaN  \n",
       "123   NaN   NaN   NaN   NaN  \n",
       "124   NaN   NaN   NaN   NaN  \n",
       "125   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[126 rows x 8100 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array(fdTS)\n",
    "print(len(arr[0]))\n",
    "dataTS = pd.DataFrame(arr)\n",
    "dataTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "8100\n",
      "0\n"
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429353</td>\n",
       "      <td>0.054662</td>\n",
       "      <td>0.429353</td>\n",
       "      <td>0.429353</td>\n",
       "      <td>0.429353</td>\n",
       "      <td>0.429353</td>\n",
       "      <td>0.249474</td>\n",
       "      <td>0.070397</td>\n",
       "      <td>0.090004</td>\n",
       "      <td>0.397933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.386141</td>\n",
       "      <td>0.386141</td>\n",
       "      <td>0.379734</td>\n",
       "      <td>0.386141</td>\n",
       "      <td>0.386141</td>\n",
       "      <td>0.342923</td>\n",
       "      <td>0.158125</td>\n",
       "      <td>0.328067</td>\n",
       "      <td>0.095690</td>\n",
       "      <td>0.270517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.397605</td>\n",
       "      <td>0.244609</td>\n",
       "      <td>0.397605</td>\n",
       "      <td>0.397605</td>\n",
       "      <td>0.397605</td>\n",
       "      <td>0.397605</td>\n",
       "      <td>0.337958</td>\n",
       "      <td>0.174885</td>\n",
       "      <td>0.070136</td>\n",
       "      <td>0.140787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.309126</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>0.362667</td>\n",
       "      <td>0.047454</td>\n",
       "      <td>0.198764</td>\n",
       "      <td>0.273321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.030776</td>\n",
       "      <td>0.108251</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.375563</td>\n",
       "      <td>0.320703</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</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>121</th>\n",
       "      <td>0.425844</td>\n",
       "      <td>0.425844</td>\n",
       "      <td>0.425844</td>\n",
       "      <td>0.378459</td>\n",
       "      <td>0.425844</td>\n",
       "      <td>0.068965</td>\n",
       "      <td>0.192560</td>\n",
       "      <td>0.252441</td>\n",
       "      <td>0.160732</td>\n",
       "      <td>0.497446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.262344</td>\n",
       "      <td>0.290235</td>\n",
       "      <td>0.316871</td>\n",
       "      <td>0.430649</td>\n",
       "      <td>0.430649</td>\n",
       "      <td>0.430649</td>\n",
       "      <td>0.430649</td>\n",
       "      <td>0.068555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.388424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>0.377812</td>\n",
       "      <td>0.377312</td>\n",
       "      <td>0.360442</td>\n",
       "      <td>0.182565</td>\n",
       "      <td>0.377812</td>\n",
       "      <td>0.377812</td>\n",
       "      <td>0.260491</td>\n",
       "      <td>0.361990</td>\n",
       "      <td>0.259365</td>\n",
       "      <td>0.370282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.319051</td>\n",
       "      <td>0.079347</td>\n",
       "      <td>0.186035</td>\n",
       "      <td>0.278141</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.495245</td>\n",
       "      <td>0.188652</td>\n",
       "      <td>0.092445</td>\n",
       "      <td>0.357068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.282530</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.407764</td>\n",
       "      <td>0.131741</td>\n",
       "      <td>0.172750</td>\n",
       "      <td>0.204009</td>\n",
       "      <td>0.407097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442368</td>\n",
       "      <td>0.280049</td>\n",
       "      <td>0.117648</td>\n",
       "      <td>0.383699</td>\n",
       "      <td>0.788125</td>\n",
       "      <td>0.330311</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>0.062711</td>\n",
       "      <td>0.037789</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 8100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6     \\\n",
       "0    0.429353  0.054662  0.429353  0.429353  0.429353  0.429353  0.249474   \n",
       "1    0.386141  0.386141  0.379734  0.386141  0.386141  0.342923  0.158125   \n",
       "2    0.397605  0.244609  0.397605  0.397605  0.397605  0.397605  0.337958   \n",
       "3    0.382401  0.382401  0.309126  0.382401  0.382401  0.382401  0.362667   \n",
       "4    0.375563  0.375563  0.375563  0.375563  0.375563  0.030776  0.108251   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "121  0.425844  0.425844  0.425844  0.378459  0.425844  0.068965  0.192560   \n",
       "122  0.262344  0.290235  0.316871  0.430649  0.430649  0.430649  0.430649   \n",
       "123  0.377812  0.377312  0.360442  0.182565  0.377812  0.377812  0.260491   \n",
       "124  0.319051  0.079347  0.186035  0.278141  0.495245  0.495245  0.495245   \n",
       "125  0.407764  0.282530  0.407764  0.407764  0.407764  0.407764  0.131741   \n",
       "\n",
       "         7         8         9     ...      8090      8091      8092  \\\n",
       "0    0.070397  0.090004  0.397933  ...  0.442368  0.280049  0.117648   \n",
       "1    0.328067  0.095690  0.270517  ...  0.442368  0.280049  0.117648   \n",
       "2    0.174885  0.070136  0.140787  ...  0.442368  0.280049  0.117648   \n",
       "3    0.047454  0.198764  0.273321  ...  0.442368  0.280049  0.117648   \n",
       "4    0.375563  0.375563  0.320703  ...  0.442368  0.280049  0.117648   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "121  0.252441  0.160732  0.497446  ...  0.442368  0.280049  0.117648   \n",
       "122  0.068555  0.000000  0.388424  ...  0.442368  0.280049  0.117648   \n",
       "123  0.361990  0.259365  0.370282  ...  0.442368  0.280049  0.117648   \n",
       "124  0.188652  0.092445  0.357068  ...  0.442368  0.280049  0.117648   \n",
       "125  0.172750  0.204009  0.407097  ...  0.442368  0.280049  0.117648   \n",
       "\n",
       "         8093      8094      8095      8096      8097      8098      8099  \n",
       "0    0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "1    0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "2    0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "3    0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "4    0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "121  0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "122  0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "123  0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "124  0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "125  0.383699  0.788125  0.330311  0.157745  0.062711  0.037789  0.004033  \n",
       "\n",
       "[126 rows x 8100 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = KNNImputer(n_neighbors=5)\n",
    "arr = imputer.fit_transform(dataTS[dataTS.columns[0:9450]])\n",
    "print(len(arr[0]))\n",
    "dataTS = pd.DataFrame(arr)\n",
    "print(dataf.isnull().sum().sum())\n",
    "dataTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA()\n",
    "pca1 = PCA()\n",
    "pca.fit(dataf)\n",
    "pca1.fit(dataTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "var=np.cumsum(pca.explained_variance_ratio_)\n",
    "var1=np.cumsum(pca1.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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"
    },
    {
     "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(var)\n",
    "plt.show()\n",
    "plt.plot(var1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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"
    },
    {
     "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(var)\n",
    "plt.axhline(y=0.9, color='r', linestyle='-')\n",
    "plt.axvline(x=356, color='r', linestyle='-')\n",
    "plt.show()\n",
    "plt.plot(var1)\n",
    "plt.axhline(y=0.9, color='r', linestyle='-')\n",
    "plt.axvline(x=67, color='r', linestyle='-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "356\n",
      "67\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(var > 0.9))\n",
    "print(np.argmax(var1 > 0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=356)\n",
    "reduced = pca.fit_transform(dataf)\n",
    "recovered = pd.DataFrame(data = reduced)\n",
    "\n",
    "pca1 = PCA(n_components=67)\n",
    "reduced1 = pca1.fit_transform(dataTS)\n",
    "recovered1 = pd.DataFrame(data = reduced1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_independant_subject(X):\n",
    "    \n",
    "    #Extracting only the name of the subject (column 'filename')\n",
    "    filenames = X.iloc[:, 0]\n",
    "    #filenames.head(5)\n",
    "    \n",
    "    #Save a copy of the trainset in 'trainsetCpy'\n",
    "    XCpy = X.copy()\n",
    "    #We replace the column 'filename' with the id of each subject. The id is composed  by the first 4 characters.\n",
    "    XCpy.loc[XCpy['filename'].notnull(), 'filename'] = \"\"\n",
    "    for i in range (0, 4): \n",
    "        XCpy.loc[XCpy['filename'].notnull(), 'filename'] += X['filename'].str[i]\n",
    "    #XCpy.head(5)\n",
    "    \n",
    "    #We shuffle randomly the dataset in order to do the separation subject independant properly and for any order of the rows\n",
    "    xshuffle = XCpy.sample(frac=1)\n",
    "    #xshuffle.head(5)\n",
    "  \n",
    "    #We extract all the unique people in the trainset\n",
    "    unique_users = xshuffle['filename'].unique()\n",
    "    #print(unique_users)\n",
    "    #print(\"There is \", len(unique_users), \" unique people in the trainset\")\n",
    "    \n",
    "    #We split the unique users with 80% in train and 20% in validation\n",
    "    #We will test our model on the validation before the prediction on the real testset\n",
    "    trainset_train_unique, trainset_validation_unique = np.split(np.random.permutation(unique_users), [int(.8 * len(unique_users))])\n",
    "    #print(\"There is \", len(trainset_train_unique), \" unique people in the trainset_train_unique\")\n",
    "    #print(\"There is \", len(trainset_validation_unique), \" unique people in the trainset_validation_unique\")\n",
    "    \n",
    "    #---------------------------------------------------------------------\n",
    "    \n",
    "    #We recreate the dataset corresponding to the unique users\n",
    "    trainset_train = X.copy() #trainset_train is the final data set for real training containing images for unique people\n",
    "    trainset_validation = X.copy() #trainset_validation is the final data set for validation containing images for unique people\n",
    "    #count1 = 0\n",
    "    #count2 = 0\n",
    "\n",
    "    #We put all the values of column 'filename' to empty string\n",
    "    trainset_train.loc[XCpy['filename'].notnull(), 'filename'] = \"\" \n",
    "    trainset_validation.loc[XCpy['filename'].notnull(), 'filename'] = \"\" \n",
    "\n",
    "    #We loop the whole trainset using iteritems(). columnName is the name of the column\n",
    "    #and columnData in a sort of list of each value of the columnn\n",
    "\n",
    "    #\n",
    "    for (columnName, columnData) in X.iteritems():\n",
    "        #We want only the column 'filename'\n",
    "        if(columnName == 'filename'):\n",
    "\n",
    "            #We select each unique subject in the train_set\n",
    "            for j in trainset_train_unique:\n",
    "                #print(\"unique_users_train\", j)\n",
    "\n",
    "                #We select all the subject (complete value. Ex: S005_001_00000001) in the trainset\n",
    "                for i in range(len(columnData.values)):\n",
    "                    #We put the value in the variable 'select_users'\n",
    "                    select_users = columnData.values[i]\n",
    "                    #print(\"Select_users\", select_users[0:4])\n",
    "\n",
    "                    #We select only the first four characters corresponding to the unique id of the subjects\n",
    "                    #and if the unique value equals the subset,\n",
    "                    if(j == select_users[0:4]):\n",
    "                        #we get all the rows\n",
    "                        trainset_train.iloc[i, 0] = select_users\n",
    "                        #count1 = count1+1\n",
    "                    else:\n",
    "                        #print(\"Not corresponding\")\n",
    "                        pass\n",
    "\n",
    "            #We select each unique subject in the validation_set\n",
    "            for k in trainset_validation_unique:\n",
    "                #print(\"unique_users_val\", k)\n",
    "\n",
    "                #We select all the subject (complete value. Ex: S005_001_00000001) in the trainset\n",
    "                for l in range(len(columnData.values)):\n",
    "                    #We put the value in the variable 'select_users'\n",
    "                    select_users = columnData.values[l]\n",
    "                    #print(\"Select_users\", select_users[0:4])\n",
    "\n",
    "                    #We select only the first four characters corresponding to the unique id of the subjects\n",
    "                    #and if the unique value equals the subset,\n",
    "                    if(k == select_users[0:4]):\n",
    "                        #we get all the rows\n",
    "                        trainset_validation.iloc[l, 0] = select_users\n",
    "                        #count2 = count2+1\n",
    "                    else:\n",
    "                        #print(\"Not corresponding\")\n",
    "                        pass\n",
    "\n",
    "    #print(count1, count2) \n",
    "    \n",
    "    #Here there are empty value for the column 'filename' because above, we took the whole line for trainset_train\n",
    "    #trainset_train.head(5)\n",
    "    #Here there are empty value for the column 'filename' because above, we took the whole line for validation_train\n",
    "    #trainset_validation.head(5) \n",
    "    \n",
    "    #We replace these empty values by nan for both train and validation\n",
    "    trainset_train['filename'].replace('', np.nan, inplace=True)\n",
    "    trainset_validation['filename'].replace('', np.nan, inplace=True)\n",
    "\n",
    "    #And we drop the nan values for both train and validation\n",
    "    trainset_train.dropna(subset=['filename'], inplace=True)\n",
    "    trainset_validation.dropna(subset=['filename'], inplace=True)\n",
    "    \n",
    "    #Finaly every images in the dataset are unique\n",
    "    #trainset_train.head(5)\n",
    "    \n",
    "    #Finaly every images in the dataset are unique\n",
    "    #trainset_validation.head(5)\n",
    "\n",
    "    return trainset_train, trainset_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
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       "      <th>x_52</th>\n",
       "      <th>x_53</th>\n",
       "      <th>x_54</th>\n",
       "      <th>x_55</th>\n",
       "      <th>x_56</th>\n",
       "      <th>x_57</th>\n",
       "      <th>x_58</th>\n",
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       "      <th>x_63</th>\n",
       "      <th>x_64</th>\n",
       "      <th>x_65</th>\n",
       "      <th>x_66</th>\n",
       "      <th>x_67</th>\n",
       "      <th>y_0</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "      <th>y_4</th>\n",
       "      <th>y_5</th>\n",
       "      <th>y_6</th>\n",
       "      <th>y_7</th>\n",
       "      <th>y_8</th>\n",
       "      <th>y_9</th>\n",
       "      <th>y_10</th>\n",
       "      <th>y_11</th>\n",
       "      <th>y_12</th>\n",
       "      <th>y_13</th>\n",
       "      <th>y_14</th>\n",
       "      <th>y_15</th>\n",
       "      <th>y_16</th>\n",
       "      <th>y_17</th>\n",
       "      <th>y_18</th>\n",
       "      <th>y_19</th>\n",
       "      <th>y_20</th>\n",
       "      <th>y_21</th>\n",
       "      <th>y_22</th>\n",
       "      <th>y_23</th>\n",
       "      <th>y_24</th>\n",
       "      <th>y_25</th>\n",
       "      <th>y_26</th>\n",
       "      <th>y_27</th>\n",
       "      <th>y_28</th>\n",
       "      <th>y_29</th>\n",
       "      <th>y_30</th>\n",
       "      <th>y_31</th>\n",
       "      <th>y_32</th>\n",
       "      <th>y_33</th>\n",
       "      <th>y_34</th>\n",
       "      <th>y_35</th>\n",
       "      <th>y_36</th>\n",
       "      <th>y_37</th>\n",
       "      <th>y_38</th>\n",
       "      <th>y_39</th>\n",
       "      <th>y_40</th>\n",
       "      <th>y_41</th>\n",
       "      <th>y_42</th>\n",
       "      <th>y_43</th>\n",
       "      <th>y_44</th>\n",
       "      <th>y_45</th>\n",
       "      <th>y_46</th>\n",
       "      <th>y_47</th>\n",
       "      <th>y_48</th>\n",
       "      <th>y_49</th>\n",
       "      <th>y_50</th>\n",
       "      <th>y_51</th>\n",
       "      <th>y_52</th>\n",
       "      <th>y_53</th>\n",
       "      <th>y_54</th>\n",
       "      <th>y_55</th>\n",
       "      <th>y_56</th>\n",
       "      <th>y_57</th>\n",
       "      <th>y_58</th>\n",
       "      <th>y_59</th>\n",
       "      <th>y_60</th>\n",
       "      <th>y_61</th>\n",
       "      <th>y_62</th>\n",
       "      <th>y_63</th>\n",
       "      <th>y_64</th>\n",
       "      <th>y_65</th>\n",
       "      <th>y_66</th>\n",
       "      <th>y_67</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S005_001_00000001</td>\n",
       "      <td>213.7</td>\n",
       "      <td>214.1</td>\n",
       "      <td>217.7</td>\n",
       "      <td>223.7</td>\n",
       "      <td>236.9</td>\n",
       "      <td>262.2</td>\n",
       "      <td>299.3</td>\n",
       "      <td>340.3</td>\n",
       "      <td>381.0</td>\n",
       "      <td>418.1</td>\n",
       "      <td>450.7</td>\n",
       "      <td>478.6</td>\n",
       "      <td>500.2</td>\n",
       "      <td>512.2</td>\n",
       "      <td>516.9</td>\n",
       "      <td>519.3</td>\n",
       "      <td>518.9</td>\n",
       "      <td>248.8</td>\n",
       "      <td>266.1</td>\n",
       "      <td>291.1</td>\n",
       "      <td>317.6</td>\n",
       "      <td>342.6</td>\n",
       "      <td>395.8</td>\n",
       "      <td>419.9</td>\n",
       "      <td>444.0</td>\n",
       "      <td>468.1</td>\n",
       "      <td>486.5</td>\n",
       "      <td>371.5</td>\n",
       "      <td>373.3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>376.5</td>\n",
       "      <td>348.3</td>\n",
       "      <td>362.3</td>\n",
       "      <td>376.3</td>\n",
       "      <td>389.9</td>\n",
       "      <td>402.4</td>\n",
       "      <td>282.8</td>\n",
       "      <td>298.9</td>\n",
       "      <td>316.8</td>\n",
       "      <td>332.1</td>\n",
       "      <td>317.0</td>\n",
       "      <td>299.2</td>\n",
       "      <td>408.5</td>\n",
       "      <td>423.0</td>\n",
       "      <td>440.6</td>\n",
       "      <td>456.2</td>\n",
       "      <td>442.6</td>\n",
       "      <td>425.6</td>\n",
       "      <td>325.0</td>\n",
       "      <td>344.8</td>\n",
       "      <td>363.6</td>\n",
       "      <td>378.1</td>\n",
       "      <td>393.5</td>\n",
       "      <td>411.8</td>\n",
       "      <td>428.9</td>\n",
       "      <td>413.5</td>\n",
       "      <td>397.0</td>\n",
       "      <td>380.3</td>\n",
       "      <td>364.7</td>\n",
       "      <td>345.6</td>\n",
       "      <td>334.5</td>\n",
       "      <td>363.7</td>\n",
       "      <td>378.3</td>\n",
       "      <td>394.1</td>\n",
       "      <td>420.5</td>\n",
       "      <td>394.3</td>\n",
       "      <td>378.3</td>\n",
       "      <td>363.6</td>\n",
       "      <td>207.1</td>\n",
       "      <td>245.8</td>\n",
       "      <td>285.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>356.7</td>\n",
       "      <td>384.4</td>\n",
       "      <td>403.1</td>\n",
       "      <td>416.4</td>\n",
       "      <td>419.9</td>\n",
       "      <td>415.9</td>\n",
       "      <td>403.2</td>\n",
       "      <td>386.6</td>\n",
       "      <td>361.8</td>\n",
       "      <td>327.5</td>\n",
       "      <td>287.0</td>\n",
       "      <td>243.7</td>\n",
       "      <td>200.5</td>\n",
       "      <td>179.2</td>\n",
       "      <td>162.5</td>\n",
       "      <td>157.1</td>\n",
       "      <td>160.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>164.6</td>\n",
       "      <td>154.3</td>\n",
       "      <td>149.9</td>\n",
       "      <td>152.9</td>\n",
       "      <td>166.5</td>\n",
       "      <td>198.0</td>\n",
       "      <td>221.9</td>\n",
       "      <td>245.4</td>\n",
       "      <td>269.8</td>\n",
       "      <td>288.2</td>\n",
       "      <td>292.2</td>\n",
       "      <td>295.3</td>\n",
       "      <td>291.4</td>\n",
       "      <td>287.1</td>\n",
       "      <td>203.9</td>\n",
       "      <td>195.5</td>\n",
       "      <td>194.9</td>\n",
       "      <td>204.8</td>\n",
       "      <td>208.1</td>\n",
       "      <td>208.6</td>\n",
       "      <td>201.5</td>\n",
       "      <td>190.1</td>\n",
       "      <td>189.6</td>\n",
       "      <td>196.6</td>\n",
       "      <td>202.4</td>\n",
       "      <td>203.6</td>\n",
       "      <td>338.9</td>\n",
       "      <td>325.0</td>\n",
       "      <td>318.1</td>\n",
       "      <td>321.4</td>\n",
       "      <td>317.5</td>\n",
       "      <td>323.9</td>\n",
       "      <td>334.6</td>\n",
       "      <td>348.8</td>\n",
       "      <td>355.4</td>\n",
       "      <td>357.2</td>\n",
       "      <td>356.4</td>\n",
       "      <td>351.8</td>\n",
       "      <td>337.6</td>\n",
       "      <td>333.1</td>\n",
       "      <td>333.6</td>\n",
       "      <td>331.9</td>\n",
       "      <td>335.1</td>\n",
       "      <td>335.0</td>\n",
       "      <td>336.4</td>\n",
       "      <td>335.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S005_001_00000011</td>\n",
       "      <td>215.0</td>\n",
       "      <td>214.6</td>\n",
       "      <td>217.3</td>\n",
       "      <td>223.4</td>\n",
       "      <td>236.9</td>\n",
       "      <td>262.7</td>\n",
       "      <td>300.6</td>\n",
       "      <td>340.6</td>\n",
       "      <td>380.5</td>\n",
       "      <td>417.6</td>\n",
       "      <td>450.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>500.1</td>\n",
       "      <td>512.6</td>\n",
       "      <td>517.2</td>\n",
