Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=2)\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 685.7x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import signal\n",
    "from scipy.io import wavfile\n",
    "import numpy as np\n",
    "from librosa.core import resample\n",
    "\n",
    "sample_rate, samples = wavfile.read('audio_normal.wav')\n",
    "#downsampling to 10000Hz\n",
    "sr = 9000\n",
    "resampled = resample(samples.astype(np.float32),sample_rate,sr) \n",
    "#make sure every signal is within the range of (0,1)\n",
    "samples_casted = resampled / (2.**15) \n",
    "seg_len = sr/40 #25ms\n",
    "overlap = seg_len*0.6  #60% overlap\n",
    "f, t, Zxx = signal.stft(samples_casted, fs = sr, nperseg = seg_len, noverlap = overlap)\n",
    "Zxx_abs = np.abs(Zxx)\n",
    "audio_normal = Zxx.real\n",
    "\n",
    "cols = ['Normal?']\n",
    "for i in range(len(f)):\n",
    "    cols.append((str(f[i])+'Hz'))\n",
    "    \n",
    "A_data = np.array([[0.0 for i in range(len(f)+1)] for i in range(len(t))])\n",
    "for i in range(1,len(t)):\n",
    "    row = np.array([1.0])\n",
    "    row = np.append(row, audio_normal[:,i])\n",
    "    A_data[i] = row\n",
    "df = pd.DataFrame(np.array([A_data[i] for i in range(len(t))]), columns = cols)\n",
    "\n",
    "\n",
    "sample_rate, samples = wavfile.read('audio_abnormal.wav')\n",
    "#downsampling to 9000Hz\n",
    "sr = 9000\n",
    "resampled = resample(samples.astype(np.float32),sample_rate,sr) \n",
    "\n",
    "#make sure every signal is within the range of (0,1)\n",
    "samples_casted = resampled / (2.**15) \n",
    "seg_len = sr/40 #25ms\n",
    "overlap = seg_len*0.6  #60% overlap\n",
    "f, t, Zxx = signal.stft(samples_casted, fs = sr, nperseg = seg_len, noverlap = overlap)\n",
    "Zxx_abs = np.abs(Zxx)\n",
    "\n",
    "audio_abnormal = Zxx.real\n",
    "cols = ['Normal?']\n",
    "for i in range(len(f)):\n",
    "    cols.append((str(f[i])+'Hz'))\n",
    "    \n",
    "A_data = np.array([[0.0 for i in range(len(f)+1)] for i in range(len(t))])\n",
    "for i in range(1,len(t)):\n",
    "    row = np.array([0.0])\n",
    "    row = np.append(row, audio_abnormal[:,i])\n",
    "    A_data[i] = row\n",
    "df_audio = pd.DataFrame(np.array([A_data[i] for i in range(len(t))]), columns = cols)\n",
    "#DataFrame appending --> Mixed audio\n",
    "df_audio = df_audio.append(df)\n",
    "\n",
    "#####PCA#####\n",
    "Audio_s = df_audio.iloc[:,1:]\n",
    "#X: shifted dataset (each feature has mean value of zero)\n",
    "X = Audio_s - Audio_s.mean()\n",
    "#Z: shifted and standardized dataset \n",
    "Z = X / Audio_s.std()\n",
    "U, S, Vt = np.linalg.svd(Z, full_matrices=False)\n",
    "variances = S**2\n",
    "V = Vt.T\n",
    "\n",
    "tot = sum(variances)\n",
    "var_exp = [(i / tot)*100 for i in variances]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "\n",
    "###Take n_components = 50\n",
    "Zstar = Z@V[:,:50]\n",
    "df_audio['p0'] = Zstar.values[:,0]\n",
    "df_audio['p1'] = Zstar.values[:,1]\n",
    "\n",
    "#PCA plotting for PC0 and PC1\n",
    "g1 = sns.lmplot('p0', 'p1', df_audio, hue='Normal?', fit_reg=False, height=8, scatter_kws={'alpha':0.7,'s':60})\n",
    "ax = g1.axes[0,0]\n",
    "ax.axhline(y=0, color='k')\n",
    "ax.axvline(x=0, color='k')\n",
    "headers = df_audio.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_pca = Zstar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#abnormal bee sounds\n",
    "audio_pca[\"Normal?\"] = 0.0\n",
    "#normal bee sounds starting from index 12002\n",
    "audio_pca[\"Normal?\"][12002:] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>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>Normal?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.019155</td>\n",
       "      <td>-0.002557</td>\n",