       "      <td>519.1</td>\n",
       "      <td>517.9</td>\n",
       "      <td>246.5</td>\n",
       "      <td>265.0</td>\n",
       "      <td>291.8</td>\n",
       "      <td>318.3</td>\n",
       "      <td>342.9</td>\n",
       "      <td>397.5</td>\n",
       "      <td>421.1</td>\n",
       "      <td>445.2</td>\n",
       "      <td>469.9</td>\n",
       "      <td>489.9</td>\n",
       "      <td>371.8</td>\n",
       "      <td>373.4</td>\n",
       "      <td>374.8</td>\n",
       "      <td>376.0</td>\n",
       "      <td>346.5</td>\n",
       "      <td>360.8</td>\n",
       "      <td>375.5</td>\n",
       "      <td>389.5</td>\n",
       "      <td>402.4</td>\n",
       "      <td>276.3</td>\n",
       "      <td>293.4</td>\n",
       "      <td>311.6</td>\n",
       "      <td>328.7</td>\n",
       "      <td>312.0</td>\n",
       "      <td>293.9</td>\n",
       "      <td>412.7</td>\n",
       "      <td>429.4</td>\n",
       "      <td>447.3</td>\n",
       "      <td>463.2</td>\n",
       "      <td>448.9</td>\n",
       "      <td>431.3</td>\n",
       "      <td>324.8</td>\n",
       "      <td>343.6</td>\n",
       "      <td>362.0</td>\n",
       "      <td>376.9</td>\n",
       "      <td>393.1</td>\n",
       "      <td>411.6</td>\n",
       "      <td>428.6</td>\n",
       "      <td>412.9</td>\n",
       "      <td>396.1</td>\n",
       "      <td>378.2</td>\n",
       "      <td>362.2</td>\n",
       "      <td>343.9</td>\n",
       "      <td>334.7</td>\n",
       "      <td>361.7</td>\n",
       "      <td>376.9</td>\n",
       "      <td>393.4</td>\n",
       "      <td>419.8</td>\n",
       "      <td>393.7</td>\n",
       "      <td>376.7</td>\n",
       "      <td>361.4</td>\n",
       "      <td>209.0</td>\n",
       "      <td>246.4</td>\n",
       "      <td>285.1</td>\n",
       "      <td>322.8</td>\n",
       "      <td>357.5</td>\n",
       "      <td>385.2</td>\n",
       "      <td>403.3</td>\n",
       "      <td>416.7</td>\n",
       "      <td>420.7</td>\n",
       "      <td>417.0</td>\n",
       "      <td>404.3</td>\n",
       "      <td>388.3</td>\n",
       "      <td>364.4</td>\n",
       "      <td>330.6</td>\n",
       "      <td>289.7</td>\n",
       "      <td>245.6</td>\n",
       "      <td>201.9</td>\n",
       "      <td>181.8</td>\n",
       "      <td>166.1</td>\n",
       "      <td>163.3</td>\n",
       "      <td>168.4</td>\n",
       "      <td>176.7</td>\n",
       "      <td>172.2</td>\n",
       "      <td>160.7</td>\n",
       "      <td>154.0</td>\n",
       "      <td>154.3</td>\n",
       "      <td>166.7</td>\n",
       "      <td>197.2</td>\n",
       "      <td>219.9</td>\n",
       "      <td>242.6</td>\n",
       "      <td>266.2</td>\n",
       "      <td>283.0</td>\n",
       "      <td>287.8</td>\n",
       "      <td>291.0</td>\n",
       "      <td>287.2</td>\n",
       "      <td>282.9</td>\n",
       "      <td>202.9</td>\n",
       "      <td>195.4</td>\n",
       "      <td>194.4</td>\n",
       "      <td>203.1</td>\n",
       "      <td>206.0</td>\n",
       "      <td>206.8</td>\n",
       "      <td>199.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>188.5</td>\n",
       "      <td>194.5</td>\n",
       "      <td>199.9</td>\n",
       "      <td>200.8</td>\n",
       "      <td>333.2</td>\n",
       "      <td>318.5</td>\n",
       "      <td>311.6</td>\n",
       "      <td>315.1</td>\n",
       "      <td>310.9</td>\n",
       "      <td>318.5</td>\n",
       "      <td>330.5</td>\n",
       "      <td>344.2</td>\n",
       "      <td>351.4</td>\n",
       "      <td>353.5</td>\n",
       "      <td>352.4</td>\n",
       "      <td>346.9</td>\n",
       "      <td>331.7</td>\n",
       "      <td>326.9</td>\n",
       "      <td>327.5</td>\n",
       "      <td>326.1</td>\n",
       "      <td>330.3</td>\n",
       "      <td>330.2</td>\n",
       "      <td>332.0</td>\n",
       "      <td>331.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S010_001_00000001</td>\n",
       "      <td>261.3</td>\n",
       "      <td>263.7</td>\n",
       "      <td>268.2</td>\n",
       "      <td>274.2</td>\n",
       "      <td>284.0</td>\n",
       "      <td>301.3</td>\n",
       "      <td>324.9</td>\n",
       "      <td>353.6</td>\n",
       "      <td>385.7</td>\n",
       "      <td>416.3</td>\n",
       "      <td>442.0</td>\n",
       "      <td>464.6</td>\n",
       "      <td>483.1</td>\n",
       "      <td>493.6</td>\n",
       "      <td>497.9</td>\n",
       "      <td>500.3</td>\n",
       "      <td>500.1</td>\n",
       "      <td>270.5</td>\n",
       "      <td>285.1</td>\n",
       "      <td>305.9</td>\n",
       "      <td>327.5</td>\n",
       "      <td>348.4</td>\n",
       "      <td>403.4</td>\n",
       "      <td>426.6</td>\n",
       "      <td>448.4</td>\n",
       "      <td>469.5</td>\n",
       "      <td>485.1</td>\n",
       "      <td>377.4</td>\n",
       "      <td>378.4</td>\n",
       "      <td>379.3</td>\n",
       "      <td>380.3</td>\n",
       "      <td>354.5</td>\n",
       "      <td>367.4</td>\n",
       "      <td>380.8</td>\n",
       "      <td>394.3</td>\n",
       "      <td>406.4</td>\n",
       "      <td>295.1</td>\n",
       "      <td>308.1</td>\n",
       "      <td>325.7</td>\n",
       "      <td>341.0</td>\n",
       "      <td>325.7</td>\n",
       "      <td>308.7</td>\n",
       "      <td>414.4</td>\n",
       "      <td>428.7</td>\n",
       "      <td>446.1</td>\n",
       "      <td>460.2</td>\n",
       "      <td>447.7</td>\n",
       "      <td>431.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>369.6</td>\n",
       "      <td>382.2</td>\n",
       "      <td>395.4</td>\n",
       "      <td>414.8</td>\n",
       "      <td>433.4</td>\n",
       "      <td>416.6</td>\n",
       "      <td>398.1</td>\n",
       "      <td>383.6</td>\n",
       "      <td>370.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>340.9</td>\n",
       "      <td>369.7</td>\n",
       "      <td>382.6</td>\n",
       "      <td>396.2</td>\n",
       "      <td>426.0</td>\n",
       "      <td>396.1</td>\n",
       "      <td>382.4</td>\n",
       "      <td>369.5</td>\n",
       "      <td>239.5</td>\n",
       "      <td>270.1</td>\n",
       "      <td>299.7</td>\n",
       "      <td>328.0</td>\n",
       "      <td>354.4</td>\n",
       "      <td>377.5</td>\n",
       "      <td>396.3</td>\n",
       "      <td>411.8</td>\n",
       "      <td>415.8</td>\n",
       "      <td>411.4</td>\n",
       "      <td>396.9</td>\n",
       "      <td>378.3</td>\n",
       "      <td>354.6</td>\n",
       "      <td>325.0</td>\n",
       "      <td>293.1</td>\n",
       "      <td>259.9</td>\n",
       "      <td>226.6</td>\n",
       "      <td>203.7</td>\n",
       "      <td>191.6</td>\n",
       "      <td>187.9</td>\n",
       "      <td>189.8</td>\n",
       "      <td>195.1</td>\n",
       "      <td>193.3</td>\n",
       "      <td>185.1</td>\n",
       "      <td>182.1</td>\n",
       "      <td>185.1</td>\n",
       "      <td>196.2</td>\n",
       "      <td>221.7</td>\n",
       "      <td>244.2</td>\n",
       "      <td>266.7</td>\n",
       "      <td>289.6</td>\n",
       "      <td>303.6</td>\n",
       "      <td>308.5</td>\n",
       "      <td>312.1</td>\n",
       "      <td>307.4</td>\n",
       "      <td>302.2</td>\n",
       "      <td>228.6</td>\n",
       "      <td>218.9</td>\n",
       "      <td>218.1</td>\n",
       "      <td>227.9</td>\n",
       "      <td>232.4</td>\n",
       "      <td>233.4</td>\n",
       "      <td>225.3</td>\n",
       "      <td>214.1</td>\n",
       "      <td>214.0</td>\n",
       "      <td>221.9</td>\n",
       "      <td>227.7</td>\n",
       "      <td>228.2</td>\n",
       "      <td>346.1</td>\n",
       "      <td>339.3</td>\n",
       "      <td>335.8</td>\n",
       "      <td>338.5</td>\n",
       "      <td>334.8</td>\n",
       "      <td>337.8</td>\n",
       "      <td>342.4</td>\n",
       "      <td>357.0</td>\n",
       "      <td>363.9</td>\n",
       "      <td>365.4</td>\n",
       "      <td>364.5</td>\n",
       "      <td>358.9</td>\n",
       "      <td>346.7</td>\n",
       "      <td>346.8</td>\n",
       "      <td>347.5</td>\n",
       "      <td>345.6</td>\n",
       "      <td>343.6</td>\n",
       "      <td>346.9</td>\n",
       "      <td>348.3</td>\n",
       "      <td>347.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S010_002_00000001</td>\n",
       "      <td>259.5</td>\n",
       "      <td>262.2</td>\n",
       "      <td>267.2</td>\n",
       "      <td>273.4</td>\n",
       "      <td>283.1</td>\n",
       "      <td>299.9</td>\n",
       "      <td>322.9</td>\n",
       "      <td>351.0</td>\n",
       "      <td>383.3</td>\n",
       "      <td>414.3</td>\n",
       "      <td>440.2</td>\n",
       "      <td>462.9</td>\n",
       "      <td>480.9</td>\n",
       "      <td>490.9</td>\n",
       "      <td>495.2</td>\n",
       "      <td>498.0</td>\n",
       "      <td>498.2</td>\n",
       "      <td>268.5</td>\n",
       "      <td>282.7</td>\n",
       "      <td>303.2</td>\n",
       "      <td>324.6</td>\n",
       "      <td>345.4</td>\n",
       "      <td>401.8</td>\n",
       "      <td>424.9</td>\n",
       "      <td>446.6</td>\n",
       "      <td>467.4</td>\n",
       "      <td>482.8</td>\n",
       "      <td>375.2</td>\n",
       "      <td>375.9</td>\n",
       "      <td>376.6</td>\n",
       "      <td>377.3</td>\n",
       "      <td>352.2</td>\n",
       "      <td>364.8</td>\n",
       "      <td>378.1</td>\n",
       "      <td>391.4</td>\n",
       "      <td>403.4</td>\n",
       "      <td>293.3</td>\n",
       "      <td>306.3</td>\n",
       "      <td>323.8</td>\n",
       "      <td>339.0</td>\n",
       "      <td>323.8</td>\n",
       "      <td>306.8</td>\n",
       "      <td>412.3</td>\n",
       "      <td>426.6</td>\n",
       "      <td>444.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>445.5</td>\n",
       "      <td>428.8</td>\n",
       "      <td>332.4</td>\n",
       "      <td>350.3</td>\n",
       "      <td>367.4</td>\n",
       "      <td>379.4</td>\n",
       "      <td>392.1</td>\n",
       "      <td>410.9</td>\n",
       "      <td>429.0</td>\n",
       "      <td>412.7</td>\n",
       "      <td>394.7</td>\n",
       "      <td>380.9</td>\n",
       "      <td>367.9</td>\n",
       "      <td>350.5</td>\n",
       "      <td>340.1</td>\n",
       "      <td>367.6</td>\n",
       "      <td>379.9</td>\n",
       "      <td>392.9</td>\n",
       "      <td>421.6</td>\n",
       "      <td>392.8</td>\n",
       "      <td>379.8</td>\n",
       "      <td>367.5</td>\n",
       "      <td>240.9</td>\n",
       "      <td>271.5</td>\n",
       "      <td>301.0</td>\n",
       "      <td>329.1</td>\n",
       "      <td>355.8</td>\n",
       "      <td>379.4</td>\n",
       "      <td>398.9</td>\n",
       "      <td>415.0</td>\n",
       "      <td>419.3</td>\n",
       "      <td>414.3</td>\n",
       "      <td>398.8</td>\n",
       "      <td>379.0</td>\n",
       "      <td>354.5</td>\n",
       "      <td>325.0</td>\n",
       "      <td>293.7</td>\n",
       "      <td>261.2</td>\n",
       "      <td>228.5</td>\n",
       "      <td>205.9</td>\n",
       "      <td>194.1</td>\n",
       "      <td>190.4</td>\n",
       "      <td>192.6</td>\n",
       "      <td>198.1</td>\n",
       "      <td>196.4</td>\n",
       "      <td>188.4</td>\n",
       "      <td>185.2</td>\n",
       "      <td>187.8</td>\n",
       "      <td>198.5</td>\n",
       "      <td>224.3</td>\n",
       "      <td>246.9</td>\n",
       "      <td>269.5</td>\n",
       "      <td>292.6</td>\n",
       "      <td>306.1</td>\n",
       "      <td>311.1</td>\n",
       "      <td>314.6</td>\n",
       "      <td>310.0</td>\n",
       "      <td>304.6</td>\n",
       "      <td>230.3</td>\n",
       "      <td>220.9</td>\n",
       "      <td>220.1</td>\n",
       "      <td>229.8</td>\n",
       "      <td>234.3</td>\n",
       "      <td>235.3</td>\n",
       "      <td>227.5</td>\n",
       "      <td>216.4</td>\n",
       "      <td>216.4</td>\n",
       "      <td>224.1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>230.5</td>\n",
       "      <td>349.5</td>\n",
       "      <td>342.3</td>\n",
       "      <td>338.6</td>\n",
       "      <td>341.3</td>\n",
       "      <td>337.7</td>\n",
       "      <td>340.9</td>\n",
       "      <td>345.9</td>\n",
       "      <td>360.5</td>\n",
       "      <td>367.3</td>\n",
       "      <td>368.8</td>\n",
       "      <td>367.9</td>\n",
       "      <td>362.4</td>\n",
       "      <td>349.9</td>\n",
       "      <td>349.6</td>\n",
       "      <td>350.3</td>\n",
       "      <td>348.6</td>\n",
       "      <td>347.1</td>\n",
       "      <td>350.1</td>\n",
       "      <td>351.6</td>\n",
       "      <td>350.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S010_002_00000014</td>\n",
       "      <td>263.7</td>\n",
       "      <td>263.4</td>\n",
       "      <td>267.1</td>\n",
       "      <td>273.7</td>\n",
       "      <td>282.1</td>\n",
       "      <td>296.9</td>\n",
       "      <td>318.3</td>\n",
       "      <td>349.5</td>\n",
       "      <td>383.5</td>\n",
       "      <td>414.7</td>\n",
       "      <td>439.3</td>\n",
       "      <td>461.9</td>\n",
       "      <td>479.3</td>\n",
       "      <td>489.9</td>\n",
       "      <td>496.3</td>\n",
       "      <td>500.3</td>\n",
       "      <td>500.9</td>\n",
       "      <td>274.9</td>\n",
       "      <td>289.6</td>\n",
       "      <td>310.1</td>\n",
       "      <td>331.4</td>\n",
       "      <td>349.8</td>\n",
       "      <td>401.9</td>\n",
       "      <td>426.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>469.7</td>\n",
       "      <td>483.7</td>\n",
       "      <td>375.5</td>\n",
       "      <td>376.3</td>\n",
       "      <td>377.2</td>\n",
       "      <td>377.9</td>\n",
       "      <td>353.4</td>\n",
       "      <td>366.0</td>\n",
       "      <td>378.8</td>\n",
       "      <td>391.8</td>\n",
       "      <td>403.7</td>\n",
       "      <td>293.2</td>\n",
       "      <td>305.7</td>\n",
       "      <td>325.4</td>\n",
       "      <td>341.1</td>\n",
       "      <td>325.1</td>\n",
       "      <td>305.9</td>\n",
       "      <td>412.4</td>\n",
       "      <td>427.1</td>\n",
       "      <td>446.2</td>\n",
       "      <td>459.8</td>\n",
       "      <td>448.7</td>\n",
       "      <td>430.0</td>\n",
       "      <td>340.8</td>\n",
       "      <td>355.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>380.1</td>\n",
       "      <td>390.6</td>\n",
       "      <td>407.7</td>\n",
       "      <td>422.0</td>\n",
       "      <td>409.7</td>\n",
       "      <td>394.3</td>\n",
       "      <td>382.4</td>\n",
       "      <td>370.4</td>\n",
       "      <td>355.8</td>\n",
       "      <td>349.7</td>\n",
       "      <td>369.2</td>\n",
       "      <td>380.5</td>\n",
       "      <td>391.3</td>\n",
       "      <td>414.0</td>\n",
       "      <td>391.4</td>\n",
       "      <td>380.5</td>\n",
       "      <td>369.1</td>\n",
       "      <td>235.3</td>\n",
       "      <td>265.2</td>\n",
       "      <td>294.8</td>\n",
       "      <td>324.4</td>\n",
       "      <td>353.9</td>\n",
       "      <td>382.1</td>\n",
       "      <td>407.0</td>\n",
       "      <td>426.4</td>\n",
       "      <td>430.6</td>\n",
       "      <td>425.2</td>\n",
       "      <td>407.7</td>\n",
       "      <td>384.2</td>\n",
       "      <td>356.9</td>\n",
       "      <td>324.8</td>\n",
       "      <td>291.1</td>\n",
       "      <td>255.9</td>\n",
       "      <td>221.5</td>\n",
       "      <td>185.2</td>\n",
       "      <td>170.8</td>\n",
       "      <td>166.5</td>\n",
       "      <td>168.8</td>\n",
       "      <td>176.2</td>\n",
       "      <td>173.2</td>\n",
       "      <td>163.2</td>\n",
       "      <td>160.1</td>\n",
       "      <td>166.3</td>\n",
       "      <td>182.3</td>\n",
       "      <td>214.3</td>\n",
       "      <td>237.2</td>\n",
       "      <td>259.9</td>\n",
       "      <td>283.1</td>\n",
       "      <td>303.2</td>\n",
       "      <td>307.5</td>\n",
       "      <td>310.4</td>\n",
       "      <td>306.7</td>\n",
       "      <td>302.2</td>\n",
       "      <td>224.1</td>\n",
       "      <td>212.0</td>\n",
       "      <td>212.2</td>\n",
       "      <td>224.7</td>\n",
       "      <td>231.1</td>\n",
       "      <td>231.9</td>\n",
       "      <td>221.2</td>\n",
       "      <td>207.4</td>\n",
       "      <td>207.0</td>\n",
       "      <td>216.7</td>\n",
       "      <td>225.2</td>\n",
       "      <td>226.2</td>\n",
       "      <td>361.3</td>\n",
       "      <td>343.9</td>\n",
       "      <td>334.6</td>\n",
       "      <td>337.6</td>\n",
       "      <td>333.8</td>\n",
       "      <td>341.8</td>\n",
       "      <td>356.2</td>\n",
       "      <td>368.3</td>\n",
       "      <td>372.3</td>\n",
       "      <td>373.1</td>\n",
       "      <td>372.7</td>\n",
       "      <td>369.6</td>\n",
       "      <td>359.8</td>\n",
       "      <td>351.5</td>\n",
       "      <td>351.3</td>\n",
       "      <td>350.5</td>\n",
       "      <td>355.7</td>\n",
       "      <td>352.5</td>\n",
       "      <td>353.3</td>\n",
       "      <td>352.6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            filename    x_0    x_1    x_2    x_3    x_4    x_5    x_6    x_7  \\\n",
       "0  S005_001_00000001  213.7  214.1  217.7  223.7  236.9  262.2  299.3  340.3   \n",
       "1  S005_001_00000011  215.0  214.6  217.3  223.4  236.9  262.7  300.6  340.6   \n",
       "2  S010_001_00000001  261.3  263.7  268.2  274.2  284.0  301.3  324.9  353.6   \n",
       "3  S010_002_00000001  259.5  262.2  267.2  273.4  283.1  299.9  322.9  351.0   \n",
       "4  S010_002_00000014  263.7  263.4  267.1  273.7  282.1  296.9  318.3  349.5   \n",
       "\n",
       "     x_8    x_9   x_10   x_11   x_12   x_13   x_14   x_15   x_16   x_17  \\\n",
       "0  381.0  418.1  450.7  478.6  500.2  512.2  516.9  519.3  518.9  248.8   \n",
       "1  380.5  417.6  450.0  478.0  500.1  512.6  517.2  519.1  517.9  246.5   \n",
       "2  385.7  416.3  442.0  464.6  483.1  493.6  497.9  500.3  500.1  270.5   \n",
       "3  383.3  414.3  440.2  462.9  480.9  490.9  495.2  498.0  498.2  268.5   \n",
       "4  383.5  414.7  439.3  461.9  479.3  489.9  496.3  500.3  500.9  274.9   \n",
       "\n",
       "    x_18   x_19   x_20   x_21   x_22   x_23   x_24   x_25   x_26   x_27  \\\n",
       "0  266.1  291.1  317.6  342.6  395.8  419.9  444.0  468.1  486.5  371.5   \n",
       "1  265.0  291.8  318.3  342.9  397.5  421.1  445.2  469.9  489.9  371.8   \n",
       "2  285.1  305.9  327.5  348.4  403.4  426.6  448.4  469.5  485.1  377.4   \n",
       "3  282.7  303.2  324.6  345.4  401.8  424.9  446.6  467.4  482.8  375.2   \n",
       "4  289.6  310.1  331.4  349.8  401.9  426.0  449.0  469.7  483.7  375.5   \n",
       "\n",
       "    x_28   x_29   x_30   x_31   x_32   x_33   x_34   x_35   x_36   x_37  \\\n",
       "0  373.3  375.0  376.5  348.3  362.3  376.3  389.9  402.4  282.8  298.9   \n",
       "1  373.4  374.8  376.0  346.5  360.8  375.5  389.5  402.4  276.3  293.4   \n",
       "2  378.4  379.3  380.3  354.5  367.4  380.8  394.3  406.4  295.1  308.1   \n",
       "3  375.9  376.6  377.3  352.2  364.8  378.1  391.4  403.4  293.3  306.3   \n",
       "4  376.3  377.2  377.9  353.4  366.0  378.8  391.8  403.7  293.2  305.7   \n",
       "\n",
       "    x_38   x_39   x_40   x_41   x_42   x_43   x_44   x_45   x_46   x_47  \\\n",
       "0  316.8  332.1  317.0  299.2  408.5  423.0  440.6  456.2  442.6  425.6   \n",
       "1  311.6  328.7  312.0  293.9  412.7  429.4  447.3  463.2  448.9  431.3   \n",
       "2  325.7  341.0  325.7  308.7  414.4  428.7  446.1  460.2  447.7  431.0   \n",
       "3  323.8  339.0  323.8  306.8  412.3  426.6  444.0  458.0  445.5  428.8   \n",
       "4  325.4  341.1  325.1  305.9  412.4  427.1  446.2  459.8  448.7  430.0   \n",
       "\n",
       "    x_48   x_49   x_50   x_51   x_52   x_53   x_54   x_55   x_56   x_57  \\\n",
       "0  325.0  344.8  363.6  378.1  393.5  411.8  428.9  413.5  397.0  380.3   \n",
       "1  324.8  343.6  362.0  376.9  393.1  411.6  428.6  412.9  396.1  378.2   \n",
       "2  333.0  352.0  369.6  382.2  395.4  414.8  433.4  416.6  398.1  383.6   \n",
       "3  332.4  350.3  367.4  379.4  392.1  410.9  429.0  412.7  394.7  380.9   \n",
       "4  340.8  355.0  369.0  380.1  390.6  407.7  422.0  409.7  394.3  382.4   \n",
       "\n",
       "    x_58   x_59   x_60   x_61   x_62   x_63   x_64   x_65   x_66   x_67  \\\n",
       "0  364.7  345.6  334.5  363.7  378.3  394.1  420.5  394.3  378.3  363.6   \n",
       "1  362.2  343.9  334.7  361.7  376.9  393.4  419.8  393.7  376.7  361.4   \n",
       "2  370.0  352.0  340.9  369.7  382.6  396.2  426.0  396.1  382.4  369.5   \n",
       "3  367.9  350.5  340.1  367.6  379.9  392.9  421.6  392.8  379.8  367.5   \n",
       "4  370.4  355.8  349.7  369.2  380.5  391.3  414.0  391.4  380.5  369.1   \n",
       "\n",
       "     y_0    y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9  \\\n",
       "0  207.1  245.8  285.0  323.0  356.7  384.4  403.1  416.4  419.9  415.9   \n",
       "1  209.0  246.4  285.1  322.8  357.5  385.2  403.3  416.7  420.7  417.0   \n",
       "2  239.5  270.1  299.7  328.0  354.4  377.5  396.3  411.8  415.8  411.4   \n",
       "3  240.9  271.5  301.0  329.1  355.8  379.4  398.9  415.0  419.3  414.3   \n",
       "4  235.3  265.2  294.8  324.4  353.9  382.1  407.0  426.4  430.6  425.2   \n",
       "\n",
       "    y_10   y_11   y_12   y_13   y_14   y_15   y_16   y_17   y_18   y_19  \\\n",
       "0  403.2  386.6  361.8  327.5  287.0  243.7  200.5  179.2  162.5  157.1   \n",
       "1  404.3  388.3  364.4  330.6  289.7  245.6  201.9  181.8  166.1  163.3   \n",
       "2  396.9  378.3  354.6  325.0  293.1  259.9  226.6  203.7  191.6  187.9   \n",
       "3  398.8  379.0  354.5  325.0  293.7  261.2  228.5  205.9  194.1  190.4   \n",
       "4  407.7  384.2  356.9  324.8  291.1  255.9  221.5  185.2  170.8  166.5   \n",
       "\n",
       "    y_20   y_21   y_22   y_23   y_24   y_25   y_26   y_27   y_28   y_29  \\\n",
       "0  160.4  168.0  164.6  154.3  149.9  152.9  166.5  198.0  221.9  245.4   \n",
       "1  168.4  176.7  172.2  160.7  154.0  154.3  166.7  197.2  219.9  242.6   \n",