       "      <td>-0.002534</td>\n",
       "      <td>-0.005877</td>\n",
       "      <td>0.004355</td>\n",
       "      <td>-0.015456</td>\n",
       "      <td>0.002539</td>\n",
       "      <td>-0.018969</td>\n",
       "      <td>-0.000474</td>\n",
       "      <td>0.012799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007054</td>\n",
       "      <td>-0.006031</td>\n",
       "      <td>0.008233</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.001704</td>\n",
       "      <td>0.003034</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.005144</td>\n",
       "      <td>-0.009466</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.880227</td>\n",
       "      <td>0.043249</td>\n",
       "      <td>0.810463</td>\n",
       "      <td>-0.525779</td>\n",
       "      <td>-0.070669</td>\n",
       "      <td>-1.536267</td>\n",
       "      <td>1.231977</td>\n",
       "      <td>-0.527014</td>\n",
       "      <td>2.041000</td>\n",
       "      <td>-0.925828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.491917</td>\n",
       "      <td>1.500413</td>\n",
       "      <td>0.988275</td>\n",
       "      <td>0.187765</td>\n",
       "      <td>-0.804564</td>\n",
       "      <td>0.887555</td>\n",
       "      <td>-0.013030</td>\n",
       "      <td>-0.080059</td>\n",
       "      <td>-0.393268</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.814768</td>\n",
       "      <td>2.133738</td>\n",
       "      <td>-0.342221</td>\n",
       "      <td>-0.509715</td>\n",
       "      <td>0.330817</td>\n",
       "      <td>-1.616263</td>\n",
       "      <td>3.084825</td>\n",
       "      <td>0.962301</td>\n",
       "      <td>0.519433</td>\n",
       "      <td>-1.424071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.906236</td>\n",
       "      <td>-0.671340</td>\n",
       "      <td>0.463547</td>\n",
       "      <td>-1.069696</td>\n",
       "      <td>0.466183</td>\n",
       "      <td>0.547371</td>\n",
       "      <td>-0.626384</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>-1.585993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.233915</td>\n",
       "      <td>-0.675901</td>\n",
       "      <td>-1.989435</td>\n",
       "      <td>-0.846981</td>\n",
       "      <td>2.306992</td>\n",
       "      <td>-1.112637</td>\n",
       "      <td>0.247026</td>\n",
       "      <td>-0.095990</td>\n",
       "      <td>-0.300163</td>\n",
       "      <td>-2.010627</td>\n",
       "      <td>...</td>\n",
       "      <td>1.847089</td>\n",
       "      <td>-0.072382</td>\n",
       "      <td>-0.047417</td>\n",
       "      <td>-0.359991</td>\n",
       "      <td>0.442304</td>\n",
       "      <td>-0.489900</td>\n",
       "      <td>0.562185</td>\n",
       "      <td>0.248784</td>\n",
       "      <td>-0.505636</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.878836</td>\n",
       "      <td>-2.494738</td>\n",
       "      <td>-0.051249</td>\n",
       "      <td>-2.474288</td>\n",
       "      <td>3.120653</td>\n",
       "      <td>0.013880</td>\n",
       "      <td>0.314216</td>\n",
       "      <td>1.555675</td>\n",
       "      <td>1.533230</td>\n",
       "      <td>-0.587836</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.171078</td>\n",
       "      <td>-1.585042</td>\n",
       "      <td>1.116186</td>\n",
       "      <td>-0.219846</td>\n",
       "      <td>-1.054201</td>\n",
       "      <td>-0.708191</td>\n",
       "      <td>-0.891057</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.861057</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.465490</td>\n",
       "      <td>3.503006</td>\n",
       "      <td>0.811173</td>\n",
       "      <td>-0.062527</td>\n",
       "      <td>2.731544</td>\n",
       "      <td>2.028730</td>\n",
       "      <td>-0.283411</td>\n",
       "      <td>-0.074541</td>\n",
       "      <td>2.117981</td>\n",
       "      <td>1.559328</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.344765</td>\n",
       "      <td>1.173636</td>\n",
       "      <td>0.382666</td>\n",
       "      <td>1.150170</td>\n",
       "      <td>1.289068</td>\n",
       "      <td>-0.635437</td>\n",
       "      <td>0.272962</td>\n",
       "      <td>-1.250388</td>\n",
       "      <td>-0.285791</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.724232</td>\n",
       "      <td>1.185434</td>\n",
       "      <td>-2.082349</td>\n",
       "      <td>-1.768147</td>\n",
       "      <td>-1.950397</td>\n",
       "      <td>-1.736932</td>\n",
       "      <td>3.031381</td>\n",
       "      <td>0.672213</td>\n",
       "      <td>0.125088</td>\n",
       "      <td>-0.935890</td>\n",
       "      <td>...</td>\n",
       "      <td>2.220851</td>\n",
       "      <td>-0.285988</td>\n",
       "      <td>0.072878</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>-0.867118</td>\n",
       "      <td>0.356124</td>\n",
       "      <td>0.146614</td>\n",
       "      <td>0.503536</td>\n",
       "      <td>-0.495361</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.008882</td>\n",
       "      <td>2.062266</td>\n",
       "      <td>-0.947977</td>\n",
       "      <td>-1.153657</td>\n",
       "      <td>0.297901</td>\n",
       "      <td>-2.117108</td>\n",
       "      <td>0.266630</td>\n",
       "      <td>-0.287486</td>\n",
       "      <td>-2.614904</td>\n",
       "      <td>-3.071081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639649</td>\n",
       "      <td>-0.049073</td>\n",
       "      <td>-1.248601</td>\n",
       "      <td>0.344704</td>\n",
       "      <td>-0.549407</td>\n",
       "      <td>1.553585</td>\n",
       "      <td>-0.081885</td>\n",
       "      <td>0.196795</td>\n",
       "      <td>-0.363517</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.612084</td>\n",
       "      <td>-0.455263</td>\n",
       "      <td>0.264269</td>\n",
       "      <td>-0.705556</td>\n",
       "      <td>1.724489</td>\n",
       "      <td>-0.234065</td>\n",
       "      <td>1.068617</td>\n",
       "      <td>1.261316</td>\n",
       "      <td>-3.511781</td>\n",
       "      <td>1.539717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506863</td>\n",
       "      <td>-0.779403</td>\n",
       "      <td>-1.027868</td>\n",
       "      <td>0.948576</td>\n",
       "      <td>1.100451</td>\n",
       "      <td>1.302081</td>\n",
       "      <td>-0.712222</td>\n",
       "      <td>0.254735</td>\n",
       "      <td>0.279973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.139501</td>\n",
       "      <td>-1.847122</td>\n",
       "      <td>0.484010</td>\n",
       "      <td>-1.922296</td>\n",
       "      <td>0.811482</td>\n",
       "      <td>1.849988</td>\n",
       "      <td>-3.331280</td>\n",
       "      <td>1.645649</td>\n",
       "      <td>-0.053200</td>\n",
       "      <td>0.142741</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.074448</td>\n",
       "      <td>-0.874612</td>\n",
       "      <td>1.818556</td>\n",
       "      <td>-1.569693</td>\n",
       "      <td>-1.498720</td>\n",
       "      <td>-0.299998</td>\n",
       "      <td>0.115941</td>\n",
       "      <td>-1.159921</td>\n",
       "      <td>1.125589</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.434687</td>\n",
       "      <td>-3.039034</td>\n",
       "      <td>0.385289</td>\n",
       "      <td>5.994414</td>\n",
       "      <td>1.745476</td>\n",
       "      <td>-1.505454</td>\n",
       "      <td>-1.867837</td>\n",
       "      <td>-0.975741</td>\n",
       "      <td>0.074112</td>\n",
       "      <td>0.185986</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.729129</td>\n",