       "2  189.8  195.1  193.3  185.1  182.1  185.1  196.2  221.7  244.2  266.7   \n",
       "3  192.6  198.1  196.4  188.4  185.2  187.8  198.5  224.3  246.9  269.5   \n",
       "4  168.8  176.2  173.2  163.2  160.1  166.3  182.3  214.3  237.2  259.9   \n",
       "\n",
       "    y_30   y_31   y_32   y_33   y_34   y_35   y_36   y_37   y_38   y_39  \\\n",
       "0  269.8  288.2  292.2  295.3  291.4  287.1  203.9  195.5  194.9  204.8   \n",
       "1  266.2  283.0  287.8  291.0  287.2  282.9  202.9  195.4  194.4  203.1   \n",
       "2  289.6  303.6  308.5  312.1  307.4  302.2  228.6  218.9  218.1  227.9   \n",
       "3  292.6  306.1  311.1  314.6  310.0  304.6  230.3  220.9  220.1  229.8   \n",
       "4  283.1  303.2  307.5  310.4  306.7  302.2  224.1  212.0  212.2  224.7   \n",
       "\n",
       "    y_40   y_41   y_42   y_43   y_44   y_45   y_46   y_47   y_48   y_49  \\\n",
       "0  208.1  208.6  201.5  190.1  189.6  196.6  202.4  203.6  338.9  325.0   \n",
       "1  206.0  206.8  199.3  188.8  188.5  194.5  199.9  200.8  333.2  318.5   \n",
       "2  232.4  233.4  225.3  214.1  214.0  221.9  227.7  228.2  346.1  339.3   \n",
       "3  234.3  235.3  227.5  216.4  216.4  224.1  230.1  230.5  349.5  342.3   \n",
       "4  231.1  231.9  221.2  207.4  207.0  216.7  225.2  226.2  361.3  343.9   \n",
       "\n",
       "    y_50   y_51   y_52   y_53   y_54   y_55   y_56   y_57   y_58   y_59  \\\n",
       "0  318.1  321.4  317.5  323.9  334.6  348.8  355.4  357.2  356.4  351.8   \n",
       "1  311.6  315.1  310.9  318.5  330.5  344.2  351.4  353.5  352.4  346.9   \n",
       "2  335.8  338.5  334.8  337.8  342.4  357.0  363.9  365.4  364.5  358.9   \n",
       "3  338.6  341.3  337.7  340.9  345.9  360.5  367.3  368.8  367.9  362.4   \n",
       "4  334.6  337.6  333.8  341.8  356.2  368.3  372.3  373.1  372.7  369.6   \n",
       "\n",
       "    y_60   y_61   y_62   y_63   y_64   y_65   y_66   y_67  label  \n",
       "0  337.6  333.1  333.6  331.9  335.1  335.0  336.4  335.8      0  \n",
       "1  331.7  326.9  327.5  326.1  330.3  330.2  332.0  331.3      3  \n",
       "2  346.7  346.8  347.5  345.6  343.6  346.9  348.3  347.3      0  \n",
       "3  349.9  349.6  350.3  348.6  347.1  350.1  351.6  350.6      0  \n",
       "4  359.8  351.5  351.3  350.5  355.7  352.5  353.3  352.6      7  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load train dataset in the noteBook\n",
    "trainset = pd.read_csv('trainset/trainset.csv')\n",
    "testset = pd.read_csv('testset/testset.csv')\n",
    "# This is to print all the rows and columns when using head\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', trainset.shape[0]+1)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', testset.shape[0]+1)\n",
    "trainset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>x_0</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>x_8</th>\n",
       "      <th>x_9</th>\n",
       "      <th>x_10</th>\n",
       "      <th>x_11</th>\n",
       "      <th>x_12</th>\n",
       "      <th>x_13</th>\n",
       "      <th>x_14</th>\n",
       "      <th>x_15</th>\n",
       "      <th>x_16</th>\n",
       "      <th>x_17</th>\n",
       "      <th>x_18</th>\n",
       "      <th>x_19</th>\n",
       "      <th>x_20</th>\n",
       "      <th>x_21</th>\n",
       "      <th>x_22</th>\n",
       "      <th>x_23</th>\n",
       "      <th>x_24</th>\n",
       "      <th>x_25</th>\n",
       "      <th>x_26</th>\n",
       "      <th>x_27</th>\n",
       "      <th>x_28</th>\n",
       "      <th>x_29</th>\n",
       "      <th>x_30</th>\n",
       "      <th>x_31</th>\n",
       "      <th>x_32</th>\n",
       "      <th>x_33</th>\n",
       "      <th>x_34</th>\n",
       "      <th>x_35</th>\n",
       "      <th>x_36</th>\n",
       "      <th>x_37</th>\n",
       "      <th>x_38</th>\n",
       "      <th>x_39</th>\n",
       "      <th>x_40</th>\n",
       "      <th>x_41</th>\n",
       "      <th>x_42</th>\n",
       "      <th>x_43</th>\n",
       "      <th>x_44</th>\n",
       "      <th>x_45</th>\n",
       "      <th>x_46</th>\n",
       "      <th>x_47</th>\n",
       "      <th>x_48</th>\n",
       "      <th>x_49</th>\n",
       "      <th>x_50</th>\n",
       "      <th>x_51</th>\n",
       "      <th>x_52</th>\n",
       "      <th>x_53</th>\n",
       "      <th>x_54</th>\n",
       "      <th>x_55</th>\n",
       "      <th>x_56</th>\n",
       "      <th>x_57</th>\n",
       "      <th>x_58</th>\n",
       "      <th>x_59</th>\n",
       "      <th>x_60</th>\n",
       "      <th>x_61</th>\n",
       "      <th>x_62</th>\n",
       "      <th>x_63</th>\n",
       "      <th>x_64</th>\n",
       "      <th>x_65</th>\n",
       "      <th>x_66</th>\n",
       "      <th>x_67</th>\n",
       "      <th>y_0</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "      <th>y_4</th>\n",
       "      <th>y_5</th>\n",
       "      <th>y_6</th>\n",
       "      <th>y_7</th>\n",
       "      <th>y_8</th>\n",
       "      <th>y_9</th>\n",
       "      <th>y_10</th>\n",
       "      <th>y_11</th>\n",
       "      <th>y_12</th>\n",
       "      <th>y_13</th>\n",
       "      <th>y_14</th>\n",
       "      <th>y_15</th>\n",
       "      <th>y_16</th>\n",
       "      <th>y_17</th>\n",
       "      <th>y_18</th>\n",
       "      <th>y_19</th>\n",
       "      <th>y_20</th>\n",
       "      <th>y_21</th>\n",
       "      <th>y_22</th>\n",
       "      <th>y_23</th>\n",
       "      <th>y_24</th>\n",
       "      <th>y_25</th>\n",
       "      <th>y_26</th>\n",
       "      <th>y_27</th>\n",
       "      <th>y_28</th>\n",
       "      <th>y_29</th>\n",
       "      <th>y_30</th>\n",
       "      <th>y_31</th>\n",
       "      <th>y_32</th>\n",
       "      <th>y_33</th>\n",
       "      <th>y_34</th>\n",
       "      <th>y_35</th>\n",
       "      <th>y_36</th>\n",
       "      <th>y_37</th>\n",
       "      <th>y_38</th>\n",
       "      <th>y_39</th>\n",
       "      <th>y_40</th>\n",
       "      <th>y_41</th>\n",
       "      <th>y_42</th>\n",
       "      <th>y_43</th>\n",
       "      <th>y_44</th>\n",
       "      <th>y_45</th>\n",
       "      <th>y_46</th>\n",
       "      <th>y_47</th>\n",
       "      <th>y_48</th>\n",
       "      <th>y_49</th>\n",
       "      <th>y_50</th>\n",
       "      <th>y_51</th>\n",
       "      <th>y_52</th>\n",
       "      <th>y_53</th>\n",
       "      <th>y_54</th>\n",
       "      <th>y_55</th>\n",
       "      <th>y_56</th>\n",
       "      <th>y_57</th>\n",
       "      <th>y_58</th>\n",
       "      <th>y_59</th>\n",
       "      <th>y_60</th>\n",
       "      <th>y_61</th>\n",
       "      <th>y_62</th>\n",
       "      <th>y_63</th>\n",
       "      <th>y_64</th>\n",
       "      <th>y_65</th>\n",
       "      <th>y_66</th>\n",
       "      <th>y_67</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S032_001_00000001</td>\n",
       "      <td>255.4</td>\n",
       "      <td>256.7</td>\n",
       "      <td>259.6</td>\n",
       "      <td>263.3</td>\n",
       "      <td>270.9</td>\n",
       "      <td>284.3</td>\n",
       "      <td>299.8</td>\n",
       "      <td>318.2</td>\n",
       "      <td>341.8</td>\n",
       "      <td>366.9</td>\n",
       "      <td>391.1</td>\n",
       "      <td>410.7</td>\n",
       "      <td>424.2</td>\n",
       "      <td>431.1</td>\n",
       "      <td>434.0</td>\n",
       "      <td>436.2</td>\n",
       "      <td>437.3</td>\n",
       "      <td>265.8</td>\n",
       "      <td>277.4</td>\n",
       "      <td>292.8</td>\n",
       "      <td>308.4</td>\n",
       "      <td>323.6</td>\n",
       "      <td>353.0</td>\n",
       "      <td>369.6</td>\n",
       "      <td>385.9</td>\n",
       "      <td>402.3</td>\n",
       "      <td>415.3</td>\n",
       "      <td>339.0</td>\n",
       "      <td>338.1</td>\n",
       "      <td>337.0</td>\n",
       "      <td>336.1</td>\n",
       "      <td>319.3</td>\n",
       "      <td>328.4</td>\n",
       "      <td>337.5</td>\n",
       "      <td>347.4</td>\n",
       "      <td>356.5</td>\n",
       "      <td>284.4</td>\n",
       "      <td>294.1</td>\n",
       "      <td>305.3</td>\n",
       "      <td>315.2</td>\n",
       "      <td>304.5</td>\n",
       "      <td>293.5</td>\n",
       "      <td>365.0</td>\n",
       "      <td>375.6</td>\n",
       "      <td>387.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>387.1</td>\n",
       "      <td>376.1</td>\n",
       "      <td>301.9</td>\n",
       "      <td>314.4</td>\n",
       "      <td>327.5</td>\n",
       "      <td>337.0</td>\n",
       "      <td>348.1</td>\n",
       "      <td>363.1</td>\n",
       "      <td>378.8</td>\n",
       "      <td>362.9</td>\n",
       "      <td>347.8</td>\n",
       "      <td>336.0</td>\n",
       "      <td>325.8</td>\n",
       "      <td>313.5</td>\n",
       "      <td>307.2</td>\n",
       "      <td>327.5</td>\n",
       "      <td>337.2</td>\n",
       "      <td>348.6</td>\n",
       "      <td>372.4</td>\n",
       "      <td>347.8</td>\n",
       "      <td>336.3</td>\n",
       "      <td>326.7</td>\n",
       "      <td>224.5</td>\n",
       "      <td>248.5</td>\n",
       "      <td>272.8</td>\n",
       "      <td>296.3</td>\n",
       "      <td>318.4</td>\n",
       "      <td>337.7</td>\n",
       "      <td>352.9</td>\n",
       "      <td>364.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>363.4</td>\n",
       "      <td>351.9</td>\n",
       "      <td>336.6</td>\n",
       "      <td>317.3</td>\n",
       "      <td>294.8</td>\n",
       "      <td>271.2</td>\n",
       "      <td>248.3</td>\n",
       "      <td>225.6</td>\n",
       "      <td>206.2</td>\n",
       "      <td>195.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>193.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>199.3</td>\n",
       "      <td>194.3</td>\n",
       "      <td>193.2</td>\n",
       "      <td>195.3</td>\n",
       "      <td>204.0</td>\n",
       "      <td>214.8</td>\n",
       "      <td>227.3</td>\n",
       "      <td>239.7</td>\n",
       "      <td>253.0</td>\n",
       "      <td>269.6</td>\n",
       "      <td>271.9</td>\n",
       "      <td>274.1</td>\n",
       "      <td>271.8</td>\n",
       "      <td>269.3</td>\n",
       "      <td>220.9</td>\n",
       "      <td>215.8</td>\n",
       "      <td>215.5</td>\n",
       "      <td>220.9</td>\n",
       "      <td>223.2</td>\n",
       "      <td>223.6</td>\n",
       "      <td>221.2</td>\n",
       "      <td>215.6</td>\n",
       "      <td>216.3</td>\n",
       "      <td>221.2</td>\n",
       "      <td>223.8</td>\n",
       "      <td>223.5</td>\n",
       "      <td>303.1</td>\n",
       "      <td>294.9</td>\n",
       "      <td>291.4</td>\n",
       "      <td>293.6</td>\n",
       "      <td>291.5</td>\n",
       "      <td>295.4</td>\n",
       "      <td>302.8</td>\n",
       "      <td>310.5</td>\n",
       "      <td>313.5</td>\n",
       "      <td>314.4</td>\n",
       "      <td>313.9</td>\n",
       "      <td>311.1</td>\n",
       "      <td>303.2</td>\n",
       "      <td>300.3</td>\n",
       "      <td>301.1</td>\n",
       "      <td>300.0</td>\n",
       "      <td>302.9</td>\n",
       "      <td>301.6</td>\n",
       "      <td>302.6</td>\n",
       "      <td>302.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S032_001_00000022</td>\n",
       "      <td>254.3</td>\n",
       "      <td>254.9</td>\n",
       "      <td>258.6</td>\n",
       "      <td>262.3</td>\n",
       "      <td>268.5</td>\n",
       "      <td>278.8</td>\n",
       "      <td>292.0</td>\n",
       "      <td>309.9</td>\n",
       "      <td>332.7</td>\n",
       "      <td>357.3</td>\n",
       "      <td>381.8</td>\n",
       "      <td>402.6</td>\n",
       "      <td>416.8</td>\n",
       "      <td>424.7</td>\n",
       "      <td>429.5</td>\n",
       "      <td>433.1</td>\n",
       "      <td>435.0</td>\n",
       "      <td>262.1</td>\n",
       "      <td>273.9</td>\n",
       "      <td>289.1</td>\n",
       "      <td>304.0</td>\n",
       "      <td>317.8</td>\n",
       "      <td>350.6</td>\n",
       "      <td>365.9</td>\n",
       "      <td>381.1</td>\n",
       "      <td>397.0</td>\n",
       "      <td>410.3</td>\n",
       "      <td>334.3</td>\n",
       "      <td>332.8</td>\n",
       "      <td>331.3</td>\n",
       "      <td>329.7</td>\n",
       "      <td>315.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>331.1</td>\n",
       "      <td>340.4</td>\n",
       "      <td>349.4</td>\n",
       "      <td>280.1</td>\n",
       "      <td>289.7</td>\n",
       "      <td>302.5</td>\n",
       "      <td>312.8</td>\n",
       "      <td>301.1</td>\n",
       "      <td>288.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>371.5</td>\n",
       "      <td>384.3</td>\n",
       "      <td>394.2</td>\n",
       "      <td>384.9</td>\n",
       "      <td>372.0</td>\n",
       "      <td>302.9</td>\n",
       "      <td>310.4</td>\n",
       "      <td>321.8</td>\n",
       "      <td>330.1</td>\n",
       "      <td>339.7</td>\n",
       "      <td>351.7</td>\n",
       "      <td>363.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>339.3</td>\n",
       "      <td>328.9</td>\n",
       "      <td>320.0</td>\n",
       "      <td>309.3</td>\n",
       "      <td>308.6</td>\n",
       "      <td>321.6</td>\n",
       "      <td>329.8</td>\n",
       "      <td>339.7</td>\n",
       "      <td>355.8</td>\n",
       "      <td>339.6</td>\n",
       "      <td>329.6</td>\n",
       "      <td>321.1</td>\n",
       "      <td>204.0</td>\n",
       "      <td>232.1</td>\n",
       "      <td>260.7</td>\n",
       "      <td>288.6</td>\n",
       "      <td>317.0</td>\n",
       "      <td>342.9</td>\n",
       "      <td>365.5</td>\n",
       "      <td>380.4</td>\n",
       "      <td>384.7</td>\n",
       "      <td>382.0</td>\n",
       "      <td>370.1</td>\n",
       "      <td>351.0</td>\n",
       "      <td>326.9</td>\n",
       "      <td>298.5</td>\n",
       "      <td>268.8</td>\n",
       "      <td>238.8</td>\n",
       "      <td>209.3</td>\n",
       "      <td>189.9</td>\n",
       "      <td>177.3</td>\n",
       "      <td>174.4</td>\n",
       "      <td>177.2</td>\n",
       "      <td>182.7</td>\n",
       "      <td>182.5</td>\n",
       "      <td>177.9</td>\n",
       "      <td>176.1</td>\n",
       "      <td>177.6</td>\n",
       "      <td>187.2</td>\n",
       "      <td>200.8</td>\n",
       "      <td>213.6</td>\n",
       "      <td>226.3</td>\n",
       "      <td>240.0</td>\n",
       "      <td>259.1</td>\n",
       "      <td>261.2</td>\n",
       "      <td>263.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>258.7</td>\n",
       "      <td>208.6</td>\n",
       "      <td>201.4</td>\n",
       "      <td>201.6</td>\n",
       "      <td>208.5</td>\n",
       "      <td>212.2</td>\n",
       "      <td>212.6</td>\n",
       "      <td>209.5</td>\n",
       "      <td>202.1</td>\n",
       "      <td>202.8</td>\n",
       "      <td>209.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>212.7</td>\n",
       "      <td>301.8</td>\n",
       "      <td>284.8</td>\n",
       "      <td>277.9</td>\n",
       "      <td>280.4</td>\n",
       "      <td>278.2</td>\n",
       "      <td>285.6</td>\n",
       "      <td>300.6</td>\n",
       "      <td>310.3</td>\n",
       "      <td>313.6</td>\n",
       "      <td>314.7</td>\n",
       "      <td>314.3</td>\n",
       "      <td>310.8</td>\n",
       "      <td>300.4</td>\n",
       "      <td>289.0</td>\n",
       "      <td>289.2</td>\n",
       "      <td>289.0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>300.5</td>\n",
       "      <td>301.6</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S032_002_00000001</td>\n",
       "      <td>257.8</td>\n",
       "      <td>258.9</td>\n",
       "      <td>261.7</td>\n",
       "      <td>265.2</td>\n",
       "      <td>272.7</td>\n",
       "      <td>285.8</td>\n",
       "      <td>301.4</td>\n",
       "      <td>319.7</td>\n",
       "      <td>343.3</td>\n",
       "      <td>368.8</td>\n",
       "      <td>393.1</td>\n",
       "      <td>412.8</td>\n",
       "      <td>426.3</td>\n",
       "      <td>433.0</td>\n",
       "      <td>435.7</td>\n",
       "      <td>437.6</td>\n",
       "      <td>438.3</td>\n",
       "      <td>268.0</td>\n",
       "      <td>280.1</td>\n",
       "      <td>295.1</td>\n",
       "      <td>310.2</td>\n",
       "      <td>325.0</td>\n",
       "      <td>355.4</td>\n",
       "      <td>371.9</td>\n",
       "      <td>387.9</td>\n",
       "      <td>403.9</td>\n",
       "      <td>416.4</td>\n",
       "      <td>341.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>338.9</td>\n",
       "      <td>338.0</td>\n",
       "      <td>321.3</td>\n",
       "      <td>330.2</td>\n",
       "      <td>339.1</td>\n",
       "      <td>348.9</td>\n",
       "      <td>357.9</td>\n",
       "      <td>286.0</td>\n",
       "      <td>295.5</td>\n",
       "      <td>306.8</td>\n",
       "      <td>316.6</td>\n",
       "      <td>305.9</td>\n",
       "      <td>294.8</td>\n",
       "      <td>366.3</td>\n",
       "      <td>376.8</td>\n",
       "      <td>388.2</td>\n",
       "      <td>398.1</td>\n",
       "      <td>388.4</td>\n",
       "      <td>377.3</td>\n",
       "      <td>303.6</td>\n",
       "      <td>316.0</td>\n",
       "      <td>329.1</td>\n",
       "      <td>338.4</td>\n",
       "      <td>349.2</td>\n",
       "      <td>364.1</td>\n",
       "      <td>379.7</td>\n",
       "      <td>363.7</td>\n",
       "      <td>348.7</td>\n",
       "      <td>337.2</td>\n",
       "      <td>327.2</td>\n",
       "      <td>315.0</td>\n",
       "      <td>308.8</td>\n",
       "      <td>329.1</td>\n",
       "      <td>338.6</td>\n",
       "      <td>349.8</td>\n",
       "      <td>373.4</td>\n",
       "      <td>348.9</td>\n",
       "      <td>337.7</td>\n",
       "      <td>328.3</td>\n",
       "      <td>219.8</td>\n",
       "      <td>244.1</td>\n",
       "      <td>268.9</td>\n",
       "      <td>292.8</td>\n",
       "      <td>315.1</td>\n",
       "      <td>334.3</td>\n",
       "      <td>349.4</td>\n",
       "      <td>360.3</td>\n",
       "      <td>363.2</td>\n",
       "      <td>359.7</td>\n",
       "      <td>348.2</td>\n",
       "      <td>332.7</td>\n",
       "      <td>313.4</td>\n",
       "      <td>290.8</td>\n",
       "      <td>267.5</td>\n",
       "      <td>244.8</td>\n",
       "      <td>222.4</td>\n",
       "      <td>200.1</td>\n",
       "      <td>190.2</td>\n",
       "      <td>186.9</td>\n",
       "      <td>188.6</td>\n",
       "      <td>193.6</td>\n",
       "      <td>193.9</td>\n",
       "      <td>189.6</td>\n",
       "      <td>188.7</td>\n",
       "      <td>191.0</td>\n",
       "      <td>199.6</td>\n",
       "      <td>210.2</td>\n",
       "      <td>222.7</td>\n",
       "      <td>235.3</td>\n",
       "      <td>248.6</td>\n",
       "      <td>265.5</td>\n",
       "      <td>267.7</td>\n",
       "      <td>269.8</td>\n",
       "      <td>267.7</td>\n",
       "      <td>265.1</td>\n",
       "      <td>216.1</td>\n",
       "      <td>210.8</td>\n",
       "      <td>210.6</td>\n",
       "      <td>216.4</td>\n",
       "      <td>218.6</td>\n",
       "      <td>218.9</td>\n",
       "      <td>217.0</td>\n",
       "      <td>211.2</td>\n",
       "      <td>211.8</td>\n",
       "      <td>217.1</td>\n",
       "      <td>219.6</td>\n",
       "      <td>219.2</td>\n",
       "      <td>298.8</td>\n",
       "      <td>290.5</td>\n",
       "      <td>286.9</td>\n",
       "      <td>289.1</td>\n",
       "      <td>287.1</td>\n",
       "      <td>291.2</td>\n",
       "      <td>298.8</td>\n",
       "      <td>306.4</td>\n",
       "      <td>309.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>309.4</td>\n",
       "      <td>306.6</td>\n",
       "      <td>299.0</td>\n",
       "      <td>295.9</td>\n",
       "      <td>296.6</td>\n",
       "      <td>295.8</td>\n",
       "      <td>298.9</td>\n",
       "      <td>297.3</td>\n",
       "      <td>298.2</td>\n",
       "      <td>297.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S032_003_00000001</td>\n",
       "      <td>259.3</td>\n",
       "      <td>260.3</td>\n",
       "      <td>262.9</td>\n",
       "      <td>266.4</td>\n",
       "      <td>273.8</td>\n",
       "      <td>286.9</td>\n",
       "      <td>302.2</td>\n",
       "      <td>320.8</td>\n",
       "      <td>344.5</td>\n",
       "      <td>369.6</td>\n",
       "      <td>393.5</td>\n",
       "      <td>413.1</td>\n",
       "      <td>426.7</td>\n",
       "      <td>433.8</td>\n",
       "      <td>436.9</td>\n",
       "      <td>439.3</td>\n",
       "      <td>440.5</td>\n",
       "      <td>270.6</td>\n",
       "      <td>282.5</td>\n",
       "      <td>297.9</td>\n",
       "      <td>313.6</td>\n",
       "      <td>328.8</td>\n",
       "      <td>358.9</td>\n",
       "      <td>375.8</td>\n",
       "      <td>392.1</td>\n",
       "      <td>408.3</td>\n",
       "      <td>420.6</td>\n",
       "      <td>344.4</td>\n",
       "      <td>343.5</td>\n",
       "      <td>342.5</td>\n",
       "      <td>341.6</td>\n",
       "      <td>324.1</td>\n",
       "      <td>333.1</td>\n",
       "      <td>342.2</td>\n",
       "      <td>352.2</td>\n",
       "      <td>361.3</td>\n",
       "      <td>288.9</td>\n",
       "      <td>298.6</td>\n",
       "      <td>309.8</td>\n",
       "      <td>319.6</td>\n",
       "      <td>309.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>369.9</td>\n",
       "      <td>380.4</td>\n",
       "      <td>391.7</td>\n",
       "      <td>401.5</td>\n",
       "      <td>391.8</td>\n",
       "      <td>380.8</td>\n",
       "      <td>306.2</td>\n",
       "      <td>319.1</td>\n",
       "      <td>332.2</td>\n",
       "      <td>341.5</td>\n",
       "      <td>352.3</td>\n",
       "      <td>366.9</td>\n",
       "      <td>381.9</td>\n",
       "      <td>366.5</td>\n",
       "      <td>351.8</td>\n",
       "      <td>340.3</td>\n",
       "      <td>330.3</td>\n",
       "      <td>318.0</td>\n",
       "      <td>311.6</td>\n",
       "      <td>332.1</td>\n",
       "      <td>341.6</td>\n",
       "      <td>352.6</td>\n",
       "      <td>375.6</td>\n",
       "      <td>351.9</td>\n",
       "      <td>340.8</td>\n",
       "      <td>331.4</td>\n",
       "      <td>219.9</td>\n",
       "      <td>244.2</td>\n",
       "      <td>268.9</td>\n",
       "      <td>292.6</td>\n",
       "      <td>315.0</td>\n",
       "      <td>334.5</td>\n",
       "      <td>350.1</td>\n",
       "      <td>361.8</td>\n",
       "      <td>365.3</td>\n",
       "      <td>362.0</td>\n",
       "      <td>350.5</td>\n",
       "      <td>335.0</td>\n",
       "      <td>315.7</td>\n",
       "      <td>293.2</td>\n",
       "      <td>269.8</td>\n",
       "      <td>246.8</td>\n",
       "      <td>224.1</td>\n",
       "      <td>202.4</td>\n",
       "      <td>192.2</td>\n",
       "      <td>188.9</td>\n",
       "      <td>191.1</td>\n",
       "      <td>196.4</td>\n",
       "      <td>197.1</td>\n",
       "      <td>192.7</td>\n",
       "      <td>191.8</td>\n",
       "      <td>194.5</td>\n",
       "      <td>203.6</td>\n",
       "      <td>213.4</td>\n",
       "      <td>226.1</td>\n",
       "      <td>238.9</td>\n",
       "      <td>252.5</td>\n",
       "      <td>268.3</td>\n",
       "      <td>270.9</td>\n",
       "      <td>273.4</td>\n",
       "      <td>271.2</td>\n",
       "      <td>268.6</td>\n",
       "      <td>218.2</td>\n",
       "      <td>213.3</td>\n",
       "      <td>213.1</td>\n",
       "      <td>218.6</td>\n",
       "      <td>220.9</td>\n",
       "      <td>221.1</td>\n",
       "      <td>219.7</td>\n",
       "      <td>214.4</td>\n",
       "      <td>215.3</td>\n",
       "      <td>220.4</td>\n",
       "      <td>222.8</td>\n",
       "      <td>222.3</td>\n",
       "      <td>302.2</td>\n",
       "      <td>294.5</td>\n",
       "      <td>291.3</td>\n",
       "      <td>293.7</td>\n",
       "      <td>291.8</td>\n",
       "      <td>295.8</td>\n",
       "      <td>303.2</td>\n",
       "      <td>310.8</td>\n",
       "      <td>313.6</td>\n",
       "      <td>314.3</td>\n",
       "      <td>313.5</td>\n",
       "      <td>310.4</td>\n",
       "      <td>302.5</td>\n",
       "      <td>300.1</td>\n",
       "      <td>301.0</td>\n",
       "      <td>300.2</td>\n",
       "      <td>303.3</td>\n",
       "      <td>301.7</td>\n",
       "      <td>302.5</td>\n",
       "      <td>301.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S032_003_00000017</td>\n",
       "      <td>257.8</td>\n",
       "      <td>258.9</td>\n",
       "      <td>261.5</td>\n",
       "      <td>264.6</td>\n",
       "      <td>271.9</td>\n",
       "      <td>285.0</td>\n",
       "      <td>300.2</td>\n",
       "      <td>317.9</td>\n",
       "      <td>340.7</td>\n",
       "      <td>365.6</td>\n",
       "      <td>389.9</td>\n",
       "      <td>409.9</td>\n",
       "      <td>424.0</td>\n",
       "      <td>431.6</td>\n",
       "      <td>435.1</td>\n",
       "      <td>437.8</td>\n",
       "      <td>439.5</td>\n",
       "      <td>267.8</td>\n",
       "      <td>279.9</td>\n",
       "      <td>296.2</td>\n",
       "      <td>312.2</td>\n",
       "      <td>327.3</td>\n",
       "      <td>352.9</td>\n",
       "      <td>370.2</td>\n",
       "      <td>387.4</td>\n",
       "      <td>404.8</td>\n",
       "      <td>418.3</td>\n",
       "      <td>340.1</td>\n",
       "      <td>338.7</td>\n",
       "      <td>337.2</td>\n",
       "      <td>335.9</td>\n",
       "      <td>319.7</td>\n",
       "      <td>328.1</td>\n",
       "      <td>336.7</td>\n",
       "      <td>346.3</td>\n",
       "      <td>355.2</td>\n",
       "      <td>286.4</td>\n",
       "      <td>296.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>316.3</td>\n",
       "      <td>305.9</td>\n",
       "      <td>295.0</td>\n",
       "      <td>366.2</td>\n",
       "      <td>376.7</td>\n",
       "      <td>387.7</td>\n",
       "      <td>397.3</td>\n",
       "      <td>387.7</td>\n",
       "      <td>376.9</td>\n",
       "      <td>304.9</td>\n",
       "      <td>316.8</td>\n",
       "      <td>328.7</td>\n",
       "      <td>336.5</td>\n",
       "      <td>346.0</td>\n",
       "      <td>359.3</td>\n",
       "      <td>374.3</td>\n",
       "      <td>358.5</td>\n",
       "      <td>345.1</td>\n",
       "      <td>335.1</td>\n",
       "      <td>326.7</td>\n",
       "      <td>315.7</td>\n",
       "      <td>309.9</td>\n",
       "      <td>328.5</td>\n",
       "      <td>336.5</td>\n",
       "      <td>346.2</td>\n",
       "      <td>368.0</td>\n",
       "      <td>345.4</td>\n",
       "      <td>335.7</td>\n",
       "      <td>327.8</td>\n",
       "      <td>221.7</td>\n",
       "      <td>246.1</td>\n",
       "      <td>271.0</td>\n",
       "      <td>294.9</td>\n",
       "      <td>318.0</td>\n",
       "      <td>338.2</td>\n",
       "      <td>354.6</td>\n",
       "      <td>367.0</td>\n",
       "      <td>370.6</td>\n",
       "      <td>366.7</td>\n",
       "      <td>354.7</td>\n",
       "      <td>338.7</td>\n",
       "      <td>319.2</td>\n",
       "      <td>296.6</td>\n",
       "      <td>273.1</td>\n",
       "      <td>250.2</td>\n",
       "      <td>227.1</td>\n",
       "      <td>211.2</td>\n",
       "      <td>201.1</td>\n",
       "      <td>198.9</td>\n",
       "      <td>201.9</td>\n",
       "      <td>207.7</td>\n",
       "      <td>208.0</td>\n",
       "      <td>203.4</td>\n",
       "      <td>201.8</td>\n",
       "      <td>203.9</td>\n",
       "      <td>213.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>232.6</td>\n",
       "      <td>245.3</td>\n",
       "      <td>258.9</td>\n",
       "      <td>272.6</td>\n",
       "      <td>275.6</td>\n",
       "      <td>278.1</td>\n",
       "      <td>275.9</td>\n",
       "      <td>273.3</td>\n",
       "      <td>222.9</td>\n",
       "      <td>218.5</td>\n",
       "      <td>218.3</td>\n",
       "      <td>223.8</td>\n",
       "      <td>226.0</td>\n",
       "      <td>226.1</td>\n",
       "      <td>225.1</td>\n",
       "      <td>219.7</td>\n",
       "      <td>220.7</td>\n",
       "      <td>225.6</td>\n",
       "      <td>228.1</td>\n",
       "      <td>227.5</td>\n",
       "      <td>304.0</td>\n",
       "      <td>297.9</td>\n",
       "      <td>295.4</td>\n",
       "      <td>297.6</td>\n",
       "      <td>295.9</td>\n",
       "      <td>299.3</td>\n",
       "      <td>305.4</td>\n",
       "      <td>312.0</td>\n",
       "      <td>314.2</td>\n",
       "      <td>314.8</td>\n",
       "      <td>314.1</td>\n",
       "      <td>311.5</td>\n",
       "      <td>304.3</td>\n",
       "      <td>302.8</td>\n",
       "      <td>303.7</td>\n",
       "      <td>303.0</td>\n",
       "      <td>305.3</td>\n",
       "      <td>303.4</td>\n",
       "      <td>304.3</td>\n",
       "      <td>303.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            filename    x_0    x_1    x_2    x_3    x_4    x_5    x_6    x_7  \\\n",
       "0  S032_001_00000001  255.4  256.7  259.6  263.3  270.9  284.3  299.8  318.2   \n",
       "1  S032_001_00000022  254.3  254.9  258.6  262.3  268.5  278.8  292.0  309.9   \n",
       "2  S032_002_00000001  257.8  258.9  261.7  265.2  272.7  285.8  301.4  319.7   \n",
       "3  S032_003_00000001  259.3  260.3  262.9  266.4  273.8  286.9  302.2  320.8   \n",
       "4  S032_003_00000017  257.8  258.9  261.5  264.6  271.9  285.0  300.2  317.9   \n",
       "\n",
       "     x_8    x_9   x_10   x_11   x_12   x_13   x_14   x_15   x_16   x_17  \\\n",
       "0  341.8  366.9  391.1  410.7  424.2  431.1  434.0  436.2  437.3  265.8   \n",
       "1  332.7  357.3  381.8  402.6  416.8  424.7  429.5  433.1  435.0  262.1   \n",
       "2  343.3  368.8  393.1  412.8  426.3  433.0  435.7  437.6  438.3  268.0   \n",
       "3  344.5  369.6  393.5  413.1  426.7  433.8  436.9  439.3  440.5  270.6   \n",
       "4  340.7  365.6  389.9  409.9  424.0  431.6  435.1  437.8  439.5  267.8   \n",
       "\n",
       "    x_18   x_19   x_20   x_21   x_22   x_23   x_24   x_25   x_26   x_27  \\\n",
       "0  277.4  292.8  308.4  323.6  353.0  369.6  385.9  402.3  415.3  339.0   \n",
       "1  273.9  289.1  304.0  317.8  350.6  365.9  381.1  397.0  410.3  334.3   \n",
       "2  280.1  295.1  310.2  325.0  355.4  371.9  387.9  403.9  416.4  341.0   \n",
       "3  282.5  297.9  313.6  328.8  358.9  375.8  392.1  408.3  420.6  344.4   \n",
       "4  279.9  296.2  312.2  327.3  352.9  370.2  387.4  404.8  418.3  340.1   \n",
       "\n",
       "    x_28   x_29   x_30   x_31   x_32   x_33   x_34   x_35   x_36   x_37  \\\n",
       "0  338.1  337.0  336.1  319.3  328.4  337.5  347.4  356.5  284.4  294.1   \n",
       "1  332.8  331.3  329.7  315.0  323.0  331.1  340.4  349.4  280.1  289.7   \n",
       "2  340.0  338.9  338.0  321.3  330.2  339.1  348.9  357.9  286.0  295.5   \n",
       "3  343.5  342.5  341.6  324.1  333.1  342.2  352.2  361.3  288.9  298.6   \n",
       "4  338.7  337.2  335.9  319.7  328.1  336.7  346.3  355.2  286.4  296.0   \n",
       "\n",
       "    x_38   x_39   x_40   x_41   x_42   x_43   x_44   x_45   x_46   x_47  \\\n",
       "0  305.3  315.2  304.5  293.5  365.0  375.6  387.0  397.0  387.1  376.1   \n",
       "1  302.5  312.8  301.1  288.5  360.0  371.5  384.3  394.2  384.9  372.0   \n",
       "2  306.8  316.6  305.9  294.8  366.3  376.8  388.2  398.1  388.4  377.3   \n",
       "3  309.8  319.6  309.0  298.0  369.9  380.4  391.7  401.5  391.8  380.8   \n",
       "4  307.0  316.3  305.9  295.0  366.2  376.7  387.7  397.3  387.7  376.9   \n",
       "\n",
       "    x_48   x_49   x_50   x_51   x_52   x_53   x_54   x_55   x_56   x_57  \\\n",
       "0  301.9  314.4  327.5  337.0  348.1  363.1  378.8  362.9  347.8  336.0   \n",
       "1  302.9  310.4  321.8  330.1  339.7  351.7  363.0  351.0  339.3  328.9   \n",
       "2  303.6  316.0  329.1  338.4  349.2  364.1  379.7  363.7  348.7  337.2   \n",
       "3  306.2  319.1  332.2  341.5  352.3  366.9  381.9  366.5  351.8  340.3   \n",
       "4  304.9  316.8  328.7  336.5  346.0  359.3  374.3  358.5  345.1  335.1   \n",
       "\n",
       "    x_58   x_59   x_60   x_61   x_62   x_63   x_64   x_65   x_66   x_67  \\\n",
       "0  325.8  313.5  307.2  327.5  337.2  348.6  372.4  347.8  336.3  326.7   \n",
       "1  320.0  309.3  308.6  321.6  329.8  339.7  355.8  339.6  329.6  321.1   \n",
       "2  327.2  315.0  308.8  329.1  338.6  349.8  373.4  348.9  337.7  328.3   \n",
       "3  330.3  318.0  311.6  332.1  341.6  352.6  375.6  351.9  340.8  331.4   \n",
       "4  326.7  315.7  309.9  328.5  336.5  346.2  368.0  345.4  335.7  327.8   \n",
       "\n",
       "     y_0    y_1    y_2    y_3    y_4    y_5    y_6    y_7    y_8    y_9  \\\n",
       "0  224.5  248.5  272.8  296.3  318.4  337.7  352.9  364.0  367.0  363.4   \n",
       "1  204.0  232.1  260.7  288.6  317.0  342.9  365.5  380.4  384.7  382.0   \n",
       "2  219.8  244.1  268.9  292.8  315.1  334.3  349.4  360.3  363.2  359.7   \n",
       "3  219.9  244.2  268.9  292.6  315.0  334.5  350.1  361.8  365.3  362.0   \n",
       "4  221.7  246.1  271.0  294.9  318.0  338.2  354.6  367.0  370.6  366.7   \n",
       "\n",
       "    y_10   y_11   y_12   y_13   y_14   y_15   y_16   y_17   y_18   y_19  \\\n",
       "0  351.9  336.6  317.3  294.8  271.2  248.3  225.6  206.2  195.6  192.0   \n",
       "1  370.1  351.0  326.9  298.5  268.8  238.8  209.3  189.9  177.3  174.4   \n",
       "2  348.2  332.7  313.4  290.8  267.5  244.8  222.4  200.1  190.2  186.9   \n",
       "3  350.5  335.0  315.7  293.2  269.8  246.8  224.1  202.4  192.2  188.9   \n",
       "4  354.7  338.7  319.2  296.6  273.1  250.2  227.1  211.2  201.1  198.9   \n",
       "\n",
       "    y_20   y_21   y_22   y_23   y_24   y_25   y_26   y_27   y_28   y_29  \\\n",
       "0  193.9  199.0  199.3  194.3  193.2  195.3  204.0  214.8  227.3  239.7   \n",
       "1  177.2  182.7  182.5  177.9  176.1  177.6  187.2  200.8  213.6  226.3   \n",
       "2  188.6  193.6  193.9  189.6  188.7  191.0  199.6  210.2  222.7  235.3   \n",
       "3  191.1  196.4  197.1  192.7  191.8  194.5  203.6  213.4  226.1  238.9   \n",
       "4  201.9  207.7  208.0  203.4  201.8  203.9  213.1  220.0  232.6  245.3   \n",
       "\n",
       "    y_30   y_31   y_32   y_33   y_34   y_35   y_36   y_37   y_38   y_39  \\\n",
       "0  253.0  269.6  271.9  274.1  271.8  269.3  220.9  215.8  215.5  220.9   \n",
       "1  240.0  259.1  261.2  263.0  260.7  258.7  208.6  201.4  201.6  208.5   \n",
       "2  248.6  265.5  267.7  269.8  267.7  265.1  216.1  210.8  210.6  216.4   \n",
       "3  252.5  268.3  270.9  273.4  271.2  268.6  218.2  213.3  213.1  218.6   \n",
       "4  258.9  272.6  275.6  278.1  275.9  273.3  222.9  218.5  218.3  223.8   \n",
       "\n",
       "    y_40   y_41   y_42   y_43   y_44   y_45   y_46   y_47   y_48   y_49  \\\n",
       "0  223.2  223.6  221.2  215.6  216.3  221.2  223.8  223.5  303.1  294.9   \n",
       "1  212.2  212.6  209.5  202.1  202.8  209.0  213.0  212.7  301.8  284.8   \n",
       "2  218.6  218.9  217.0  211.2  211.8  217.1  219.6  219.2  298.8  290.5   \n",
       "3  220.9  221.1  219.7  214.4  215.3  220.4  222.8  222.3  302.2  294.5   \n",
       "4  226.0  226.1  225.1  219.7  220.7  225.6  228.1  227.5  304.0  297.9   \n",
       "\n",
       "    y_50   y_51   y_52   y_53   y_54   y_55   y_56   y_57   y_58   y_59  \\\n",
       "0  291.4  293.6  291.5  295.4  302.8  310.5  313.5  314.4  313.9  311.1   \n",
       "1  277.9  280.4  278.2  285.6  300.6  310.3  313.6  314.7  314.3  310.8   \n",
       "2  286.9  289.1  287.1  291.2  298.8  306.4  309.2  310.0  309.4  306.6   \n",
       "3  291.3  293.7  291.8  295.8  303.2  310.8  313.6  314.3  313.5  310.4   \n",
       "4  295.4  297.6  295.9  299.3  305.4  312.0  314.2  314.8  314.1  311.5   \n",
       "\n",
       "    y_60   y_61   y_62   y_63   y_64   y_65   y_66   y_67  \n",
       "0  303.2  300.3  301.1  300.0  302.9  301.6  302.6  302.1  \n",
       "1  300.4  289.0  289.2  289.0  299.4  300.5  301.6  301.0  \n",
       "2  299.0  295.9  296.6  295.8  298.9  297.3  298.2  297.7  \n",
       "3  302.5  300.1  301.0  300.2  303.3  301.7  302.5  301.8  \n",
       "4  304.3  302.8  303.7  303.0  305.3  303.4  304.3  303.7  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainsetCpy = trainset.copy()\n",
    "testsetCpy = testset.copy()\n",
    "testsetCpy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pour les données d'entrainement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
       "      <th>152</th>\n",
       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "      <th>216</th>\n",
       "      <th>217</th>\n",
       "      <th>218</th>\n",
       "      <th>219</th>\n",
       "      <th>220</th>\n",
       "      <th>221</th>\n",
       "      <th>222</th>\n",
       "      <th>223</th>\n",
       "      <th>224</th>\n",
       "      <th>225</th>\n",
       "      <th>226</th>\n",
       "      <th>227</th>\n",
       "      <th>228</th>\n",
       "      <th>229</th>\n",
       "      <th>230</th>\n",
       "      <th>231</th>\n",
       "      <th>232</th>\n",
       "      <th>233</th>\n",
       "      <th>234</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "      <th>245</th>\n",
       "      <th>246</th>\n",
       "      <th>247</th>\n",
       "      <th>248</th>\n",
       "      <th>249</th>\n",
       "      <th>250</th>\n",
       "      <th>251</th>\n",
       "      <th>252</th>\n",
       "      <th>253</th>\n",
       "      <th>254</th>\n",
       "      <th>255</th>\n",
       "      <th>256</th>\n",
       "      <th>257</th>\n",
       "      <th>258</th>\n",
       "      <th>259</th>\n",
       "      <th>260</th>\n",
       "      <th>261</th>\n",
       "      <th>262</th>\n",
       "      <th>263</th>\n",
       "      <th>264</th>\n",
       "      <th>265</th>\n",
       "      <th>266</th>\n",
       "      <th>267</th>\n",
       "      <th>268</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "      <th>279</th>\n",
       "      <th>280</th>\n",
       "      <th>281</th>\n",
       "      <th>282</th>\n",
       "      <th>283</th>\n",
       "      <th>284</th>\n",
       "      <th>285</th>\n",
       "      <th>286</th>\n",
       "      <th>287</th>\n",
       "      <th>288</th>\n",
       "      <th>289</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "      <th>300</th>\n",
       "      <th>301</th>\n",
       "      <th>302</th>\n",
       "      <th>303</th>\n",
       "      <th>304</th>\n",
       "      <th>305</th>\n",
       "      <th>306</th>\n",
       "      <th>307</th>\n",
       "      <th>308</th>\n",
       "      <th>309</th>\n",
       "      <th>310</th>\n",
       "      <th>311</th>\n",
       "      <th>312</th>\n",
       "      <th>313</th>\n",
       "      <th>314</th>\n",
       "      <th>315</th>\n",
       "      <th>316</th>\n",
       "      <th>317</th>\n",
       "      <th>318</th>\n",
       "      <th>319</th>\n",
       "      <th>320</th>\n",
       "      <th>321</th>\n",
       "      <th>322</th>\n",
       "      <th>323</th>\n",
       "      <th>324</th>\n",
       "      <th>325</th>\n",
       "      <th>326</th>\n",
       "      <th>327</th>\n",
       "      <th>328</th>\n",
       "      <th>329</th>\n",
       "      <th>330</th>\n",
       "      <th>331</th>\n",
       "      <th>332</th>\n",
       "      <th>333</th>\n",
       "      <th>334</th>\n",
       "      <th>335</th>\n",
       "      <th>336</th>\n",
       "      <th>337</th>\n",
       "      <th>338</th>\n",
       "      <th>339</th>\n",
       "      <th>340</th>\n",
       "      <th>341</th>\n",
       "      <th>342</th>\n",
       "      <th>343</th>\n",
       "      <th>344</th>\n",
       "      <th>345</th>\n",
       "      <th>346</th>\n",
       "      <th>347</th>\n",
       "      <th>348</th>\n",
       "      <th>349</th>\n",
       "      <th>350</th>\n",
       "      <th>351</th>\n",
       "      <th>352</th>\n",
       "      <th>353</th>\n",
       "      <th>354</th>\n",
       "      <th>355</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S005_001_00000001</td>\n",
       "      <td>-2.422045</td>\n",
       "      <td>-0.514499</td>\n",
       "      <td>-0.504978</td>\n",
       "      <td>0.842154</td>\n",
       "      <td>-0.590387</td>\n",
       "      <td>0.944383</td>\n",
       "      <td>0.280484</td>\n",
       "      <td>0.516634</td>\n",
       "      <td>0.140506</td>\n",
       "      <td>0.099023</td>\n",
       "      <td>2.276509</td>\n",
       "      <td>0.797066</td>\n",
       "      <td>1.089989</td>\n",
       "      <td>-0.237536</td>\n",
       "      <td>0.382572</td>\n",
       "      <td>-0.420350</td>\n",
       "      <td>1.301158</td>\n",
       "      <td>-0.828614</td>\n",
       "      <td>-0.521586</td>\n",
       "      <td>0.474950</td>\n",
       "      <td>1.277160</td>\n",
       "      <td>0.574633</td>\n",
       "      <td>-0.707459</td>\n",
       "      <td>0.128109</td>\n",
       "      <td>0.266873</td>\n",
       "      <td>0.362604</td>\n",
       "      <td>-0.965180</td>\n",
       "      <td>-0.164825</td>\n",
       "      <td>0.645037</td>\n",
       "      <td>-0.832571</td>\n",
       "      <td>-0.830679</td>\n",
       "      <td>-0.720573</td>\n",
       "      <td>-0.674197</td>\n",
       "      <td>0.863832</td>\n",
       "      <td>-0.027864</td>\n",
       "      <td>-1.088777</td>\n",
       "      <td>2.572479</td>\n",
       "      <td>0.962688</td>\n",
       "      <td>0.222646</td>\n",
       "      <td>0.055087</td>\n",
       "      <td>-2.293699</td>\n",
       "      <td>-1.649053</td>\n",
       "      <td>-1.001459</td>\n",
       "      <td>-0.067005</td>\n",
       "      <td>1.487614</td>\n",
       "      <td>0.158582</td>\n",
       "      <td>-0.789824</td>\n",
       "      <td>-1.092460</td>\n",
       "      <td>3.063921</td>\n",
       "      <td>3.035338</td>\n",
       "      <td>3.191445</td>\n",
       "      <td>0.804751</td>\n",
       "      <td>0.444656</td>\n",
       "      <td>0.767143</td>\n",
       "      <td>-0.892039</td>\n",
       "      <td>2.627187</td>\n",
       "      <td>1.414970</td>\n",
       "      <td>3.964590</td>\n",
       "      <td>-1.229673</td>\n",
       "      <td>0.300425</td>\n",
       "      <td>1.047827</td>\n",
       "      <td>2.345936</td>\n",
       "      <td>-0.287113</td>\n",
       "      <td>1.918857</td>\n",
       "      <td>0.406911</td>\n",
       "      <td>0.566958</td>\n",
       "      <td>-1.468247</td>\n",
       "      <td>0.798881</td>\n",
       "      <td>-2.347889</td>\n",
       "      <td>0.378051</td>\n",
       "      <td>-0.033132</td>\n",
       "      <td>-0.477294</td>\n",
       "      <td>-1.308110</td>\n",
       "      <td>-0.119284</td>\n",
       "      <td>-0.100177</td>\n",
       "      <td>0.428898</td>\n",
       "      <td>-1.898794</td>\n",
       "      <td>-0.265512</td>\n",
       "      <td>1.048010</td>\n",
       "      <td>-0.184117</td>\n",
       "      <td>0.677399</td>\n",
       "      <td>0.423569</td>\n",
       "      <td>1.945772</td>\n",
       "      <td>0.901140</td>\n",
       "      <td>0.822684</td>\n",
       "      <td>0.638795</td>\n",
       "      <td>-2.139044</td>\n",
       "      <td>-1.037973</td>\n",
       "      <td>-0.280762</td>\n",
       "      <td>-0.683216</td>\n",
       "      <td>0.825780</td>\n",
       "      <td>3.139535</td>\n",
       "      <td>-1.051019</td>\n",
       "      <td>-0.167132</td>\n",
       "      <td>0.840751</td>\n",
       "      <td>0.110484</td>\n",
       "      <td>0.920122</td>\n",
       "      <td>1.417406</td>\n",
       "      <td>-1.064067</td>\n",
       "      <td>1.412985</td>\n",
       "      <td>1.201106</td>\n",
       "      <td>-0.843437</td>\n",
       "      <td>-1.613027</td>\n",
       "      <td>0.097397</td>\n",
       "      <td>0.523434</td>\n",
       "      <td>1.061154</td>\n",
       "      <td>0.979967</td>\n",
       "      <td>0.024707</td>\n",
       "      <td>0.526197</td>\n",
       "      <td>-0.146056</td>\n",
       "      <td>-0.767169</td>\n",
       "      <td>-0.065331</td>\n",
       "      <td>0.541808</td>\n",
       "      <td>0.391427</td>\n",
       "      <td>0.821145</td>\n",
       "      <td>-0.669368</td>\n",
       "      <td>0.404505</td>\n",
       "      <td>0.055005</td>\n",
       "      <td>-0.782735</td>\n",
       "      <td>-0.013662</td>\n",
       "      <td>0.112026</td>\n",
       "      <td>0.810741</td>\n",