       "      <td>0.892806</td>\n",
       "      <td>-1.670198</td>\n",
       "      <td>-0.432876</td>\n",
       "      <td>-0.535024</td>\n",
       "      <td>0.635339</td>\n",
       "      <td>1.047735</td>\n",
       "      <td>0.520128</td>\n",
       "      <td>0.333359</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.243106</td>\n",
       "      <td>0.900953</td>\n",
       "      <td>2.859788</td>\n",
       "      <td>2.430354</td>\n",
       "      <td>3.376317</td>\n",
       "      <td>0.138108</td>\n",
       "      <td>0.440921</td>\n",
       "      <td>0.664636</td>\n",
       "      <td>1.524929</td>\n",
       "      <td>0.872408</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968030</td>\n",
       "      <td>1.295836</td>\n",
       "      <td>0.164649</td>\n",
       "      <td>0.746021</td>\n",
       "      <td>1.204942</td>\n",
       "      <td>0.526412</td>\n",
       "      <td>-0.071663</td>\n",
       "      <td>0.626179</td>\n",
       "      <td>-0.619189</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.668533</td>\n",
       "      <td>-0.203530</td>\n",
       "      <td>2.374682</td>\n",
       "      <td>-0.879305</td>\n",
       "      <td>-0.291416</td>\n",
       "      <td>-0.194431</td>\n",
       "      <td>-1.256863</td>\n",
       "      <td>-1.005620</td>\n",
       "      <td>0.204383</td>\n",
       "      <td>-0.113069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238028</td>\n",
       "      <td>-1.379133</td>\n",
       "      <td>1.481303</td>\n",
       "      <td>0.777061</td>\n",
       "      <td>-0.034354</td>\n",
       "      <td>0.709078</td>\n",
       "      <td>-1.572460</td>\n",
       "      <td>0.133502</td>\n",
       "      <td>0.036547</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.129439</td>\n",
       "      <td>-0.748461</td>\n",
       "      <td>0.754472</td>\n",
       "      <td>1.577966</td>\n",
       "      <td>-0.406028</td>\n",
       "      <td>-1.071313</td>\n",
       "      <td>-2.207812</td>\n",
       "      <td>-0.537315</td>\n",
       "      <td>0.343557</td>\n",
       "      <td>-0.157957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974485</td>\n",
       "      <td>1.426844</td>\n",
       "      <td>1.567520</td>\n",
       "      <td>1.171641</td>\n",
       "      <td>0.245582</td>\n",
       "      <td>-0.070390</td>\n",
       "      <td>1.617714</td>\n",
       "      <td>1.120578</td>\n",
       "      <td>0.151736</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.198524</td>\n",
       "      <td>-1.814017</td>\n",
       "      <td>0.259623</td>\n",
       "      <td>2.529539</td>\n",
       "      <td>-1.565903</td>\n",
       "      <td>0.510546</td>\n",
       "      <td>-3.868862</td>\n",
       "      <td>0.903491</td>\n",
       "      <td>-2.332952</td>\n",
       "      <td>0.003312</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.343978</td>\n",
       "      <td>-0.949392</td>\n",
       "      <td>-0.136243</td>\n",
       "      <td>-0.239554</td>\n",
       "      <td>-0.939848</td>\n",
       "      <td>0.531523</td>\n",
       "      <td>-0.759164</td>\n",
       "      <td>0.668398</td>\n",
       "      <td>-1.185236</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.785114</td>\n",
       "      <td>0.608356</td>\n",
       "      <td>0.445541</td>\n",
       "      <td>0.693918</td>\n",
       "      <td>0.602376</td>\n",
       "      <td>-0.980224</td>\n",
       "      <td>-1.689105</td>\n",
       "      <td>-2.801558</td>\n",
       "      <td>-1.523043</td>\n",
       "      <td>-3.646624</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.207269</td>\n",
       "      <td>1.768233</td>\n",
       "      <td>1.833406</td>\n",
       "      <td>-1.008238</td>\n",