       "      <td>0.071041</td>\n",
       "      <td>0.462119</td>\n",
       "      <td>0.506668</td>\n",
       "      <td>0.006435</td>\n",
       "      <td>0.231409</td>\n",
       "      <td>0.469970</td>\n",
       "      <td>-0.205928</td>\n",
       "      <td>0.153500</td>\n",
       "      <td>0.448333</td>\n",
       "      <td>-0.146562</td>\n",
       "      <td>-0.585131</td>\n",
       "      <td>0.357613</td>\n",
       "      <td>-0.352428</td>\n",
       "      <td>0.729494</td>\n",
       "      <td>-0.114007</td>\n",
       "      <td>0.180825</td>\n",
       "      <td>0.294510</td>\n",
       "      <td>0.021162</td>\n",
       "      <td>0.501719</td>\n",
       "      <td>0.749670</td>\n",
       "      <td>-0.425777</td>\n",
       "      <td>-0.240957</td>\n",
       "      <td>-0.508020</td>\n",
       "      <td>0.012814</td>\n",
       "      <td>-0.183088</td>\n",
       "      <td>-0.007520</td>\n",
       "      <td>0.236373</td>\n",
       "      <td>-0.576461</td>\n",
       "      <td>-0.543274</td>\n",
       "      <td>0.518400</td>\n",
       "      <td>0.030273</td>\n",
       "      <td>0.501695</td>\n",
       "      <td>0.375954</td>\n",
       "      <td>0.489191</td>\n",
       "      <td>0.943326</td>\n",
       "      <td>0.093237</td>\n",
       "      <td>-0.221395</td>\n",
       "      <td>0.378368</td>\n",
       "      <td>0.052560</td>\n",
       "      <td>0.092875</td>\n",
       "      <td>0.378795</td>\n",
       "      <td>0.200418</td>\n",
       "      <td>-0.180958</td>\n",
       "      <td>0.044293</td>\n",
       "      <td>-0.238839</td>\n",
       "      <td>-0.492745</td>\n",
       "      <td>0.665203</td>\n",
       "      <td>-0.111076</td>\n",
       "      <td>0.158070</td>\n",
       "      <td>0.450825</td>\n",
       "      <td>-0.508927</td>\n",
       "      <td>0.497720</td>\n",
       "      <td>0.418634</td>\n",
       "      <td>0.281906</td>\n",
       "      <td>-0.029992</td>\n",
       "      <td>0.193345</td>\n",
       "      <td>-0.010611</td>\n",
       "      <td>0.394107</td>\n",
       "      <td>0.476401</td>\n",
       "      <td>0.015009</td>\n",
       "      <td>0.306408</td>\n",
       "      <td>-0.364764</td>\n",
       "      <td>0.226373</td>\n",
       "      <td>0.023008</td>\n",
       "      <td>-0.005134</td>\n",
       "      <td>0.526651</td>\n",
       "      <td>-0.191662</td>\n",
       "      <td>0.116471</td>\n",
       "      <td>-0.538892</td>\n",
       "      <td>-0.131936</td>\n",
       "      <td>0.073221</td>\n",
       "      <td>0.240766</td>\n",
       "      <td>0.313051</td>\n",
       "      <td>-0.020694</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>-0.355131</td>\n",
       "      <td>-0.554434</td>\n",
       "      <td>0.059641</td>\n",
       "      <td>0.492502</td>\n",
       "      <td>-0.723126</td>\n",
       "      <td>-0.479278</td>\n",
       "      <td>-0.477729</td>\n",
       "      <td>-0.083133</td>\n",
       "      <td>0.480568</td>\n",
       "      <td>-0.718129</td>\n",
       "      <td>-0.165555</td>\n",
       "      <td>-0.983583</td>\n",
       "      <td>0.840593</td>\n",
       "      <td>-0.406568</td>\n",
       "      <td>1.046419</td>\n",
       "      <td>-0.083893</td>\n",
       "      <td>0.007322</td>\n",
       "      <td>-0.004909</td>\n",
       "      <td>-0.079930</td>\n",
       "      <td>-0.120973</td>\n",
       "      <td>-0.838983</td>\n",
       "      <td>-0.904007</td>\n",
       "      <td>0.515731</td>\n",
       "      <td>0.894112</td>\n",
       "      <td>-1.143567</td>\n",
       "      <td>1.019680</td>\n",
       "      <td>-0.173489</td>\n",
       "      <td>0.201580</td>\n",
       "      <td>-0.576973</td>\n",
       "      <td>0.202880</td>\n",
       "      <td>0.608302</td>\n",
       "      <td>-1.305887</td>\n",
       "      <td>0.326183</td>\n",
       "      <td>-0.598493</td>\n",
       "      <td>1.040402</td>\n",
       "      <td>-0.499363</td>\n",
       "      <td>0.055896</td>\n",
       "      <td>1.672653</td>\n",
       "      <td>-0.666856</td>\n",
       "      <td>0.848751</td>\n",
       "      <td>-0.350116</td>\n",
       "      <td>0.131658</td>\n",
       "      <td>0.049437</td>\n",
       "      <td>-0.233581</td>\n",
       "      <td>0.703434</td>\n",
       "      <td>0.027097</td>\n",
       "      <td>0.441636</td>\n",
       "      <td>-0.455268</td>\n",
       "      <td>-0.251607</td>\n",
       "      <td>-0.011107</td>\n",
       "      <td>0.195264</td>\n",
       "      <td>-0.414941</td>\n",
       "      <td>0.175404</td>\n",
       "      <td>1.094966</td>\n",
       "      <td>-0.370902</td>\n",
       "      <td>-0.331974</td>\n",
       "      <td>0.480576</td>\n",
       "      <td>-0.198805</td>\n",
       "      <td>0.354888</td>\n",
       "      <td>0.618082</td>\n",
       "      <td>-0.291748</td>\n",
       "      <td>0.975548</td>\n",
       "      <td>0.550659</td>\n",
       "      <td>-0.415794</td>\n",
       "      <td>0.303245</td>\n",
       "      <td>-0.627194</td>\n",
       "      <td>-0.401057</td>\n",
       "      <td>-0.024082</td>\n",
       "      <td>0.101071</td>\n",
       "      <td>-0.952048</td>\n",
       "      <td>0.776166</td>\n",
       "      <td>0.157719</td>\n",
       "      <td>-0.106416</td>\n",
       "      <td>-0.395406</td>\n",
       "      <td>-0.443853</td>\n",
       "      <td>-0.536166</td>\n",
       "      <td>0.135406</td>\n",
       "      <td>0.145441</td>\n",
       "      <td>0.443553</td>\n",
       "      <td>0.627004</td>\n",
       "      <td>0.333064</td>\n",
       "      <td>-0.371739</td>\n",
       "      <td>-0.300969</td>\n",
       "      <td>-0.559815</td>\n",
       "      <td>0.658823</td>\n",
       "      <td>-0.074787</td>\n",
       "      <td>0.105122</td>\n",
       "      <td>0.181579</td>\n",
       "      <td>-0.030059</td>\n",
       "      <td>-0.177499</td>\n",
       "      <td>0.550571</td>\n",
       "      <td>0.209239</td>\n",
       "      <td>0.417161</td>\n",
       "      <td>0.354636</td>\n",
       "      <td>0.585453</td>\n",
       "      <td>-0.215858</td>\n",
       "      <td>0.393470</td>\n",
       "      <td>-0.294912</td>\n",
       "      <td>-0.119379</td>\n",
       "      <td>0.166032</td>\n",
       "      <td>0.138318</td>\n",
       "      <td>0.205800</td>\n",
       "      <td>0.071766</td>\n",
       "      <td>0.116803</td>\n",
       "      <td>0.015501</td>\n",
       "      <td>0.403927</td>\n",
       "      <td>-0.285879</td>\n",
       "      <td>-0.124954</td>\n",
       "      <td>0.458053</td>\n",
       "      <td>0.245666</td>\n",
       "      <td>-0.132666</td>\n",
       "      <td>-0.072168</td>\n",
       "      <td>-0.128495</td>\n",
       "      <td>0.371667</td>\n",
       "      <td>0.073819</td>\n",
       "      <td>-0.346774</td>\n",
       "      <td>-0.154980</td>\n",
       "      <td>-0.043927</td>\n",
       "      <td>0.266374</td>\n",
       "      <td>-0.377492</td>\n",
       "      <td>0.287021</td>\n",
       "      <td>0.323220</td>\n",
       "      <td>-0.139446</td>\n",
       "      <td>-0.041754</td>\n",
       "      <td>-0.330841</td>\n",
       "      <td>-0.327843</td>\n",
       "      <td>-0.419849</td>\n",
       "      <td>-0.315440</td>\n",
       "      <td>0.243553</td>\n",
       "      <td>0.034042</td>\n",
       "      <td>-0.048024</td>\n",
       "      <td>-0.358343</td>\n",
       "      <td>0.685317</td>\n",
       "      <td>0.149768</td>\n",
       "      <td>0.093728</td>\n",
       "      <td>-0.115640</td>\n",
       "      <td>0.049546</td>\n",
       "      <td>0.196791</td>\n",
       "      <td>0.138526</td>\n",
       "      <td>-0.285874</td>\n",
       "      <td>-0.063073</td>\n",
       "      <td>-0.126309</td>\n",
       "      <td>-0.552038</td>\n",
       "      <td>-0.076345</td>\n",
       "      <td>0.162986</td>\n",
       "      <td>-0.099211</td>\n",
       "      <td>-0.150293</td>\n",
       "      <td>-0.446018</td>\n",
       "      <td>0.048942</td>\n",
       "      <td>-0.193010</td>\n",
       "      <td>-0.122656</td>\n",
       "      <td>0.106874</td>\n",
       "      <td>-0.060901</td>\n",
       "      <td>0.138733</td>\n",
       "      <td>-0.462648</td>\n",
       "      <td>-0.368683</td>\n",
       "      <td>-0.032201</td>\n",
       "      <td>-0.120023</td>\n",
       "      <td>0.006298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S005_001_00000011</td>\n",
       "      <td>-2.220450</td>\n",
       "      <td>-0.824084</td>\n",
       "      <td>-0.442616</td>\n",
       "      <td>0.867950</td>\n",
       "      <td>-0.807278</td>\n",
       "      <td>0.911351</td>\n",
       "      <td>0.409966</td>\n",
       "      <td>0.716671</td>\n",
       "      <td>0.594750</td>\n",
       "      <td>0.450701</td>\n",
       "      <td>1.952625</td>\n",
       "      <td>0.880323</td>\n",
       "      <td>0.949376</td>\n",
       "      <td>-0.323829</td>\n",
       "      <td>0.700085</td>\n",
       "      <td>-0.695030</td>\n",
       "      <td>1.392928</td>\n",
       "      <td>-0.303409</td>\n",
       "      <td>-0.586684</td>\n",
       "      <td>0.371747</td>\n",
       "      <td>1.676886</td>\n",
       "      <td>0.354014</td>\n",
       "      <td>-0.603328</td>\n",
       "      <td>0.333678</td>\n",
       "      <td>0.154593</td>\n",
       "      <td>0.230180</td>\n",
       "      <td>-1.178306</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>1.250172</td>\n",
       "      <td>-0.934775</td>\n",
       "      <td>-0.927225</td>\n",
       "      <td>-0.826885</td>\n",
       "      <td>-1.027498</td>\n",
       "      <td>0.873422</td>\n",
       "      <td>0.223782</td>\n",
       "      <td>-0.952116</td>\n",
       "      <td>2.333242</td>\n",
       "      <td>0.665000</td>\n",
       "      <td>0.913002</td>\n",
       "      <td>-0.546047</td>\n",
       "      <td>-2.473572</td>\n",
       "      <td>-1.593174</td>\n",
       "      <td>-1.281094</td>\n",
       "      <td>-0.392417</td>\n",
       "      <td>1.897944</td>\n",
       "      <td>0.269570</td>\n",
       "      <td>-0.920724</td>\n",
       "      <td>-0.771628</td>\n",
       "      <td>3.196625</td>\n",
       "      <td>2.724439</td>\n",
       "      <td>3.103635</td>\n",
       "      <td>0.788405</td>\n",
       "      <td>0.341103</td>\n",
       "      <td>0.883828</td>\n",
       "      <td>-1.057583</td>\n",
       "      <td>2.810453</td>\n",
       "      <td>1.293946</td>\n",
       "      <td>3.683671</td>\n",
       "      <td>-1.134929</td>\n",
       "      <td>0.027931</td>\n",
       "      <td>1.462615</td>\n",
       "      <td>2.107863</td>\n",
       "      <td>-0.268499</td>\n",
       "      <td>1.653162</td>\n",
       "      <td>0.494796</td>\n",
       "      <td>0.631624</td>\n",
       "      <td>-1.191445</td>\n",
       "      <td>1.090118</td>\n",
       "      <td>-1.765492</td>\n",
       "      <td>0.356764</td>\n",
       "      <td>-0.878103</td>\n",
       "      <td>-0.270191</td>\n",
       "      <td>-0.706211</td>\n",
       "      <td>0.313790</td>\n",
       "      <td>-0.030242</td>\n",
       "      <td>0.614113</td>\n",
       "      <td>-1.598050</td>\n",
       "      <td>0.179738</td>\n",
       "      <td>0.787420</td>\n",
       "      <td>-0.635254</td>\n",
       "      <td>0.749319</td>\n",
       "      <td>0.641541</td>\n",
       "      <td>2.113908</td>\n",
       "      <td>1.177171</td>\n",
       "      <td>1.182172</td>\n",
       "      <td>0.758176</td>\n",
       "      <td>-1.831354</td>\n",
       "      <td>-0.796943</td>\n",
       "      <td>-0.584075</td>\n",
       "      <td>-0.208147</td>\n",
       "      <td>1.196413</td>\n",
       "      <td>3.241490</td>\n",
       "      <td>-0.863080</td>\n",
       "      <td>-0.098399</td>\n",
       "      <td>0.700504</td>\n",
       "      <td>0.212661</td>\n",
       "      <td>1.231197</td>\n",
       "      <td>1.717772</td>\n",
       "      <td>-0.733260</td>\n",
       "      <td>1.209952</td>\n",
       "      <td>1.281554</td>\n",
       "      <td>-0.860577</td>\n",
       "      <td>-1.823862</td>\n",
       "      <td>-0.155897</td>\n",
       "      <td>1.217095</td>\n",
       "      <td>0.891797</td>\n",
       "      <td>0.658699</td>\n",
       "      <td>-0.009148</td>\n",
       "      <td>0.104836</td>\n",
       "      <td>-0.383683</td>\n",
       "      <td>-0.525198</td>\n",
       "      <td>-0.001017</td>\n",
       "      <td>0.201325</td>\n",
       "      <td>0.410508</td>\n",
       "      <td>-0.335771</td>\n",
       "      <td>-0.833709</td>\n",
       "      <td>0.394416</td>\n",
       "      <td>0.452575</td>\n",
       "      <td>-0.152868</td>\n",
       "      <td>-0.418220</td>\n",
       "      <td>0.100647</td>\n",
       "      <td>0.260156</td>\n",
       "      <td>0.397543</td>\n",
       "      <td>0.133620</td>\n",
       "      <td>-0.307294</td>\n",
       "      <td>0.638067</td>\n",
       "      <td>-0.107472</td>\n",
       "      <td>-0.498461</td>\n",
       "      <td>0.079847</td>\n",
       "      <td>0.264229</td>\n",
       "      <td>-0.664943</td>\n",
       "      <td>0.974927</td>\n",
       "      <td>-0.016227</td>\n",
       "      <td>-0.566363</td>\n",
       "      <td>0.276577</td>\n",
       "      <td>0.171008</td>\n",
       "      <td>0.264465</td>\n",
       "      <td>-0.110557</td>\n",
       "      <td>-0.171294</td>\n",
       "      <td>0.528732</td>\n",
       "      <td>-0.155434</td>\n",
       "      <td>0.153182</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.107335</td>\n",
       "      <td>0.231366</td>\n",
       "      <td>-0.054775</td>\n",
       "      <td>-0.522291</td>\n",
       "      <td>-0.228046</td>\n",
       "      <td>-0.538766</td>\n",
       "      <td>0.050291</td>\n",
       "      <td>0.539805</td>\n",
       "      <td>-0.414306</td>\n",
       "      <td>0.523218</td>\n",
       "      <td>0.684844</td>\n",
       "      <td>-0.119669</td>\n",
       "      <td>-0.483274</td>\n",
       "      <td>-0.270304</td>\n",
       "      <td>-0.043544</td>\n",
       "      <td>0.020570</td>\n",
       "      <td>-0.072609</td>\n",
       "      <td>0.166963</td>\n",
       "      <td>-0.154771</td>\n",
       "      <td>-0.042501</td>\n",
       "      <td>0.302268</td>\n",
       "      <td>-0.634133</td>\n",
       "      <td>-0.442756</td>\n",
       "      <td>0.402583</td>\n",
       "      <td>-0.115385</td>\n",
       "      <td>-0.713197</td>\n",
       "      <td>-0.057345</td>\n",
       "      <td>0.293745</td>\n",
       "      <td>-0.062184</td>\n",
       "      <td>0.487431</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.182655</td>\n",
       "      <td>0.363003</td>\n",
       "      <td>-0.172662</td>\n",
       "      <td>0.208688</td>\n",
       "      <td>-0.353348</td>\n",
       "      <td>-0.086140</td>\n",
       "      <td>-0.450511</td>\n",
       "      <td>-0.531916</td>\n",
       "      <td>-0.456386</td>\n",
       "      <td>0.701345</td>\n",
       "      <td>-0.249106</td>\n",
       "      <td>-0.635945</td>\n",
       "      <td>-0.237587</td>\n",
       "      <td>-0.572241</td>\n",
       "      <td>0.498425</td>\n",
       "      <td>-0.450956</td>\n",
       "      <td>0.209684</td>\n",
       "      <td>0.077766</td>\n",
       "      <td>-0.159234</td>\n",
       "      <td>-0.661501</td>\n",
       "      <td>0.066634</td>\n",
       "      <td>-0.087044</td>\n",
       "      <td>-0.520225</td>\n",
       "      <td>0.226015</td>\n",
       "      <td>0.647374</td>\n",
       "      <td>0.331490</td>\n",
       "      <td>-0.235818</td>\n",
       "      <td>0.743090</td>\n",
       "      <td>0.284794</td>\n",
       "      <td>0.274975</td>\n",
       "      <td>0.223156</td>\n",
       "      <td>-0.539892</td>\n",
       "      <td>0.611208</td>\n",
       "      <td>0.202739</td>\n",
       "      <td>0.846151</td>\n",
       "      <td>-0.718736</td>\n",
       "      <td>0.185858</td>\n",
       "      <td>-1.101641</td>\n",
       "      <td>-0.229508</td>\n",
       "      <td>0.061440</td>\n",
       "      <td>0.024907</td>\n",
       "      <td>0.001952</td>\n",
       "      <td>0.489348</td>\n",
       "      <td>0.783492</td>\n",
       "      <td>0.913868</td>\n",
       "      <td>-0.396426</td>\n",
       "      <td>-0.882205</td>\n",
       "      <td>0.584180</td>\n",
       "      <td>-0.965429</td>\n",
       "      <td>0.050864</td>\n",
       "      <td>0.147838</td>\n",
       "      <td>0.248824</td>\n",
       "      <td>-0.176336</td>\n",
       "      <td>-0.678960</td>\n",
       "      <td>1.207026</td>\n",
       "      <td>-0.050563</td>\n",
       "      <td>0.324167</td>\n",
       "      <td>-1.012958</td>\n",
       "      <td>0.474943</td>\n",
       "      <td>-0.047748</td>\n",
       "      <td>-1.388518</td>\n",
       "      <td>0.584402</td>\n",
       "      <td>-0.808239</td>\n",
       "      <td>0.192782</td>\n",
       "      <td>-0.491053</td>\n",
       "      <td>-0.101206</td>\n",
       "      <td>0.387043</td>\n",
       "      <td>-0.420627</td>\n",
       "      <td>-0.115148</td>\n",
       "      <td>-0.075386</td>\n",
       "      <td>0.596870</td>\n",
       "      <td>0.077647</td>\n",
       "      <td>0.242664</td>\n",
       "      <td>-0.233233</td>\n",
       "      <td>0.653358</td>\n",
       "      <td>-0.187237</td>\n",
       "      <td>-1.183770</td>\n",
       "      <td>0.346001</td>\n",
       "      <td>0.065111</td>\n",
       "      <td>-0.619420</td>\n",
       "      <td>0.204310</td>\n",
       "      <td>-0.056871</td>\n",
       "      <td>-0.906835</td>\n",
       "      <td>0.559999</td>\n",
       "      <td>-1.231934</td>\n",
       "      <td>-0.588163</td>\n",
       "      <td>0.200391</td>\n",
       "      <td>-0.176763</td>\n",
       "      <td>0.655819</td>\n",
       "      <td>0.219338</td>\n",
       "      <td>0.011935</td>\n",
       "      <td>-0.209794</td>\n",
       "      <td>0.936342</td>\n",
       "      <td>-0.750864</td>\n",
       "      <td>-0.388577</td>\n",
       "      <td>0.069368</td>\n",
       "      <td>0.118136</td>\n",
       "      <td>0.483215</td>\n",
       "      <td>0.558529</td>\n",
       "      <td>-0.481610</td>\n",
       "      <td>-0.019643</td>\n",
       "      <td>-0.393431</td>\n",
       "      <td>-0.758803</td>\n",
       "      <td>-0.069155</td>\n",
       "      <td>0.419813</td>\n",
       "      <td>0.226089</td>\n",
       "      <td>0.668821</td>\n",
       "      <td>-0.408183</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>-0.070386</td>\n",
       "      <td>-0.085070</td>\n",
       "      <td>0.159678</td>\n",
       "      <td>0.203354</td>\n",
       "      <td>-0.587928</td>\n",
       "      <td>-0.220395</td>\n",
       "      <td>-0.401240</td>\n",
       "      <td>-0.300075</td>\n",
       "      <td>-0.612535</td>\n",
       "      <td>0.116213</td>\n",
       "      <td>-0.341433</td>\n",
       "      <td>0.315142</td>\n",
       "      <td>0.173374</td>\n",
       "      <td>-0.123385</td>\n",
       "      <td>-0.129131</td>\n",
       "      <td>-0.380350</td>\n",
       "      <td>-0.039306</td>\n",
       "      <td>0.067938</td>\n",
       "      <td>0.074311</td>\n",
       "      <td>-0.694888</td>\n",
       "      <td>0.230362</td>\n",
       "      <td>0.317080</td>\n",
       "      <td>-0.430243</td>\n",
       "      <td>-0.013616</td>\n",
       "      <td>-0.008109</td>\n",
       "      <td>0.100474</td>\n",
       "      <td>0.350076</td>\n",
       "      <td>-0.403367</td>\n",
       "      <td>-0.147593</td>\n",
       "      <td>0.449490</td>\n",
       "      <td>0.076959</td>\n",
       "      <td>-0.170959</td>\n",
       "      <td>-0.290641</td>\n",
       "      <td>0.444392</td>\n",
       "      <td>-0.237573</td>\n",
       "      <td>-0.314239</td>\n",
       "      <td>0.163471</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>0.414928</td>\n",
       "      <td>0.061441</td>\n",
       "      <td>0.371939</td>\n",
       "      <td>0.372028</td>\n",
       "      <td>-0.009726</td>\n",
       "      <td>0.033973</td>\n",
       "      <td>-0.004191</td>\n",
       "      <td>0.422105</td>\n",
       "      <td>-0.851663</td>\n",
       "      <td>-0.195349</td>\n",
       "      <td>-0.062573</td>\n",
       "      <td>0.089064</td>\n",
       "      <td>-0.166465</td>\n",
       "      <td>-0.200279</td>\n",
       "      <td>-0.145031</td>\n",
       "      <td>0.284497</td>\n",
       "      <td>-0.015318</td>\n",
       "      <td>0.153042</td>\n",
       "      <td>0.576949</td>\n",
       "      <td>0.166307</td>\n",
       "      <td>-0.212129</td>\n",
       "      <td>0.120308</td>\n",
       "      <td>0.095037</td>\n",
       "      <td>0.476205</td>\n",
       "      <td>0.035356</td>\n",
       "      <td>0.190164</td>\n",
       "      <td>0.086299</td>\n",
       "      <td>0.031945</td>\n",
       "      <td>-0.016451</td>\n",
       "      <td>-0.195049</td>\n",
       "      <td>0.427069</td>\n",
       "      <td>0.347049</td>\n",
       "      <td>0.227120</td>\n",
       "      <td>0.060197</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S010_001_00000001</td>\n",
       "      <td>-2.991666</td>\n",
       "      <td>5.081572</td>\n",
       "      <td>-0.801330</td>\n",
       "      <td>-0.892331</td>\n",
       "      <td>0.463246</td>\n",
       "      <td>0.355654</td>\n",
       "      <td>0.286743</td>\n",
       "      <td>0.353372</td>\n",
       "      <td>-0.153716</td>\n",
       "      <td>0.852929</td>\n",
       "      <td>0.841748</td>\n",
       "      <td>1.674563</td>\n",
       "      <td>0.674734</td>\n",
       "      <td>-0.668415</td>\n",
       "      <td>1.871574</td>\n",
       "      <td>-1.444211</td>\n",
       "      <td>0.043157</td>\n",
       "      <td>-1.441852</td>\n",
       "      <td>-1.507439</td>\n",
       "      <td>-3.270778</td>\n",
       "      <td>2.853501</td>\n",
       "      <td>2.892128</td>\n",
       "      <td>-0.892537</td>\n",
       "      <td>-0.603601</td>\n",
       "      <td>-1.731733</td>\n",
       "      <td>1.510062</td>\n",
       "      <td>0.663404</td>\n",
       "      <td>0.252319</td>\n",
       "      <td>0.852474</td>\n",
       "      <td>1.654812</td>\n",
       "      <td>-0.080267</td>\n",
       "      <td>0.914169</td>\n",
       "      <td>-1.674726</td>\n",
       "      <td>2.431463</td>\n",
       "      <td>0.708107</td>\n",
       "      <td>-2.982527</td>\n",
       "      <td>0.228068</td>\n",
       "      <td>0.164293</td>\n",
       "      <td>-0.082841</td>\n",
       "      <td>-0.279028</td>\n",
       "      <td>1.709136</td>\n",
       "      <td>-0.261806</td>\n",
       "      <td>-0.774093</td>\n",
       "      <td>0.709187</td>\n",
       "      <td>0.226467</td>\n",
       "      <td>-0.200427</td>\n",
       "      <td>-1.907094</td>\n",
       "      <td>0.768830</td>\n",
       "      <td>0.720327</td>\n",
       "      <td>-0.736454</td>\n",
       "      <td>0.542728</td>\n",
       "      <td>0.226437</td>\n",
       "      <td>0.289722</td>\n",
       "      <td>-0.258221</td>\n",
       "      <td>0.136392</td>\n",
       "      <td>-0.049586</td>\n",
       "      <td>0.289519</td>\n",
       "      <td>-0.633701</td>\n",
       "      <td>0.188268</td>\n",
       "      <td>0.256398</td>\n",
       "      <td>-0.373638</td>\n",
       "      <td>-0.423397</td>\n",
       "      <td>0.394149</td>\n",
       "      <td>-0.048800</td>\n",
       "      <td>-0.335222</td>\n",
       "      <td>0.104925</td>\n",
       "      <td>1.114764</td>\n",
       "      <td>-0.022974</td>\n",
       "      <td>0.251713</td>\n",
       "      <td>-0.813759</td>\n",
       "      <td>0.661019</td>\n",
       "      <td>0.568348</td>\n",
       "      <td>0.597469</td>\n",
       "      <td>-0.646597</td>\n",
       "      <td>-0.062673</td>\n",
       "      <td>0.483450</td>\n",
       "      <td>-0.300936</td>\n",
       "      <td>-0.325422</td>\n",
       "      <td>0.107917</td>\n",
       "      <td>0.217974</td>\n",
       "      <td>0.059186</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>-0.233618</td>\n",
       "      <td>-0.240524</td>\n",
       "      <td>-0.110295</td>\n",
       "      <td>-0.593550</td>\n",
       "      <td>-0.124802</td>\n",
       "      <td>0.185869</td>\n",
       "      <td>-0.251905</td>\n",
       "      <td>0.110858</td>\n",
       "      <td>-0.215797</td>\n",
       "      <td>-0.042777</td>\n",
       "      <td>-0.107726</td>\n",
       "      <td>-0.144958</td>\n",
       "      <td>0.109767</td>\n",
       "      <td>-0.038723</td>\n",
       "      <td>0.180957</td>\n",
       "      <td>-0.185896</td>\n",
       "      <td>-0.196481</td>\n",
       "      <td>0.065829</td>\n",
       "      <td>-0.037164</td>\n",
       "      <td>0.034334</td>\n",
       "      <td>0.311325</td>\n",
       "      <td>-0.242133</td>\n",
       "      <td>-0.177846</td>\n",
       "      <td>-0.082437</td>\n",
       "      <td>0.099571</td>\n",
       "      <td>0.198227</td>\n",
       "      <td>0.027904</td>\n",
       "      <td>0.023973</td>\n",
       "      <td>-0.086693</td>\n",
       "      <td>-0.216920</td>\n",
       "      <td>-0.148408</td>\n",
       "      <td>-0.018120</td>\n",
       "      <td>0.091168</td>\n",
       "      <td>-0.048020</td>\n",
       "      <td>0.362139</td>\n",
       "      <td>0.070480</td>\n",
       "      <td>-0.314073</td>\n",
       "      <td>-0.212033</td>\n",
       "      <td>-0.109785</td>\n",
       "      <td>0.216676</td>\n",
       "      <td>-0.296590</td>\n",
       "      <td>-0.126223</td>\n",
       "      <td>-0.222525</td>\n",
       "      <td>0.001472</td>\n",
       "      <td>0.093955</td>\n",
       "      <td>0.108034</td>\n",
       "      <td>0.055080</td>\n",
       "      <td>-0.136495</td>\n",
       "      <td>0.018403</td>\n",
       "      <td>-0.133705</td>\n",
       "      <td>0.280994</td>\n",
       "      <td>0.061648</td>\n",
       "      <td>0.308039</td>\n",
       "      <td>-0.173703</td>\n",
       "      <td>-0.216687</td>\n",
       "      <td>-0.134579</td>\n",
       "      <td>0.430395</td>\n",
       "      <td>-0.250557</td>\n",
       "      <td>0.240227</td>\n",
       "      <td>-0.396795</td>\n",
       "      <td>0.063419</td>\n",
       "      <td>-0.002313</td>\n",
       "      <td>-0.256632</td>\n",
       "      <td>0.167892</td>\n",
       "      <td>-0.080438</td>\n",
       "      <td>0.066788</td>\n",
       "      <td>0.355535</td>\n",
       "      <td>-0.015503</td>\n",
       "      <td>0.034047</td>\n",
       "      <td>0.018479</td>\n",
       "      <td>-0.016418</td>\n",
       "      <td>-0.036859</td>\n",
       "      <td>0.360075</td>\n",
       "      <td>0.103245</td>\n",
       "      <td>-0.152916</td>\n",
       "      <td>0.226950</td>\n",
       "      <td>0.085335</td>\n",
       "      <td>0.386814</td>\n",
       "      <td>-0.012485</td>\n",
       "      <td>0.082011</td>\n",
       "      <td>0.312625</td>\n",
       "      <td>0.022827</td>\n",
       "      <td>0.031809</td>\n",
       "      <td>0.151288</td>\n",
       "      <td>-0.141875</td>\n",
       "      <td>0.169544</td>\n",
       "      <td>0.129223</td>\n",
       "      <td>0.032306</td>\n",
       "      <td>-0.109390</td>\n",
       "      <td>0.250066</td>\n",
       "      <td>-0.023959</td>\n",
       "      <td>-0.197970</td>\n",
       "      <td>0.159414</td>\n",
       "      <td>-0.179555</td>\n",
       "      <td>-0.015777</td>\n",
       "      <td>0.060492</td>\n",
       "      <td>0.298184</td>\n",
       "      <td>-0.019445</td>\n",
       "      <td>-0.074952</td>\n",
       "      <td>-0.361177</td>\n",
       "      <td>0.145191</td>\n",
       "      <td>-0.338683</td>\n",
       "      <td>0.081664</td>\n",
       "      <td>0.248201</td>\n",
       "      <td>0.160680</td>\n",
       "      <td>0.035077</td>\n",
       "      <td>0.183261</td>\n",
       "      <td>0.377370</td>\n",
       "      <td>-0.243978</td>\n",
       "      <td>0.171284</td>\n",
       "      <td>-0.028362</td>\n",
       "      <td>-0.156530</td>\n",
       "      <td>0.130317</td>\n",
       "      <td>0.021539</td>\n",
       "      <td>0.097140</td>\n",
       "      <td>0.048384</td>\n",
       "      <td>0.024004</td>\n",
       "      <td>-0.056803</td>\n",
       "      <td>0.181153</td>\n",
       "      <td>-0.211795</td>\n",
       "      <td>-0.214565</td>\n",
       "      <td>0.225148</td>\n",
       "      <td>0.303655</td>\n",
       "      <td>-0.258448</td>\n",
       "      <td>0.075328</td>\n",
       "      <td>-0.322921</td>\n",
       "      <td>0.367936</td>\n",
       "      <td>-0.206992</td>\n",
       "      <td>0.091420</td>\n",
       "      <td>-0.025859</td>\n",
       "      <td>0.406632</td>\n",
       "      <td>0.190404</td>\n",
       "      <td>0.308664</td>\n",
       "      <td>-0.078961</td>\n",
       "      <td>0.199932</td>\n",
       "      <td>-0.406716</td>\n",
       "      <td>-0.157563</td>\n",
       "      <td>-0.283261</td>\n",
       "      <td>-0.007741</td>\n",
       "      <td>-0.285453</td>\n",
       "      <td>0.054580</td>\n",
       "      <td>0.310809</td>\n",
       "      <td>0.370762</td>\n",
       "      <td>0.297125</td>\n",
       "      <td>-0.321039</td>\n",
       "      <td>-0.153430</td>\n",
       "      <td>-0.253430</td>\n",
       "      <td>0.082924</td>\n",
       "      <td>0.128690</td>\n",
       "      <td>0.469325</td>\n",
       "      <td>-0.180198</td>\n",
       "      <td>-0.107041</td>\n",
       "      <td>-0.255766</td>\n",
       "      <td>-0.079094</td>\n",
       "      <td>-0.035906</td>\n",
       "      <td>0.061377</td>\n",
       "      <td>-0.343138</td>\n",
       "      <td>-0.120172</td>\n",
       "      <td>0.256778</td>\n",
       "      <td>0.413937</td>\n",
       "      <td>-0.151382</td>\n",
       "      <td>0.154023</td>\n",
       "      <td>-0.233580</td>\n",
       "      <td>-0.468033</td>\n",
       "      <td>0.171741</td>\n",
       "      <td>-0.202337</td>\n",
       "      <td>-0.046027</td>\n",
       "      <td>0.330105</td>\n",
       "      <td>0.157430</td>\n",
       "      <td>0.240231</td>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.004662</td>\n",
       "      <td>-0.210189</td>\n",
       "      <td>0.292131</td>\n",
       "      <td>-0.836930</td>\n",
       "      <td>-0.187183</td>\n",
       "      <td>0.111733</td>\n",
       "      <td>0.026391</td>\n",
       "      <td>-0.123596</td>\n",
       "      <td>-0.229646</td>\n",
       "      <td>-0.153699</td>\n",
       "      <td>-0.081612</td>\n",
       "      <td>-0.356637</td>\n",
       "      <td>0.409039</td>\n",
       "      <td>-0.233172</td>\n",
       "      <td>0.020605</td>\n",
       "      <td>0.087255</td>\n",
       "      <td>-0.105354</td>\n",
       "      <td>0.320390</td>\n",
       "      <td>-0.139473</td>\n",
       "      <td>0.472985</td>\n",
       "      <td>0.243048</td>\n",
       "      <td>0.144855</td>\n",
       "      <td>0.067172</td>\n",
       "      <td>-0.009456</td>\n",
       "      <td>-0.101887</td>\n",
       "      <td>0.529519</td>\n",
       "      <td>-0.103683</td>\n",
       "      <td>0.263418</td>\n",
       "      <td>0.098345</td>\n",
       "      <td>-0.006643</td>\n",
       "      <td>-0.161891</td>\n",
       "      <td>0.181282</td>\n",
       "      <td>0.042802</td>\n",
       "      <td>0.351489</td>\n",
       "      <td>-0.057814</td>\n",
       "      <td>-0.008520</td>\n",
       "      <td>0.608987</td>\n",
       "      <td>0.125567</td>\n",
       "      <td>-0.254436</td>\n",
       "      <td>-0.206294</td>\n",
       "      <td>-0.097433</td>\n",
       "      <td>0.186749</td>\n",
       "      <td>0.395433</td>\n",
       "      <td>-0.001360</td>\n",
       "      <td>-0.399197</td>\n",
       "      <td>-0.290370</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.413264</td>\n",
       "      <td>-0.369490</td>\n",
       "      <td>-0.060014</td>\n",
       "      <td>-0.136184</td>\n",
       "      <td>-0.422541</td>\n",
       "      <td>0.453322</td>\n",
       "      <td>-0.168332</td>\n",
       "      <td>0.370748</td>\n",
       "      <td>-0.049302</td>\n",
       "      <td>0.764073</td>\n",
       "      <td>-0.082208</td>\n",
       "      <td>-0.134470</td>\n",
       "      <td>0.141556</td>\n",
       "      <td>-0.120196</td>\n",
       "      <td>-0.166558</td>\n",
       "      <td>0.052498</td>\n",
       "      <td>0.410828</td>\n",
       "      <td>-0.087558</td>\n",
       "      <td>0.162144</td>\n",
       "      <td>0.113441</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.331360</td>\n",
       "      <td>-0.185279</td>\n",
       "      <td>-0.538368</td>\n",
       "      <td>0.293583</td>\n",
       "      <td>-0.181265</td>\n",
       "      <td>-0.053735</td>\n",
       "      <td>0.214390</td>\n",
       "      <td>-0.237613</td>\n",
       "      <td>-0.266590</td>\n",
       "      <td>-0.425849</td>\n",
       "      <td>-0.084471</td>\n",
       "      <td>-0.045366</td>\n",
       "      <td>0.250672</td>\n",
       "      <td>0.203500</td>\n",
       "      <td>0.358119</td>\n",
       "      <td>0.124741</td>\n",
       "      <td>-0.110608</td>\n",
       "      <td>0.166173</td>\n",
       "      <td>0.245588</td>\n",
       "      <td>0.198816</td>\n",
       "      <td>0.487905</td>\n",
       "      <td>-0.005629</td>\n",
       "      <td>-0.381211</td>\n",
       "      <td>0.161587</td>\n",
       "      <td>-0.270808</td>\n",
       "      <td>0.113834</td>\n",
       "      <td>-0.196901</td>\n",
       "      <td>0.276646</td>\n",
       "      <td>-0.207903</td>\n",
       "      <td>0.183486</td>\n",
       "      <td>0.107410</td>\n",
       "      <td>-0.682283</td>\n",
       "      <td>0.028788</td>\n",
       "      <td>0.310639</td>\n",
       "      <td>-0.194379</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S010_002_00000001</td>\n",
       "      <td>-3.000261</td>\n",
       "      <td>4.987984</td>\n",
       "      <td>-0.886566</td>\n",
       "      <td>-1.187165</td>\n",
       "      <td>0.506597</td>\n",
       "      <td>0.538944</td>\n",
       "      <td>0.319966</td>\n",
       "      <td>0.464281</td>\n",
       "      <td>-0.372665</td>\n",
       "      <td>0.927417</td>\n",
       "      <td>0.857080</td>\n",
       "      <td>1.715775</td>\n",
       "      <td>0.448777</td>\n",
       "      <td>-0.621174</td>\n",
       "      <td>1.838618</td>\n",
       "      <td>-1.481218</td>\n",
       "      <td>0.082254</td>\n",
       "      <td>-1.280967</td>\n",
       "      <td>-1.600561</td>\n",
       "      <td>-3.601663</td>\n",
       "      <td>2.611679</td>\n",
       "      <td>2.770883</td>\n",
       "      <td>-1.129668</td>\n",
       "      <td>-0.671729</td>\n",
       "      <td>-1.906041</td>\n",
       "      <td>1.348601</td>\n",
       "      <td>0.849921</td>\n",
       "      <td>0.381892</td>\n",
       "      <td>0.640950</td>\n",
       "      <td>1.650235</td>\n",
       "      <td>0.040808</td>\n",
       "      <td>1.068203</td>\n",
       "      <td>-1.692235</td>\n",
       "      <td>2.746948</td>\n",
       "      <td>0.619423</td>\n",
       "      <td>-3.086794</td>\n",
       "      <td>0.489284</td>\n",
       "      <td>0.063288</td>\n",
       "      <td>-0.145932</td>\n",
       "      <td>-0.211169</td>\n",
       "      <td>1.825597</td>\n",
       "      <td>-0.187447</td>\n",
       "      <td>-0.913887</td>\n",
       "      <td>0.660509</td>\n",
       "      <td>0.157651</td>\n",
       "      <td>-0.365176</td>\n",
       "      <td>-2.132250</td>\n",
       "      <td>0.845049</td>\n",
       "      <td>0.753795</td>\n",
       "      <td>-0.730130</td>\n",
       "      <td>0.778739</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>0.215949</td>\n",
       "      <td>-0.265140</td>\n",
       "      <td>-0.104827</td>\n",
       "      <td>0.053552</td>\n",
       "      <td>0.225323</td>\n",
       "      <td>-0.627534</td>\n",
       "      <td>0.086951</td>\n",
       "      <td>0.593712</td>\n",
       "      <td>-0.329511</td>\n",
       "      <td>-0.483027</td>\n",
       "      <td>0.506543</td>\n",
       "      <td>-0.023146</td>\n",
       "      <td>-0.256002</td>\n",
       "      <td>0.243578</td>\n",
       "      <td>0.991077</td>\n",
       "      <td>0.304715</td>\n",
       "      <td>0.191549</td>\n",
       "      <td>-0.771337</td>\n",
       "      <td>0.678660</td>\n",
       "      <td>0.615216</td>\n",
       "      <td>0.714461</td>\n",
       "      <td>-0.721590</td>\n",
       "      <td>-0.220720</td>\n",
       "      <td>0.298385</td>\n",
       "      <td>-0.106454</td>\n",
       "      <td>-0.323289</td>\n",
       "      <td>0.238201</td>\n",
       "      <td>0.439817</td>\n",
       "      <td>-0.171312</td>\n",
       "      <td>-0.071290</td>\n",
       "      <td>-0.322492</td>\n",
       "      <td>-0.123676</td>\n",
       "      <td>0.142975</td>\n",
       "      <td>-0.344624</td>\n",
       "      <td>-0.134088</td>\n",
       "      <td>0.388923</td>\n",
       "      <td>-0.397284</td>\n",
       "      <td>-0.027440</td>\n",
       "      <td>-0.078154</td>\n",
       "      <td>-0.001789</td>\n",
       "      <td>-0.031790</td>\n",
       "      <td>-0.141844</td>\n",
       "      <td>0.098757</td>\n",
       "      <td>0.096051</td>\n",
       "      <td>-0.001853</td>\n",
       "      <td>-0.160831</td>\n",
       "      <td>-0.015280</td>\n",
       "      <td>0.141139</td>\n",
       "      <td>-0.048071</td>\n",
       "      <td>0.190158</td>\n",
       "      <td>0.366872</td>\n",
       "      <td>-0.449399</td>\n",
       "      <td>-0.232137</td>\n",
       "      <td>-0.076804</td>\n",
       "      <td>-0.000629</td>\n",
       "      <td>-0.143414</td>\n",
       "      <td>-0.069063</td>\n",
       "      <td>0.112402</td>\n",
       "      <td>0.037352</td>\n",
       "      <td>-0.350307</td>\n",
       "      <td>-0.146011</td>\n",
       "      <td>0.104279</td>\n",
       "      <td>0.061891</td>\n",
       "      <td>0.061902</td>\n",
       "      <td>0.130847</td>\n",
       "      <td>-0.118661</td>\n",
       "      <td>-0.077212</td>\n",
       "      <td>-0.066519</td>\n",
       "      <td>-0.279594</td>\n",
       "      <td>-0.060427</td>\n",
       "      <td>-0.317562</td>\n",
       "      <td>-0.177887</td>\n",
       "      <td>0.130270</td>\n",
       "      <td>0.047124</td>\n",
       "      <td>-0.032765</td>\n",
       "      <td>-0.082232</td>\n",
       "      <td>0.145574</td>\n",
       "      <td>-0.554348</td>\n",
       "      <td>-0.125889</td>\n",
       "      <td>-0.106686</td>\n",
       "      <td>0.285143</td>\n",
       "      <td>0.300389</td>\n",
       "      <td>0.302394</td>\n",
       "      <td>-0.109391</td>\n",
       "      <td>-0.117863</td>\n",
       "      <td>-0.213878</td>\n",
       "      <td>0.483013</td>\n",
       "      <td>-0.157113</td>\n",
       "      <td>0.251764</td>\n",
       "      <td>-0.146132</td>\n",
       "      <td>0.216774</td>\n",
       "      <td>0.291309</td>\n",
       "      <td>-0.093583</td>\n",
       "      <td>0.141399</td>\n",
       "      <td>-0.127420</td>\n",
       "      <td>0.195819</td>\n",
       "      <td>0.315281</td>\n",
       "      <td>0.249637</td>\n",
       "      <td>-0.084056</td>\n",
       "      <td>-0.343581</td>\n",
       "      <td>-0.130224</td>\n",
       "      <td>0.130653</td>\n",
       "      <td>0.102455</td>\n",
       "      <td>0.392232</td>\n",
       "      <td>0.180607</td>\n",
       "      <td>-0.033724</td>\n",
       "      <td>0.162908</td>\n",
       "      <td>0.144205</td>\n",
       "      <td>-0.053717</td>\n",
       "      <td>0.251422</td>\n",
       "      <td>-0.018133</td>\n",
       "      <td>0.017424</td>\n",
       "      <td>0.094911</td>\n",
       "      <td>0.050630</td>\n",
       "      <td>-0.253298</td>\n",
       "      <td>0.341791</td>\n",
       "      <td>-0.088841</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>-0.089736</td>\n",
       "      <td>0.391152</td>\n",
       "      <td>0.180914</td>\n",
       "      <td>0.118013</td>\n",
       "      <td>0.059143</td>\n",
       "      <td>0.063760</td>\n",
       "      <td>0.028314</td>\n",
       "      <td>0.405807</td>\n",
       "      <td>0.117760</td>\n",
       "      <td>-0.323205</td>\n",
       "      <td>-0.145816</td>\n",
       "      <td>0.049484</td>\n",
       "      <td>-0.059181</td>\n",
       "      <td>-0.355938</td>\n",
       "      <td>-0.000792</td>\n",
       "      <td>0.080526</td>\n",
       "      <td>0.039515</td>\n",
       "      <td>-0.287098</td>\n",
       "      <td>0.311920</td>\n",
       "      <td>0.362632</td>\n",
       "      <td>-0.147260</td>\n",
       "      <td>0.201945</td>\n",
       "      <td>0.067685</td>\n",
       "      <td>0.089192</td>\n",
       "      <td>0.072969</td>\n",
       "      <td>0.158969</td>\n",
       "      <td>0.215558</td>\n",
       "      <td>0.059033</td>\n",
       "      <td>0.044240</td>\n",
       "      <td>0.056766</td>\n",
       "      <td>0.102684</td>\n",
       "      <td>-0.197176</td>\n",
       "      <td>-0.429740</td>\n",
       "      <td>-0.108089</td>\n",
       "      <td>0.295853</td>\n",
       "      <td>-0.018504</td>\n",
       "      <td>0.133207</td>\n",
       "      <td>-0.275633</td>\n",
       "      <td>0.202609</td>\n",
       "      <td>-0.099401</td>\n",
       "      <td>0.194353</td>\n",
       "      <td>-0.136984</td>\n",
       "      <td>0.093678</td>\n",
       "      <td>0.358612</td>\n",
       "      <td>-0.021945</td>\n",
       "      <td>0.005724</td>\n",
       "      <td>-0.026893</td>\n",
       "      <td>-0.690286</td>\n",
       "      <td>-0.043817</td>\n",
       "      <td>-0.110333</td>\n",
       "      <td>0.277275</td>\n",
       "      <td>-0.231797</td>\n",
       "      <td>0.116795</td>\n",
       "      <td>0.028782</td>\n",
       "      <td>0.285869</td>\n",
       "      <td>0.252962</td>\n",
       "      <td>0.022652</td>\n",
       "      <td>-0.578489</td>\n",
       "      <td>0.060782</td>\n",
       "      <td>-0.189818</td>\n",
       "      <td>0.169378</td>\n",
       "      <td>0.613736</td>\n",
       "      <td>-0.094724</td>\n",
       "      <td>0.349380</td>\n",
       "      <td>-0.415492</td>\n",
       "      <td>-0.117175</td>\n",
       "      <td>-0.042213</td>\n",
       "      <td>0.035832</td>\n",
       "      <td>-0.220276</td>\n",
       "      <td>0.360921</td>\n",
       "      <td>-0.020970</td>\n",
       "      <td>0.007781</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.208080</td>\n",
       "      <td>-0.338935</td>\n",
       "      <td>-0.368579</td>\n",
       "      <td>0.246758</td>\n",
       "      <td>-0.011785</td>\n",
       "      <td>-0.066307</td>\n",
       "      <td>0.215017</td>\n",
       "      <td>0.233765</td>\n",
       "      <td>0.224827</td>\n",
       "      <td>0.114647</td>\n",
       "      <td>0.271403</td>\n",
       "      <td>-0.235074</td>\n",
       "      <td>0.101809</td>\n",
       "      <td>-0.018171</td>\n",
       "      <td>-0.030644</td>\n",
       "      <td>0.005704</td>\n",
       "      <td>-0.122396</td>\n",
       "      <td>-0.144829</td>\n",
       "      <td>-0.384027</td>\n",
       "      <td>0.236081</td>\n",
       "      <td>0.185890</td>\n",
       "      <td>-0.417878</td>\n",
       "      <td>0.813251</td>\n",
       "      <td>-0.045555</td>\n",
       "      <td>-0.202402</td>\n",
       "      <td>0.031545</td>\n",
       "      <td>0.069139</td>\n",
       "      <td>0.430678</td>\n",
       "      <td>0.088536</td>\n",
       "      <td>-0.022661</td>\n",
       "      <td>0.147315</td>\n",
       "      <td>0.157243</td>\n",
       "      <td>0.102490</td>\n",
       "      <td>0.265023</td>\n",
       "      <td>-0.115462</td>\n",
       "      <td>0.386733</td>\n",
       "      <td>-0.120474</td>\n",
       "      <td>0.233064</td>\n",
       "      <td>-0.226294</td>\n",
       "      <td>0.010265</td>\n",
       "      <td>0.327544</td>\n",
       "      <td>0.116238</td>\n",
       "      <td>0.536446</td>\n",
       "      <td>0.406257</td>\n",
       "      <td>-0.096926</td>\n",
       "      <td>0.145454</td>\n",
       "      <td>0.727021</td>\n",
       "      <td>0.282504</td>\n",
       "      <td>0.061588</td>\n",
       "      <td>-0.021145</td>\n",
       "      <td>-0.666585</td>\n",
       "      <td>-0.161876</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.138440</td>\n",
       "      <td>-0.454783</td>\n",