       "      <td>-0.327356</td>\n",
       "      <td>-0.658073</td>\n",
       "      <td>0.268712</td>\n",
       "      <td>0.445796</td>\n",
       "      <td>-0.775115</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.340655</td>\n",
       "      <td>2.218813</td>\n",
       "      <td>1.673745</td>\n",
       "      <td>-0.982037</td>\n",
       "      <td>0.489490</td>\n",
       "      <td>-2.550619</td>\n",
       "      <td>-0.794465</td>\n",
       "      <td>-0.636273</td>\n",
       "      <td>1.033843</td>\n",
       "      <td>3.059594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876730</td>\n",
       "      <td>-0.794915</td>\n",
       "      <td>-1.266747</td>\n",
       "      <td>0.729701</td>\n",
       "      <td>1.149351</td>\n",
       "      <td>-0.088523</td>\n",
       "      <td>-1.486449</td>\n",
       "      <td>1.019229</td>\n",
       "      <td>-0.797592</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.566952</td>\n",
       "      <td>-0.261995</td>\n",
       "      <td>-1.706287</td>\n",
       "      <td>1.338536</td>\n",
       "      <td>0.319589</td>\n",
       "      <td>-0.961553</td>\n",
       "      <td>-0.062999</td>\n",
       "      <td>-3.315907</td>\n",
       "      <td>-0.887560</td>\n",
       "      <td>0.470329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534998</td>\n",
       "      <td>0.417675</td>\n",
       "      <td>-0.086117</td>\n",
       "      <td>-1.754030</td>\n",
       "      <td>-0.527726</td>\n",
       "      <td>-0.495249</td>\n",
       "      <td>-0.701816</td>\n",
       "      <td>-1.226338</td>\n",
       "      <td>0.726363</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.988500</td>\n",
       "      <td>0.759884</td>\n",
       "      <td>-0.376780</td>\n",
       "      <td>-0.311833</td>\n",
       "      <td>-0.088986</td>\n",
       "      <td>0.337724</td>\n",
       "      <td>3.130179</td>\n",
       "      <td>2.906400</td>\n",
       "      <td>0.570410</td>\n",
       "      <td>-1.985361</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.407325</td>\n",
       "      <td>0.532786</td>\n",
       "      <td>-0.373575</td>\n",
       "      <td>0.701886</td>\n",
       "      <td>0.785436</td>\n",
       "      <td>-0.645898</td>\n",
       "      <td>-1.283363</td>\n",
       "      <td>1.056896</td>\n",
       "      <td>-0.570667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1.420992</td>\n",
       "      <td>0.128411</td>\n",
       "      <td>0.241671</td>\n",
       "      <td>-3.200366</td>\n",
       "      <td>0.139792</td>\n",
       "      <td>-0.313606</td>\n",
       "      <td>2.202582</td>\n",
       "      <td>-1.811008</td>\n",
       "      <td>0.773211</td>\n",
       "      <td>1.080640</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.660455</td>\n",
       "      <td>0.394514</td>\n",
       "      <td>-1.365582</td>\n",
       "      <td>1.663064</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.487937</td>\n",
       "      <td>-0.542692</td>\n",
       "      <td>0.670331</td>\n",
       "      <td>1.503329</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1.332134</td>\n",
       "      <td>-0.619825</td>\n",
       "      <td>2.435713</td>\n",
       "      <td>0.413139</td>\n",
       "      <td>0.713073</td>\n",
       "      <td>0.639056</td>\n",
       "      <td>1.452527</td>\n",
       "      <td>-2.052653</td>\n",
       "      <td>1.534486</td>\n",
       "      <td>1.825912</td>\n",
       "      <td>...</td>\n",
       "      <td>1.087778</td>\n",
       "      <td>1.828017</td>\n",
       "      <td>1.321120</td>\n",
       "      <td>-1.847678</td>\n",
       "      <td>-1.008058</td>\n",
       "      <td>0.996512</td>\n",
       "      <td>0.421598</td>\n",
       "      <td>-0.219942</td>\n",