       "      <td>-0.251295</td>\n",
       "      <td>0.261801</td>\n",
       "      <td>-0.392919</td>\n",
       "      <td>-0.304634</td>\n",
       "      <td>-0.141169</td>\n",
       "      <td>0.351133</td>\n",
       "      <td>-0.342971</td>\n",
       "      <td>-0.220899</td>\n",
       "      <td>-0.159482</td>\n",
       "      <td>0.343924</td>\n",
       "      <td>-0.299267</td>\n",
       "      <td>0.573308</td>\n",
       "      <td>-0.046271</td>\n",
       "      <td>-0.141913</td>\n",
       "      <td>0.147154</td>\n",
       "      <td>-0.099693</td>\n",
       "      <td>0.182339</td>\n",
       "      <td>0.119959</td>\n",
       "      <td>0.203136</td>\n",
       "      <td>-0.051442</td>\n",
       "      <td>0.281536</td>\n",
       "      <td>0.216792</td>\n",
       "      <td>-0.214773</td>\n",
       "      <td>-0.166411</td>\n",
       "      <td>0.237822</td>\n",
       "      <td>-0.481369</td>\n",
       "      <td>0.515954</td>\n",
       "      <td>0.083816</td>\n",
       "      <td>0.030179</td>\n",
       "      <td>-0.116629</td>\n",
       "      <td>-0.006645</td>\n",
       "      <td>-0.180737</td>\n",
       "      <td>-0.316803</td>\n",
       "      <td>0.312707</td>\n",
       "      <td>0.134614</td>\n",
       "      <td>0.285130</td>\n",
       "      <td>-0.100552</td>\n",
       "      <td>0.281081</td>\n",
       "      <td>-0.102719</td>\n",
       "      <td>-0.196316</td>\n",
       "      <td>0.499533</td>\n",
       "      <td>0.397943</td>\n",
       "      <td>0.055209</td>\n",
       "      <td>0.149815</td>\n",
       "      <td>0.110090</td>\n",
       "      <td>-0.737400</td>\n",
       "      <td>0.205180</td>\n",
       "      <td>-0.361550</td>\n",
       "      <td>-0.101456</td>\n",
       "      <td>0.098347</td>\n",
       "      <td>-0.314998</td>\n",
       "      <td>0.277956</td>\n",
       "      <td>-0.053242</td>\n",
       "      <td>-0.515852</td>\n",
       "      <td>-0.070509</td>\n",
       "      <td>-0.103246</td>\n",
       "      <td>0.172510</td>\n",
       "      <td>0.036080</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S010_002_00000014</td>\n",
       "      <td>-2.084378</td>\n",
       "      <td>-0.676196</td>\n",
       "      <td>-0.634774</td>\n",
       "      <td>-0.035407</td>\n",
       "      <td>-1.076634</td>\n",
       "      <td>1.064610</td>\n",
       "      <td>0.421051</td>\n",
       "      <td>0.349194</td>\n",
       "      <td>1.062961</td>\n",
       "      <td>-0.554128</td>\n",
       "      <td>1.005786</td>\n",
       "      <td>-0.069664</td>\n",
       "      <td>1.659366</td>\n",
       "      <td>-0.199920</td>\n",
       "      <td>0.040776</td>\n",
       "      <td>0.550168</td>\n",
       "      <td>0.199435</td>\n",
       "      <td>-0.326052</td>\n",
       "      <td>-0.344036</td>\n",
       "      <td>-0.012860</td>\n",
       "      <td>0.157003</td>\n",
       "      <td>0.315434</td>\n",
       "      <td>-0.607336</td>\n",
       "      <td>0.128673</td>\n",
       "      <td>-0.182047</td>\n",
       "      <td>-0.582677</td>\n",
       "      <td>-0.845514</td>\n",
       "      <td>0.151586</td>\n",
       "      <td>0.676436</td>\n",
       "      <td>-1.160942</td>\n",
       "      <td>0.834660</td>\n",
       "      <td>0.559527</td>\n",
       "      <td>0.062448</td>\n",
       "      <td>0.098751</td>\n",
       "      <td>0.790910</td>\n",
       "      <td>-1.331400</td>\n",
       "      <td>-0.420499</td>\n",
       "      <td>1.006220</td>\n",
       "      <td>-0.579372</td>\n",
       "      <td>-0.487405</td>\n",
       "      <td>0.174867</td>\n",
       "      <td>0.241545</td>\n",
       "      <td>0.503933</td>\n",
       "      <td>-0.003340</td>\n",
       "      <td>0.649263</td>\n",
       "      <td>0.347613</td>\n",
       "      <td>0.122806</td>\n",
       "      <td>0.198099</td>\n",
       "      <td>1.032336</td>\n",
       "      <td>-0.107051</td>\n",
       "      <td>-0.784489</td>\n",
       "      <td>0.308524</td>\n",
       "      <td>-0.709832</td>\n",
       "      <td>0.152530</td>\n",
       "      <td>1.094268</td>\n",
       "      <td>-0.154932</td>\n",
       "      <td>-0.508429</td>\n",
       "      <td>0.916107</td>\n",
       "      <td>1.133092</td>\n",
       "      <td>0.275372</td>\n",
       "      <td>-0.013103</td>\n",
       "      <td>-0.048398</td>\n",
       "      <td>0.217121</td>\n",
       "      <td>0.071053</td>\n",
       "      <td>-0.533557</td>\n",
       "      <td>-0.383654</td>\n",
       "      <td>0.220762</td>\n",
       "      <td>-0.620202</td>\n",
       "      <td>-0.194038</td>\n",
       "      <td>-0.392315</td>\n",
       "      <td>0.465379</td>\n",
       "      <td>-0.957329</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.543926</td>\n",
       "      <td>-0.777516</td>\n",
       "      <td>0.819456</td>\n",
       "      <td>0.247632</td>\n",
       "      <td>-0.456008</td>\n",
       "      <td>-0.459553</td>\n",
       "      <td>-1.114822</td>\n",
       "      <td>1.020849</td>\n",
       "      <td>-0.416438</td>\n",
       "      <td>0.359438</td>\n",
       "      <td>-1.369286</td>\n",
       "      <td>-0.018680</td>\n",
       "      <td>0.927173</td>\n",
       "      <td>1.857118</td>\n",
       "      <td>-0.799661</td>\n",
       "      <td>0.517617</td>\n",
       "      <td>0.775582</td>\n",
       "      <td>-0.690511</td>\n",
       "      <td>1.462198</td>\n",
       "      <td>0.247171</td>\n",
       "      <td>0.528519</td>\n",
       "      <td>2.570713</td>\n",
       "      <td>-0.294436</td>\n",
       "      <td>-2.222973</td>\n",
       "      <td>0.030868</td>\n",
       "      <td>0.985020</td>\n",
       "      <td>0.653243</td>\n",
       "      <td>-0.596921</td>\n",
       "      <td>-0.380563</td>\n",
       "      <td>-1.017879</td>\n",
       "      <td>1.352299</td>\n",
       "      <td>-0.940821</td>\n",
       "      <td>-0.290740</td>\n",
       "      <td>-0.032887</td>\n",
       "      <td>-2.315440</td>\n",
       "      <td>1.252694</td>\n",
       "      <td>-0.347594</td>\n",
       "      <td>-0.327893</td>\n",
       "      <td>-0.012920</td>\n",
       "      <td>0.576107</td>\n",
       "      <td>-1.516343</td>\n",
       "      <td>-0.153630</td>\n",
       "      <td>0.093365</td>\n",
       "      <td>0.339262</td>\n",
       "      <td>-1.343560</td>\n",
       "      <td>1.204575</td>\n",
       "      <td>2.978340</td>\n",
       "      <td>0.317038</td>\n",
       "      <td>-0.681593</td>\n",
       "      <td>2.336733</td>\n",
       "      <td>-0.412725</td>\n",
       "      <td>-1.844573</td>\n",
       "      <td>0.308685</td>\n",
       "      <td>0.735878</td>\n",
       "      <td>-1.140252</td>\n",
       "      <td>1.109510</td>\n",
       "      <td>0.608688</td>\n",
       "      <td>1.455933</td>\n",
       "      <td>3.117443</td>\n",
       "      <td>-1.734795</td>\n",
       "      <td>2.164242</td>\n",
       "      <td>0.067989</td>\n",
       "      <td>-1.394213</td>\n",
       "      <td>0.613614</td>\n",
       "      <td>-1.490832</td>\n",
       "      <td>0.545536</td>\n",
       "      <td>2.850475</td>\n",
       "      <td>-0.081500</td>\n",
       "      <td>-1.577114</td>\n",
       "      <td>0.103517</td>\n",
       "      <td>0.041479</td>\n",
       "      <td>-0.632654</td>\n",
       "      <td>0.825305</td>\n",
       "      <td>1.053799</td>\n",
       "      <td>0.315749</td>\n",
       "      <td>0.766560</td>\n",
       "      <td>0.156963</td>\n",
       "      <td>0.779681</td>\n",
       "      <td>2.000513</td>\n",
       "      <td>-0.509617</td>\n",
       "      <td>-0.556693</td>\n",
       "      <td>-0.132361</td>\n",
       "      <td>-0.722115</td>\n",
       "      <td>-1.321452</td>\n",
       "      <td>-0.273689</td>\n",
       "      <td>0.039432</td>\n",
       "      <td>0.464650</td>\n",
       "      <td>0.586555</td>\n",
       "      <td>-0.489672</td>\n",
       "      <td>0.503398</td>\n",
       "      <td>-1.519616</td>\n",
       "      <td>-0.235685</td>\n",
       "      <td>-1.342502</td>\n",
       "      <td>0.025880</td>\n",
       "      <td>-0.395547</td>\n",
       "      <td>-1.232639</td>\n",
       "      <td>-0.790715</td>\n",
       "      <td>-1.634736</td>\n",
       "      <td>0.063484</td>\n",
       "      <td>-0.435693</td>\n",
       "      <td>0.577848</td>\n",
       "      <td>0.325519</td>\n",
       "      <td>-0.307315</td>\n",
       "      <td>-0.062977</td>\n",
       "      <td>0.763930</td>\n",
       "      <td>0.751303</td>\n",
       "      <td>1.184353</td>\n",
       "      <td>-0.725602</td>\n",
       "      <td>0.415367</td>\n",
       "      <td>0.309578</td>\n",
       "      <td>-0.910546</td>\n",
       "      <td>0.231463</td>\n",
       "      <td>-0.982245</td>\n",
       "      <td>1.031710</td>\n",
       "      <td>-0.048461</td>\n",
       "      <td>-0.150629</td>\n",
       "      <td>0.943249</td>\n",
       "      <td>0.206317</td>\n",
       "      <td>0.353495</td>\n",
       "      <td>0.543397</td>\n",
       "      <td>-0.448460</td>\n",
       "      <td>0.479497</td>\n",
       "      <td>0.617494</td>\n",
       "      <td>-0.576516</td>\n",
       "      <td>-0.180667</td>\n",
       "      <td>0.079660</td>\n",
       "      <td>0.168116</td>\n",
       "      <td>-0.177256</td>\n",
       "      <td>0.045798</td>\n",
       "      <td>-0.441170</td>\n",
       "      <td>-0.039238</td>\n",
       "      <td>0.730861</td>\n",
       "      <td>0.581710</td>\n",
       "      <td>0.550412</td>\n",
       "      <td>0.178306</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.566393</td>\n",
       "      <td>0.008340</td>\n",
       "      <td>0.090055</td>\n",
       "      <td>-0.186366</td>\n",
       "      <td>0.169934</td>\n",
       "      <td>0.021925</td>\n",
       "      <td>0.441668</td>\n",
       "      <td>-0.291815</td>\n",
       "      <td>-0.327298</td>\n",
       "      <td>0.151318</td>\n",
       "      <td>0.534177</td>\n",
       "      <td>0.076912</td>\n",
       "      <td>-0.206223</td>\n",
       "      <td>-0.109936</td>\n",
       "      <td>0.065134</td>\n",
       "      <td>-0.119303</td>\n",
       "      <td>0.413021</td>\n",
       "      <td>-0.236286</td>\n",
       "      <td>0.485847</td>\n",
       "      <td>0.102939</td>\n",
       "      <td>-0.031126</td>\n",
       "      <td>0.155380</td>\n",
       "      <td>-0.043026</td>\n",
       "      <td>0.175100</td>\n",
       "      <td>-0.133107</td>\n",
       "      <td>0.033305</td>\n",
       "      <td>-0.415193</td>\n",
       "      <td>-0.517826</td>\n",
       "      <td>0.380221</td>\n",
       "      <td>0.132011</td>\n",
       "      <td>0.422722</td>\n",
       "      <td>0.493911</td>\n",
       "      <td>-0.131026</td>\n",
       "      <td>0.546813</td>\n",
       "      <td>0.188525</td>\n",
       "      <td>-0.050346</td>\n",
       "      <td>-0.523672</td>\n",
       "      <td>0.194303</td>\n",
       "      <td>-0.069167</td>\n",
       "      <td>0.053773</td>\n",
       "      <td>-0.197326</td>\n",
       "      <td>-0.335832</td>\n",
       "      <td>-0.192127</td>\n",
       "      <td>0.050395</td>\n",
       "      <td>-0.162486</td>\n",
       "      <td>-0.087314</td>\n",
       "      <td>-0.197637</td>\n",
       "      <td>0.037382</td>\n",
       "      <td>-0.383737</td>\n",
       "      <td>0.342681</td>\n",
       "      <td>-0.372748</td>\n",
       "      <td>-0.287816</td>\n",
       "      <td>0.328763</td>\n",
       "      <td>-0.445657</td>\n",
       "      <td>0.411410</td>\n",
       "      <td>-0.245420</td>\n",
       "      <td>0.072002</td>\n",
       "      <td>-0.035890</td>\n",
       "      <td>0.104423</td>\n",
       "      <td>0.170222</td>\n",
       "      <td>0.145319</td>\n",
       "      <td>0.382768</td>\n",
       "      <td>-0.306205</td>\n",
       "      <td>0.129152</td>\n",
       "      <td>-0.183744</td>\n",
       "      <td>-0.529519</td>\n",
       "      <td>0.198279</td>\n",
       "      <td>0.058881</td>\n",
       "      <td>-0.161410</td>\n",
       "      <td>-0.511821</td>\n",
       "      <td>-0.369095</td>\n",
       "      <td>-0.137649</td>\n",
       "      <td>0.041778</td>\n",
       "      <td>0.089169</td>\n",
       "      <td>-0.385662</td>\n",
       "      <td>0.078466</td>\n",
       "      <td>-0.043410</td>\n",
       "      <td>0.289149</td>\n",
       "      <td>-0.049194</td>\n",
       "      <td>0.086328</td>\n",
       "      <td>0.190775</td>\n",
       "      <td>-0.084317</td>\n",
       "      <td>0.039347</td>\n",
       "      <td>-0.040895</td>\n",
       "      <td>-0.007906</td>\n",
       "      <td>0.209714</td>\n",
       "      <td>-0.187348</td>\n",
       "      <td>0.042160</td>\n",
       "      <td>0.369111</td>\n",
       "      <td>-0.155851</td>\n",
       "      <td>-0.142377</td>\n",
       "      <td>-0.098879</td>\n",
       "      <td>0.203624</td>\n",
       "      <td>0.079339</td>\n",
       "      <td>0.002289</td>\n",
       "      <td>0.141781</td>\n",
       "      <td>0.072971</td>\n",
       "      <td>0.182311</td>\n",
       "      <td>-0.115285</td>\n",
       "      <td>0.244940</td>\n",
       "      <td>0.285008</td>\n",
       "      <td>-0.158990</td>\n",
       "      <td>0.221658</td>\n",
       "      <td>-0.247090</td>\n",
       "      <td>-0.082899</td>\n",
       "      <td>0.154496</td>\n",
       "      <td>0.279779</td>\n",
       "      <td>0.185300</td>\n",
       "      <td>0.063957</td>\n",
       "      <td>0.160231</td>\n",
       "      <td>0.241484</td>\n",
       "      <td>-0.172270</td>\n",
       "      <td>0.092395</td>\n",
       "      <td>-0.148565</td>\n",
       "      <td>-0.006172</td>\n",
       "      <td>0.221339</td>\n",
       "      <td>-0.120513</td>\n",
       "      <td>-0.058244</td>\n",
       "      <td>0.156907</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.130059</td>\n",
       "      <td>0.007458</td>\n",
       "      <td>0.321026</td>\n",
       "      <td>-0.071255</td>\n",
       "      <td>-0.109881</td>\n",
       "      <td>-0.076796</td>\n",
       "      <td>-0.017891</td>\n",
       "      <td>-0.079960</td>\n",
       "      <td>-0.066078</td>\n",
       "      <td>0.033817</td>\n",
       "      <td>-0.041758</td>\n",
       "      <td>0.324819</td>\n",
       "      <td>0.220990</td>\n",
       "      <td>-0.021265</td>\n",
       "      <td>0.118044</td>\n",
       "      <td>0.068968</td>\n",
       "      <td>-0.214662</td>\n",
       "      <td>0.009142</td>\n",
       "      <td>0.048827</td>\n",
       "      <td>0.011017</td>\n",
       "      <td>0.060845</td>\n",
       "      <td>-0.062490</td>\n",
       "      <td>-0.167027</td>\n",
       "      <td>0.057304</td>\n",
       "      <td>0.109163</td>\n",
       "      <td>0.193754</td>\n",
       "      <td>-0.014018</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            filename         0         1         2         3         4  \\\n",
       "0  S005_001_00000001 -2.422045 -0.514499 -0.504978  0.842154 -0.590387   \n",
       "1  S005_001_00000011 -2.220450 -0.824084 -0.442616  0.867950 -0.807278   \n",
       "2  S010_001_00000001 -2.991666  5.081572 -0.801330 -0.892331  0.463246   \n",
       "3  S010_002_00000001 -3.000261  4.987984 -0.886566 -1.187165  0.506597   \n",
       "4  S010_002_00000014 -2.084378 -0.676196 -0.634774 -0.035407 -1.076634   \n",
       "\n",
       "          5         6         7         8         9        10        11  \\\n",
       "0  0.944383  0.280484  0.516634  0.140506  0.099023  2.276509  0.797066   \n",
       "1  0.911351  0.409966  0.716671  0.594750  0.450701  1.952625  0.880323   \n",
       "2  0.355654  0.286743  0.353372 -0.153716  0.852929  0.841748  1.674563   \n",
       "3  0.538944  0.319966  0.464281 -0.372665  0.927417  0.857080  1.715775   \n",
       "4  1.064610  0.421051  0.349194  1.062961 -0.554128  1.005786 -0.069664   \n",
       "\n",
       "         12        13        14        15        16        17        18  \\\n",
       "0  1.089989 -0.237536  0.382572 -0.420350  1.301158 -0.828614 -0.521586   \n",
       "1  0.949376 -0.323829  0.700085 -0.695030  1.392928 -0.303409 -0.586684   \n",
       "2  0.674734 -0.668415  1.871574 -1.444211  0.043157 -1.441852 -1.507439   \n",
       "3  0.448777 -0.621174  1.838618 -1.481218  0.082254 -1.280967 -1.600561   \n",
       "4  1.659366 -0.199920  0.040776  0.550168  0.199435 -0.326052 -0.344036   \n",
       "\n",
       "         19        20        21        22        23        24        25  \\\n",
       "0  0.474950  1.277160  0.574633 -0.707459  0.128109  0.266873  0.362604   \n",
       "1  0.371747  1.676886  0.354014 -0.603328  0.333678  0.154593  0.230180   \n",
       "2 -3.270778  2.853501  2.892128 -0.892537 -0.603601 -1.731733  1.510062   \n",
       "3 -3.601663  2.611679  2.770883 -1.129668 -0.671729 -1.906041  1.348601   \n",
       "4 -0.012860  0.157003  0.315434 -0.607336  0.128673 -0.182047 -0.582677   \n",
       "\n",
       "         26        27        28        29        30        31        32  \\\n",
       "0 -0.965180 -0.164825  0.645037 -0.832571 -0.830679 -0.720573 -0.674197   \n",
       "1 -1.178306  0.001869  1.250172 -0.934775 -0.927225 -0.826885 -1.027498   \n",
       "2  0.663404  0.252319  0.852474  1.654812 -0.080267  0.914169 -1.674726   \n",
       "3  0.849921  0.381892  0.640950  1.650235  0.040808  1.068203 -1.692235   \n",
       "4 -0.845514  0.151586  0.676436 -1.160942  0.834660  0.559527  0.062448   \n",
       "\n",
       "         33        34        35        36        37        38        39  \\\n",
       "0  0.863832 -0.027864 -1.088777  2.572479  0.962688  0.222646  0.055087   \n",
       "1  0.873422  0.223782 -0.952116  2.333242  0.665000  0.913002 -0.546047   \n",
       "2  2.431463  0.708107 -2.982527  0.228068  0.164293 -0.082841 -0.279028   \n",
       "3  2.746948  0.619423 -3.086794  0.489284  0.063288 -0.145932 -0.211169   \n",
       "4  0.098751  0.790910 -1.331400 -0.420499  1.006220 -0.579372 -0.487405   \n",
       "\n",
       "         40        41        42        43        44        45        46  \\\n",
       "0 -2.293699 -1.649053 -1.001459 -0.067005  1.487614  0.158582 -0.789824   \n",
       "1 -2.473572 -1.593174 -1.281094 -0.392417  1.897944  0.269570 -0.920724   \n",
       "2  1.709136 -0.261806 -0.774093  0.709187  0.226467 -0.200427 -1.907094   \n",
       "3  1.825597 -0.187447 -0.913887  0.660509  0.157651 -0.365176 -2.132250   \n",
       "4  0.174867  0.241545  0.503933 -0.003340  0.649263  0.347613  0.122806   \n",
       "\n",
       "         47        48        49        50        51        52        53  \\\n",
       "0 -1.092460  3.063921  3.035338  3.191445  0.804751  0.444656  0.767143   \n",
       "1 -0.771628  3.196625  2.724439  3.103635  0.788405  0.341103  0.883828   \n",
       "2  0.768830  0.720327 -0.736454  0.542728  0.226437  0.289722 -0.258221   \n",
       "3  0.845049  0.753795 -0.730130  0.778739  0.069231  0.215949 -0.265140   \n",
       "4  0.198099  1.032336 -0.107051 -0.784489  0.308524 -0.709832  0.152530   \n",
       "\n",
       "         54        55        56        57        58        59        60  \\\n",
       "0 -0.892039  2.627187  1.414970  3.964590 -1.229673  0.300425  1.047827   \n",
       "1 -1.057583  2.810453  1.293946  3.683671 -1.134929  0.027931  1.462615   \n",
       "2  0.136392 -0.049586  0.289519 -0.633701  0.188268  0.256398 -0.373638   \n",
       "3 -0.104827  0.053552  0.225323 -0.627534  0.086951  0.593712 -0.329511   \n",
       "4  1.094268 -0.154932 -0.508429  0.916107  1.133092  0.275372 -0.013103   \n",
       "\n",
       "         61        62        63        64        65        66        67  \\\n",
       "0  2.345936 -0.287113  1.918857  0.406911  0.566958 -1.468247  0.798881   \n",
       "1  2.107863 -0.268499  1.653162  0.494796  0.631624 -1.191445  1.090118   \n",
       "2 -0.423397  0.394149 -0.048800 -0.335222  0.104925  1.114764 -0.022974   \n",
       "3 -0.483027  0.506543 -0.023146 -0.256002  0.243578  0.991077  0.304715   \n",
       "4 -0.048398  0.217121  0.071053 -0.533557 -0.383654  0.220762 -0.620202   \n",
       "\n",
       "         68        69        70        71        72        73        74  \\\n",
       "0 -2.347889  0.378051 -0.033132 -0.477294 -1.308110 -0.119284 -0.100177   \n",
       "1 -1.765492  0.356764 -0.878103 -0.270191 -0.706211  0.313790 -0.030242   \n",
       "2  0.251713 -0.813759  0.661019  0.568348  0.597469 -0.646597 -0.062673   \n",
       "3  0.191549 -0.771337  0.678660  0.615216  0.714461 -0.721590 -0.220720   \n",
       "4 -0.194038 -0.392315  0.465379 -0.957329  0.055925  0.543926 -0.777516   \n",
       "\n",
       "         75        76        77        78        79        80        81  \\\n",
       "0  0.428898 -1.898794 -0.265512  1.048010 -0.184117  0.677399  0.423569   \n",
       "1  0.614113 -1.598050  0.179738  0.787420 -0.635254  0.749319  0.641541   \n",
       "2  0.483450 -0.300936 -0.325422  0.107917  0.217974  0.059186  0.021989   \n",
       "3  0.298385 -0.106454 -0.323289  0.238201  0.439817 -0.171312 -0.071290   \n",
       "4  0.819456  0.247632 -0.456008 -0.459553 -1.114822  1.020849 -0.416438   \n",
       "\n",
       "         82        83        84        85        86        87        88  \\\n",
       "0  1.945772  0.901140  0.822684  0.638795 -2.139044 -1.037973 -0.280762   \n",
       "1  2.113908  1.177171  1.182172  0.758176 -1.831354 -0.796943 -0.584075   \n",
       "2 -0.233618 -0.240524 -0.110295 -0.593550 -0.124802  0.185869 -0.251905   \n",
       "3 -0.322492 -0.123676  0.142975 -0.344624 -0.134088  0.388923 -0.397284   \n",
       "4  0.359438 -1.369286 -0.018680  0.927173  1.857118 -0.799661  0.517617   \n",
       "\n",
       "         89        90        91        92        93        94        95  \\\n",
       "0 -0.683216  0.825780  3.139535 -1.051019 -0.167132  0.840751  0.110484   \n",
       "1 -0.208147  1.196413  3.241490 -0.863080 -0.098399  0.700504  0.212661   \n",
       "2  0.110858 -0.215797 -0.042777 -0.107726 -0.144958  0.109767 -0.038723   \n",
       "3 -0.027440 -0.078154 -0.001789 -0.031790 -0.141844  0.098757  0.096051   \n",
       "4  0.775582 -0.690511  1.462198  0.247171  0.528519  2.570713 -0.294436   \n",
       "\n",
       "         96        97        98        99       100       101       102  \\\n",