       "      <td>0.654331</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.123624</td>\n",
       "      <td>1.038725</td>\n",
       "      <td>2.044733</td>\n",
       "      <td>-0.768464</td>\n",
       "      <td>0.443983</td>\n",
       "      <td>2.457204</td>\n",
       "      <td>2.161508</td>\n",
       "      <td>-1.520998</td>\n",
       "      <td>1.978605</td>\n",
       "      <td>-1.833641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.325806</td>\n",
       "      <td>0.421032</td>\n",
       "      <td>1.359355</td>\n",
       "      <td>-0.315683</td>\n",
       "      <td>-0.590700</td>\n",
       "      <td>0.398781</td>\n",
       "      <td>0.094830</td>\n",
       "      <td>-0.737189</td>\n",
       "      <td>-1.732351</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.525457</td>\n",
       "      <td>-0.573934</td>\n",
       "      <td>-1.225599</td>\n",
       "      <td>0.665359</td>\n",
       "      <td>-1.086314</td>\n",
       "      <td>3.004673</td>\n",
       "      <td>0.554974</td>\n",
       "      <td>1.275830</td>\n",
       "      <td>0.922578</td>\n",
       "      <td>-2.009521</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.400624</td>\n",
       "      <td>-0.723114</td>\n",
       "      <td>-0.155336</td>\n",
       "      <td>1.708140</td>\n",
       "      <td>0.457126</td>\n",
       "      <td>-0.475600</td>\n",
       "      <td>-0.906052</td>\n",
       "      <td>-0.830917</td>\n",
       "      <td>0.433808</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.283854</td>\n",
       "      <td>-1.283728</td>\n",
       "      <td>1.750507</td>\n",
       "      <td>-1.049647</td>\n",
       "      <td>2.430185</td>\n",
       "      <td>2.346939</td>\n",
       "      <td>0.165272</td>\n",
       "      <td>2.608832</td>\n",
       "      <td>-2.800216</td>\n",
       "      <td>3.249932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891580</td>\n",
       "      <td>0.570624</td>\n",
       "      <td>-1.419087</td>\n",
       "      <td>0.373260</td>\n",
       "      <td>0.872111</td>\n",
       "      <td>0.998091</td>\n",
       "      <td>-0.463426</td>\n",
       "      <td>0.740339</td>\n",
       "      <td>0.725388</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.169619</td>\n",
       "      <td>-2.648571</td>\n",
       "      <td>0.451959</td>\n",
       "      <td>0.510172</td>\n",
       "      <td>-1.470874</td>\n",
       "      <td>0.237697</td>\n",
       "      <td>0.847881</td>\n",
       "      <td>2.725744</td>\n",
       "      <td>-0.298856</td>\n",
       "      <td>1.960303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489381</td>\n",
       "      <td>-0.497444</td>\n",
       "      <td>0.371637</td>\n",
       "      <td>0.914163</td>\n",
       "      <td>0.256280</td>\n",
       "      <td>-1.115768</td>\n",
       "      <td>0.388817</td>\n",
       "      <td>0.024428</td>\n",
       "      <td>-0.406505</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.737184</td>\n",
       "      <td>-1.547332</td>\n",
       "      <td>-2.176716</td>\n",
       "      <td>-0.683920</td>\n",
       "      <td>-1.106047</td>\n",
       "      <td>2.265428</td>\n",
       "      <td>0.463112</td>\n",
       "      <td>-0.510806</td>\n",
       "      <td>-2.180518</td>\n",
       "      <td>1.276391</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.618635</td>\n",
       "      <td>-0.468226</td>\n",
       "      <td>-0.730568</td>\n",
       "      <td>-0.206562</td>\n",
       "      <td>-0.149883</td>\n",
       "      <td>0.131117</td>\n",
       "      <td>0.124050</td>\n",
       "      <td>-0.152299</td>\n",
       "      <td>2.213474</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.184043</td>\n",
       "      <td>-1.595353</td>\n",
       "      <td>-0.425123</td>\n",
       "      <td>0.887120</td>\n",
       "      <td>-0.739930</td>\n",
       "      <td>0.593320</td>\n",
       "      <td>0.409477</td>\n",
       "      <td>-1.376227</td>\n",
       "      <td>1.495291</td>\n",
       "      <td>0.222539</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.654312</td>\n",
       "      <td>-0.122274</td>\n",
       "      <td>1.468356</td>\n",
       "      <td>-1.554461</td>\n",
       "      <td>-0.582255</td>\n",
       "      <td>1.883963</td>\n",
       "      <td>0.763002</td>\n",
       "      <td>0.010577</td>\n",
       "      <td>0.835488</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.773512</td>\n",
       "      <td>0.511251</td>\n",
       "      <td>-0.538669</td>\n",
       "      <td>-0.279207</td>\n",
       "      <td>1.709984</td>\n",
       "      <td>-2.620379</td>\n",
       "      <td>-0.681047</td>\n",
       "      <td>-0.324749</td>\n",
       "      <td>-0.623412</td>\n",
       "      <td>0.784867</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.161515</td>\n",
       "      <td>-2.106008</td>\n",
       "      <td>0.279960</td>\n",
       "      <td>1.314532</td>\n",
       "      <td>0.233539</td>\n",
       "      <td>1.637556</td>\n",
       "      <td>-0.374170</td>\n",
       "      <td>-0.792148</td>\n",
       "      <td>0.571869</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.775510</td>\n",
       "      <td>-1.462693</td>\n",
       "      <td>-0.366784</td>\n",
       "      <td>0.910910</td>\n",
       "      <td>-2.292830</td>\n",
       "      <td>-0.213356</td>\n",
       "      <td>0.783569</td>\n",
       "      <td>-0.345809</td>\n",
       "      <td>0.230761</td>\n",
       "      <td>-1.483022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.779214</td>\n",
       "      <td>0.481392</td>\n",
       "      <td>0.517028</td>\n",
       "      <td>-0.744459</td>\n",
       "      <td>-1.208480</td>\n",
       "      <td>-1.451864</td>\n",
       "      <td>0.920813</td>\n",
       "      <td>0.497764</td>\n",
       "      <td>-0.546534</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.199381</td>\n",
       "      <td>2.425769</td>\n",
       "      <td>0.696304</td>\n",
       "      <td>-1.785025</td>\n",
       "      <td>-0.087694</td>\n",
       "      <td>2.556051</td>\n",
       "      <td>0.783342</td>\n",
       "      <td>-0.743730</td>\n",
       "      <td>-2.565174</td>\n",
       "      <td>-1.251055</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.216270</td>\n",
       "      <td>0.808175</td>\n",
       "      <td>-2.582358</td>\n",
       "      <td>1.124949</td>\n",
       "      <td>0.520550</td>\n",
       "      <td>-0.747573</td>\n",
       "      <td>-0.719888</td>\n",
       "      <td>1.406912</td>\n",
       "      <td>-0.773137</td>\n",
       "      <td>0.0</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>11971</th>\n",
       "      <td>0.515300</td>\n",
       "      <td>-1.771668</td>\n",
       "      <td>-0.874923</td>\n",
       "      <td>2.678350</td>\n",
       "      <td>0.161571</td>\n",
       "      <td>-0.470737</td>\n",
       "      <td>0.406064</td>\n",
       "      <td>-0.129174</td>\n",
       "      <td>-1.566306</td>\n",
       "      <td>0.976028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.673497</td>\n",
       "      <td>0.289047</td>\n",
       "      <td>-0.316365</td>\n",
       "      <td>0.264567</td>\n",
       "      <td>2.235367</td>\n",
       "      <td>-1.124104</td>\n",
       "      <td>0.335837</td>\n",
       "      <td>0.917009</td>\n",
       "      <td>0.336346</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11972</th>\n",
       "      <td>0.919135</td>\n",
       "      <td>-1.130314</td>\n",
       "      <td>0.513962</td>\n",
       "      <td>2.045402</td>\n",