       "0  0.920122  1.417406 -1.064067  1.412985  1.201106 -0.843437 -1.613027   \n",
       "1  1.231197  1.717772 -0.733260  1.209952  1.281554 -0.860577 -1.823862   \n",
       "2  0.180957 -0.185896 -0.196481  0.065829 -0.037164  0.034334  0.311325   \n",
       "3 -0.001853 -0.160831 -0.015280  0.141139 -0.048071  0.190158  0.366872   \n",
       "4 -2.222973  0.030868  0.985020  0.653243 -0.596921 -0.380563 -1.017879   \n",
       "\n",
       "        103       104       105       106       107       108       109  \\\n",
       "0  0.097397  0.523434  1.061154  0.979967  0.024707  0.526197 -0.146056   \n",
       "1 -0.155897  1.217095  0.891797  0.658699 -0.009148  0.104836 -0.383683   \n",
       "2 -0.242133 -0.177846 -0.082437  0.099571  0.198227  0.027904  0.023973   \n",
       "3 -0.449399 -0.232137 -0.076804 -0.000629 -0.143414 -0.069063  0.112402   \n",
       "4  1.352299 -0.940821 -0.290740 -0.032887 -2.315440  1.252694 -0.347594   \n",
       "\n",
       "        110       111       112       113       114       115       116  \\\n",
       "0 -0.767169 -0.065331  0.541808  0.391427  0.821145 -0.669368  0.404505   \n",
       "1 -0.525198 -0.001017  0.201325  0.410508 -0.335771 -0.833709  0.394416   \n",
       "2 -0.086693 -0.216920 -0.148408 -0.018120  0.091168 -0.048020  0.362139   \n",
       "3  0.037352 -0.350307 -0.146011  0.104279  0.061891  0.061902  0.130847   \n",
       "4 -0.327893 -0.012920  0.576107 -1.516343 -0.153630  0.093365  0.339262   \n",
       "\n",
       "        117       118       119       120       121       122       123  \\\n",
       "0  0.055005 -0.782735 -0.013662  0.112026  0.810741  0.071041  0.462119   \n",
       "1  0.452575 -0.152868 -0.418220  0.100647  0.260156  0.397543  0.133620   \n",
       "2  0.070480 -0.314073 -0.212033 -0.109785  0.216676 -0.296590 -0.126223   \n",
       "3 -0.118661 -0.077212 -0.066519 -0.279594 -0.060427 -0.317562 -0.177887   \n",
       "4 -1.343560  1.204575  2.978340  0.317038 -0.681593  2.336733 -0.412725   \n",
       "\n",
       "        124       125       126       127       128       129       130  \\\n",
       "0  0.506668  0.006435  0.231409  0.469970 -0.205928  0.153500  0.448333   \n",
       "1 -0.307294  0.638067 -0.107472 -0.498461  0.079847  0.264229 -0.664943   \n",
       "2 -0.222525  0.001472  0.093955  0.108034  0.055080 -0.136495  0.018403   \n",
       "3  0.130270  0.047124 -0.032765 -0.082232  0.145574 -0.554348 -0.125889   \n",
       "4 -1.844573  0.308685  0.735878 -1.140252  1.109510  0.608688  1.455933   \n",
       "\n",
       "        131       132       133       134       135       136       137  \\\n",
       "0 -0.146562 -0.585131  0.357613 -0.352428  0.729494 -0.114007  0.180825   \n",
       "1  0.974927 -0.016227 -0.566363  0.276577  0.171008  0.264465 -0.110557   \n",
       "2 -0.133705  0.280994  0.061648  0.308039 -0.173703 -0.216687 -0.134579   \n",
       "3 -0.106686  0.285143  0.300389  0.302394 -0.109391 -0.117863 -0.213878   \n",
       "4  3.117443 -1.734795  2.164242  0.067989 -1.394213  0.613614 -1.490832   \n",
       "\n",
       "        138       139       140       141       142       143       144  \\\n",
       "0  0.294510  0.021162  0.501719  0.749670 -0.425777 -0.240957 -0.508020   \n",
       "1 -0.171294  0.528732 -0.155434  0.153182 -0.231722  0.107335  0.231366   \n",
       "2  0.430395 -0.250557  0.240227 -0.396795  0.063419 -0.002313 -0.256632   \n",
       "3  0.483013 -0.157113  0.251764 -0.146132  0.216774  0.291309 -0.093583   \n",
       "4  0.545536  2.850475 -0.081500 -1.577114  0.103517  0.041479 -0.632654   \n",
       "\n",
       "        145       146       147       148       149       150       151  \\\n",
       "0  0.012814 -0.183088 -0.007520  0.236373 -0.576461 -0.543274  0.518400   \n",
       "1 -0.054775 -0.522291 -0.228046 -0.538766  0.050291  0.539805 -0.414306   \n",
       "2  0.167892 -0.080438  0.066788  0.355535 -0.015503  0.034047  0.018479   \n",
       "3  0.141399 -0.127420  0.195819  0.315281  0.249637 -0.084056 -0.343581   \n",
       "4  0.825305  1.053799  0.315749  0.766560  0.156963  0.779681  2.000513   \n",
       "\n",
       "        152       153       154       155       156       157       158  \\\n",
       "0  0.030273  0.501695  0.375954  0.489191  0.943326  0.093237 -0.221395   \n",
       "1  0.523218  0.684844 -0.119669 -0.483274 -0.270304 -0.043544  0.020570   \n",
       "2 -0.016418 -0.036859  0.360075  0.103245 -0.152916  0.226950  0.085335   \n",
       "3 -0.130224  0.130653  0.102455  0.392232  0.180607 -0.033724  0.162908   \n",
       "4 -0.509617 -0.556693 -0.132361 -0.722115 -1.321452 -0.273689  0.039432   \n",
       "\n",
       "        159       160       161       162       163       164       165  \\\n",
       "0  0.378368  0.052560  0.092875  0.378795  0.200418 -0.180958  0.044293   \n",
       "1 -0.072609  0.166963 -0.154771 -0.042501  0.302268 -0.634133 -0.442756   \n",
       "2  0.386814 -0.012485  0.082011  0.312625  0.022827  0.031809  0.151288   \n",
       "3  0.144205 -0.053717  0.251422 -0.018133  0.017424  0.094911  0.050630   \n",
       "4  0.464650  0.586555 -0.489672  0.503398 -1.519616 -0.235685 -1.342502   \n",
       "\n",
       "        166       167       168       169       170       171       172  \\\n",
       "0 -0.238839 -0.492745  0.665203 -0.111076  0.158070  0.450825 -0.508927   \n",
       "1  0.402583 -0.115385 -0.713197 -0.057345  0.293745 -0.062184  0.487431   \n",
       "2 -0.141875  0.169544  0.129223  0.032306 -0.109390  0.250066 -0.023959   \n",
       "3 -0.253298  0.341791 -0.088841  0.002981 -0.089736  0.391152  0.180914   \n",
       "4  0.025880 -0.395547 -1.232639 -0.790715 -1.634736  0.063484 -0.435693   \n",
       "\n",
       "        173       174       175       176       177       178       179  \\\n",
       "0  0.497720  0.418634  0.281906 -0.029992  0.193345 -0.010611  0.394107   \n",
       "1  0.009849 -0.182655  0.363003 -0.172662  0.208688 -0.353348 -0.086140   \n",
       "2 -0.197970  0.159414 -0.179555 -0.015777  0.060492  0.298184 -0.019445   \n",
       "3  0.118013  0.059143  0.063760  0.028314  0.405807  0.117760 -0.323205   \n",
       "4  0.577848  0.325519 -0.307315 -0.062977  0.763930  0.751303  1.184353   \n",
       "\n",
       "        180       181       182       183       184       185       186  \\\n",
       "0  0.476401  0.015009  0.306408 -0.364764  0.226373  0.023008 -0.005134   \n",
       "1 -0.450511 -0.531916 -0.456386  0.701345 -0.249106 -0.635945 -0.237587   \n",
       "2 -0.074952 -0.361177  0.145191 -0.338683  0.081664  0.248201  0.160680   \n",
       "3 -0.145816  0.049484 -0.059181 -0.355938 -0.000792  0.080526  0.039515   \n",
       "4 -0.725602  0.415367  0.309578 -0.910546  0.231463 -0.982245  1.031710   \n",
       "\n",
       "        187       188       189       190       191       192       193  \\\n",
       "0  0.526651 -0.191662  0.116471 -0.538892 -0.131936  0.073221  0.240766   \n",
       "1 -0.572241  0.498425 -0.450956  0.209684  0.077766 -0.159234 -0.661501   \n",
       "2  0.035077  0.183261  0.377370 -0.243978  0.171284 -0.028362 -0.156530   \n",
       "3 -0.287098  0.311920  0.362632 -0.147260  0.201945  0.067685  0.089192   \n",
       "4 -0.048461 -0.150629  0.943249  0.206317  0.353495  0.543397 -0.448460   \n",
       "\n",
       "        194       195       196       197       198       199       200  \\\n",
       "0  0.313051 -0.020694  0.003187 -0.355131 -0.554434  0.059641  0.492502   \n",
       "1  0.066634 -0.087044 -0.520225  0.226015  0.647374  0.331490 -0.235818   \n",
       "2  0.130317  0.021539  0.097140  0.048384  0.024004 -0.056803  0.181153   \n",
       "3  0.072969  0.158969  0.215558  0.059033  0.044240  0.056766  0.102684   \n",
       "4  0.479497  0.617494 -0.576516 -0.180667  0.079660  0.168116 -0.177256   \n",
       "\n",
       "        201       202       203       204       205       206       207  \\\n",
       "0 -0.723126 -0.479278 -0.477729 -0.083133  0.480568 -0.718129 -0.165555   \n",
       "1  0.743090  0.284794  0.274975  0.223156 -0.539892  0.611208  0.202739   \n",
       "2 -0.211795 -0.214565  0.225148  0.303655 -0.258448  0.075328 -0.322921   \n",
       "3 -0.197176 -0.429740 -0.108089  0.295853 -0.018504  0.133207 -0.275633   \n",
       "4  0.045798 -0.441170 -0.039238  0.730861  0.581710  0.550412  0.178306   \n",
       "\n",
       "        208       209       210       211       212       213       214  \\\n",
       "0 -0.983583  0.840593 -0.406568  1.046419 -0.083893  0.007322 -0.004909   \n",
       "1  0.846151 -0.718736  0.185858 -1.101641 -0.229508  0.061440  0.024907   \n",
       "2  0.367936 -0.206992  0.091420 -0.025859  0.406632  0.190404  0.308664   \n",
       "3  0.202609 -0.099401  0.194353 -0.136984  0.093678  0.358612 -0.021945   \n",
       "4 -0.055151 -0.566393  0.008340  0.090055 -0.186366  0.169934  0.021925   \n",
       "\n",
       "        215       216       217       218       219       220       221  \\\n",
       "0 -0.079930 -0.120973 -0.838983 -0.904007  0.515731  0.894112 -1.143567   \n",
       "1  0.001952  0.489348  0.783492  0.913868 -0.396426 -0.882205  0.584180   \n",
       "2 -0.078961  0.199932 -0.406716 -0.157563 -0.283261 -0.007741 -0.285453   \n",
       "3  0.005724 -0.026893 -0.690286 -0.043817 -0.110333  0.277275 -0.231797   \n",
       "4  0.441668 -0.291815 -0.327298  0.151318  0.534177  0.076912 -0.206223   \n",
       "\n",
       "        222       223       224       225       226       227       228  \\\n",
       "0  1.019680 -0.173489  0.201580 -0.576973  0.202880  0.608302 -1.305887   \n",
       "1 -0.965429  0.050864  0.147838  0.248824 -0.176336 -0.678960  1.207026   \n",
       "2  0.054580  0.310809  0.370762  0.297125 -0.321039 -0.153430 -0.253430   \n",
       "3  0.116795  0.028782  0.285869  0.252962  0.022652 -0.578489  0.060782   \n",
       "4 -0.109936  0.065134 -0.119303  0.413021 -0.236286  0.485847  0.102939   \n",
       "\n",
       "        229       230       231       232       233       234       235  \\\n",
       "0  0.326183 -0.598493  1.040402 -0.499363  0.055896  1.672653 -0.666856   \n",
       "1 -0.050563  0.324167 -1.012958  0.474943 -0.047748 -1.388518  0.584402   \n",
       "2  0.082924  0.128690  0.469325 -0.180198 -0.107041 -0.255766 -0.079094   \n",
       "3 -0.189818  0.169378  0.613736 -0.094724  0.349380 -0.415492 -0.117175   \n",
       "4 -0.031126  0.155380 -0.043026  0.175100 -0.133107  0.033305 -0.415193   \n",
       "\n",
       "        236       237       238       239       240       241       242  \\\n",
       "0  0.848751 -0.350116  0.131658  0.049437 -0.233581  0.703434  0.027097   \n",
       "1 -0.808239  0.192782 -0.491053 -0.101206  0.387043 -0.420627 -0.115148   \n",
       "2 -0.035906  0.061377 -0.343138 -0.120172  0.256778  0.413937 -0.151382   \n",
       "3 -0.042213  0.035832 -0.220276  0.360921 -0.020970  0.007781 -0.179668   \n",
       "4 -0.517826  0.380221  0.132011  0.422722  0.493911 -0.131026  0.546813   \n",
       "\n",
       "        243       244       245       246       247       248       249  \\\n",
       "0  0.441636 -0.455268 -0.251607 -0.011107  0.195264 -0.414941  0.175404   \n",
       "1 -0.075386  0.596870  0.077647  0.242664 -0.233233  0.653358 -0.187237   \n",
       "2  0.154023 -0.233580 -0.468033  0.171741 -0.202337 -0.046027  0.330105   \n",
       "3  0.208080 -0.338935 -0.368579  0.246758 -0.011785 -0.066307  0.215017   \n",
       "4  0.188525 -0.050346 -0.523672  0.194303 -0.069167  0.053773 -0.197326   \n",
       "\n",
       "        250       251       252       253       254       255       256  \\\n",
       "0  1.094966 -0.370902 -0.331974  0.480576 -0.198805  0.354888  0.618082   \n",
       "1 -1.183770  0.346001  0.065111 -0.619420  0.204310 -0.056871 -0.906835   \n",
       "2  0.157430  0.240231  0.212982  0.004662 -0.210189  0.292131 -0.836930   \n",
       "3  0.233765  0.224827  0.114647  0.271403 -0.235074  0.101809 -0.018171   \n",
       "4 -0.335832 -0.192127  0.050395 -0.162486 -0.087314 -0.197637  0.037382   \n",
       "\n",
       "        257       258       259       260       261       262       263  \\\n",
       "0 -0.291748  0.975548  0.550659 -0.415794  0.303245 -0.627194 -0.401057   \n",
       "1  0.559999 -1.231934 -0.588163  0.200391 -0.176763  0.655819  0.219338   \n",
       "2 -0.187183  0.111733  0.026391 -0.123596 -0.229646 -0.153699 -0.081612   \n",
       "3 -0.030644  0.005704 -0.122396 -0.144829 -0.384027  0.236081  0.185890   \n",
       "4 -0.383737  0.342681 -0.372748 -0.287816  0.328763 -0.445657  0.411410   \n",
       "\n",
       "        264       265       266       267       268       269       270  \\\n",
       "0 -0.024082  0.101071 -0.952048  0.776166  0.157719 -0.106416 -0.395406   \n",
       "1  0.011935 -0.209794  0.936342 -0.750864 -0.388577  0.069368  0.118136   \n",
       "2 -0.356637  0.409039 -0.233172  0.020605  0.087255 -0.105354  0.320390   \n",
       "3 -0.417878  0.813251 -0.045555 -0.202402  0.031545  0.069139  0.430678   \n",
       "4 -0.245420  0.072002 -0.035890  0.104423  0.170222  0.145319  0.382768   \n",
       "\n",
       "        271       272       273       274       275       276       277  \\\n",
       "0 -0.443853 -0.536166  0.135406  0.145441  0.443553  0.627004  0.333064   \n",
       "1  0.483215  0.558529 -0.481610 -0.019643 -0.393431 -0.758803 -0.069155   \n",
       "2 -0.139473  0.472985  0.243048  0.144855  0.067172 -0.009456 -0.101887   \n",
       "3  0.088536 -0.022661  0.147315  0.157243  0.102490  0.265023 -0.115462   \n",
       "4 -0.306205  0.129152 -0.183744 -0.529519  0.198279  0.058881 -0.161410   \n",
       "\n",
       "        278       279       280       281       282       283       284  \\\n",
       "0 -0.371739 -0.300969 -0.559815  0.658823 -0.074787  0.105122  0.181579   \n",
       "1  0.419813  0.226089  0.668821 -0.408183  0.007700 -0.070386 -0.085070   \n",
       "2  0.529519 -0.103683  0.263418  0.098345 -0.006643 -0.161891  0.181282   \n",
       "3  0.386733 -0.120474  0.233064 -0.226294  0.010265  0.327544  0.116238   \n",
       "4 -0.511821 -0.369095 -0.137649  0.041778  0.089169 -0.385662  0.078466   \n",
       "\n",
       "        285       286       287       288       289       290       291  \\\n",
       "0 -0.030059 -0.177499  0.550571  0.209239  0.417161  0.354636  0.585453   \n",
       "1  0.159678  0.203354 -0.587928 -0.220395 -0.401240 -0.300075 -0.612535   \n",
       "2  0.042802  0.351489 -0.057814 -0.008520  0.608987  0.125567 -0.254436   \n",
       "3  0.536446  0.406257 -0.096926  0.145454  0.727021  0.282504  0.061588   \n",
       "4 -0.043410  0.289149 -0.049194  0.086328  0.190775 -0.084317  0.039347   \n",
       "\n",
       "        292       293       294       295       296       297       298  \\\n",
       "0 -0.215858  0.393470 -0.294912 -0.119379  0.166032  0.138318  0.205800   \n",
       "1  0.116213 -0.341433  0.315142  0.173374 -0.123385 -0.129131 -0.380350   \n",
       "2 -0.206294 -0.097433  0.186749  0.395433 -0.001360 -0.399197 -0.290370   \n",
       "3 -0.021145 -0.666585 -0.161876  0.003941 -0.138440 -0.454783 -0.251295   \n",
       "4 -0.040895 -0.007906  0.209714 -0.187348  0.042160  0.369111 -0.155851   \n",
       "\n",
       "        299       300       301       302       303       304       305  \\\n",
       "0  0.071766  0.116803  0.015501  0.403927 -0.285879 -0.124954  0.458053   \n",
       "1 -0.039306  0.067938  0.074311 -0.694888  0.230362  0.317080 -0.430243   \n",
       "2 -0.001038 -0.413264 -0.369490 -0.060014 -0.136184 -0.422541  0.453322   \n",
       "3  0.261801 -0.392919 -0.304634 -0.141169  0.351133 -0.342971 -0.220899   \n",
       "4 -0.142377 -0.098879  0.203624  0.079339  0.002289  0.141781  0.072971   \n",
       "\n",
       "        306       307       308       309       310       311       312  \\\n",
       "0  0.245666 -0.132666 -0.072168 -0.128495  0.371667  0.073819 -0.346774   \n",
       "1 -0.013616 -0.008109  0.100474  0.350076 -0.403367 -0.147593  0.449490   \n",
       "2 -0.168332  0.370748 -0.049302  0.764073 -0.082208 -0.134470  0.141556   \n",
       "3 -0.159482  0.343924 -0.299267  0.573308 -0.046271 -0.141913  0.147154   \n",
       "4  0.182311 -0.115285  0.244940  0.285008 -0.158990  0.221658 -0.247090   \n",
       "\n",
       "        313       314       315       316       317       318       319  \\\n",
       "0 -0.154980 -0.043927  0.266374 -0.377492  0.287021  0.323220 -0.139446   \n",
       "1  0.076959 -0.170959 -0.290641  0.444392 -0.237573 -0.314239  0.163471   \n",
       "2 -0.120196 -0.166558  0.052498  0.410828 -0.087558  0.162144  0.113441   \n",
       "3 -0.099693  0.182339  0.119959  0.203136 -0.051442  0.281536  0.216792   \n",
       "4 -0.082899  0.154496  0.279779  0.185300  0.063957  0.160231  0.241484   \n",
       "\n",
       "        320       321       322       323       324       325       326  \\\n",
       "0 -0.041754 -0.330841 -0.327843 -0.419849 -0.315440  0.243553  0.034042   \n",
       "1 -0.006598  0.414928  0.061441  0.371939  0.372028 -0.009726  0.033973   \n",
       "2  0.006000  0.331360 -0.185279 -0.538368  0.293583 -0.181265 -0.053735   \n",
       "3 -0.214773 -0.166411  0.237822 -0.481369  0.515954  0.083816  0.030179   \n",
       "4 -0.172270  0.092395 -0.148565 -0.006172  0.221339 -0.120513 -0.058244   \n",
       "\n",
       "        327       328       329       330       331       332       333  \\\n",
       "0 -0.048024 -0.358343  0.685317  0.149768  0.093728 -0.115640  0.049546   \n",
       "1 -0.004191  0.422105 -0.851663 -0.195349 -0.062573  0.089064 -0.166465   \n",
       "2  0.214390 -0.237613 -0.266590 -0.425849 -0.084471 -0.045366  0.250672   \n",
       "3 -0.116629 -0.006645 -0.180737 -0.316803  0.312707  0.134614  0.285130   \n",
       "4  0.156907  0.020528  0.130059  0.007458  0.321026 -0.071255 -0.109881   \n",
       "\n",
       "        334       335       336       337       338       339       340  \\\n",
       "0  0.196791  0.138526 -0.285874 -0.063073 -0.126309 -0.552038 -0.076345   \n",
       "1 -0.200279 -0.145031  0.284497 -0.015318  0.153042  0.576949  0.166307   \n",
       "2  0.203500  0.358119  0.124741 -0.110608  0.166173  0.245588  0.198816   \n",
       "3 -0.100552  0.281081 -0.102719 -0.196316  0.499533  0.397943  0.055209   \n",
       "4 -0.076796 -0.017891 -0.079960 -0.066078  0.033817 -0.041758  0.324819   \n",
       "\n",
       "        341       342       343       344       345       346       347  \\\n",
       "0  0.162986 -0.099211 -0.150293 -0.446018  0.048942 -0.193010 -0.122656   \n",
       "1 -0.212129  0.120308  0.095037  0.476205  0.035356  0.190164  0.086299   \n",
       "2  0.487905 -0.005629 -0.381211  0.161587 -0.270808  0.113834 -0.196901   \n",
       "3  0.149815  0.110090 -0.737400  0.205180 -0.361550 -0.101456  0.098347   \n",
       "4  0.220990 -0.021265  0.118044  0.068968 -0.214662  0.009142  0.048827   \n",
       "\n",
       "        348       349       350       351       352       353       354  \\\n",
       "0  0.106874 -0.060901  0.138733 -0.462648 -0.368683 -0.032201 -0.120023   \n",
       "1  0.031945 -0.016451 -0.195049  0.427069  0.347049  0.227120  0.060197   \n",
       "2  0.276646 -0.207903  0.183486  0.107410 -0.682283  0.028788  0.310639   \n",
       "3 -0.314998  0.277956 -0.053242 -0.515852 -0.070509 -0.103246  0.172510   \n",
       "4  0.011017  0.060845 -0.062490 -0.167027  0.057304  0.109163  0.193754   \n",
       "\n",
       "        355  label  \n",
       "0  0.006298      0  \n",
       "1  0.028783      3  \n",
       "2 -0.194379      0  \n",
       "3  0.036080      0  \n",
       "4 -0.014018      7  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = trainsetCpy.iloc[:, 0]\n",
    "label = trainsetCpy.iloc[:, 137]\n",
    "recovered.insert(0, column='filename', value=filenames)\n",
    "recovered.insert(357, column='label', value=label)\n",
    "recovered.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pour les données de test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_train_X, trainset_validation_X = unique_independant_subject(recovered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    3\n",
       "2    0\n",
       "3    0\n",
       "4    7\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset_train_y = trainset_train_X.iloc[:, 357]\n",
    "\n",
    "trainset_train_y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_validation_y = trainset_validation_X.iloc[:, 357]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trainset et validationset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(trainset_train_X.shape)\n",
    "trainset_train_X.drop(columns=['filename'], inplace=True)\n",
    "trainset_train_X.drop(columns=['label'], inplace=True)\n",
    "trainset_train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(trainset_validation_X.shape)\n",
    "trainset_validation_X.drop(columns=['filename'], inplace=True)\n",
    "trainset_validation_X.drop(columns=['label'], inplace=True)\n",
    "trainset_validation_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=7, metric='euclidean')\n",
    "knn.fit(trainset_train_X, trainset_train_y)\n",
    "y_pred = knn.predict(trainset_validation_X)\n",
    "y_predP = knn.predict(recovered1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type(y_pred))\n",
    "print(trainset_validation_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(trainset_validation_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate model with 1000 decision trees\n",
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\n",
    "\n",
    "# Train the model on training data\n",
    "rf.fit(trainset_train_X, trainset_train_y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(trainset_validation_X)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "# Calculate the absolute errors\n",
    "errors = abs(predictions - trainset_validation_y)\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / trainset_validation_y)\n",
    "# Calculate and display accuracy\n",
    "print(np.mean(mape))\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print(accuracy)\n",
    "print('Accuracy:', accuracy, '%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EVAL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(y_predP).to_csv(\"label.csv\",header=False,index=False)"
   ]
  }
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