       "      <td>1.705760</td>\n",
       "      <td>1.856706</td>\n",
       "      <td>2.475524</td>\n",
       "      <td>-0.585109</td>\n",
       "      <td>1.139820</td>\n",
       "      <td>0.699457</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280176</td>\n",
       "      <td>1.877038</td>\n",
       "      <td>-2.006620</td>\n",
       "      <td>-1.933253</td>\n",
       "      <td>-0.301478</td>\n",
       "      <td>-1.367966</td>\n",
       "      <td>-1.191636</td>\n",
       "      <td>-0.206729</td>\n",
       "      <td>0.106547</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11973</th>\n",
       "      <td>-0.971012</td>\n",
       "      <td>-0.789201</td>\n",
       "      <td>-2.168889</td>\n",
       "      <td>-2.122721</td>\n",
       "      <td>0.314446</td>\n",
       "      <td>-1.571622</td>\n",
       "      <td>0.841637</td>\n",
       "      <td>1.192934</td>\n",
       "      <td>3.618536</td>\n",
       "      <td>-1.174175</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.402376</td>\n",
       "      <td>1.145481</td>\n",
       "      <td>-0.020745</td>\n",
       "      <td>0.154186</td>\n",
       "      <td>-0.578581</td>\n",
       "      <td>-1.845163</td>\n",
       "      <td>0.248591</td>\n",
       "      <td>-0.954870</td>\n",
       "      <td>-1.364246</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11974</th>\n",
       "      <td>-0.685894</td>\n",
       "      <td>1.143085</td>\n",
       "      <td>1.242950</td>\n",
       "      <td>-0.338709</td>\n",
       "      <td>-0.874081</td>\n",
       "      <td>-0.020144</td>\n",
       "      <td>-2.165118</td>\n",
       "      <td>0.161211</td>\n",
       "      <td>-0.553350</td>\n",
       "      <td>4.981703</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.198253</td>\n",
       "      <td>0.438768</td>\n",
       "      <td>-1.148970</td>\n",
       "      <td>1.408641</td>\n",
       "      <td>-0.385985</td>\n",
       "      <td>-0.009901</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>-1.462641</td>\n",
       "      <td>-0.474488</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11975</th>\n",
       "      <td>-1.714561</td>\n",
       "      <td>3.477926</td>\n",
       "      <td>-0.515085</td>\n",
       "      <td>0.943537</td>\n",
       "      <td>1.219907</td>\n",
       "      <td>-0.777615</td>\n",
       "      <td>-1.836058</td>\n",
       "      <td>0.905370</td>\n",
       "      <td>0.590356</td>\n",
       "      <td>-1.373211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939826</td>\n",
       "      <td>-1.078713</td>\n",
       "      <td>0.156047</td>\n",
       "      <td>0.004073</td>\n",
       "      <td>-1.063424</td>\n",
       "      <td>-1.623410</td>\n",
       "      <td>0.437496</td>\n",
       "      <td>-2.129633</td>\n",
       "      <td>0.018362</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11976</th>\n",
       "      <td>-0.611278</td>\n",
       "      <td>2.571543</td>\n",
       "      <td>-0.715477</td>\n",
       "      <td>1.473162</td>\n",
       "      <td>1.041687</td>\n",
       "      <td>1.626700</td>\n",
       "      <td>-0.090412</td>\n",
       "      <td>3.661459</td>\n",
       "      <td>0.151983</td>\n",
       "      <td>-1.263478</td>\n",
       "      <td>...</td>\n",
       "      <td>1.440532</td>\n",
       "      <td>-1.547701</td>\n",
       "      <td>-0.847287</td>\n",
       "      <td>-0.477687</td>\n",
       "      <td>-0.408103</td>\n",
       "      <td>-0.637916</td>\n",
       "      <td>-0.177007</td>\n",
       "      <td>-0.693048</td>\n",
       "      <td>0.752888</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11977</th>\n",
       "      <td>-0.951821</td>\n",
       "      <td>-1.977360</td>\n",
       "      <td>1.041777</td>\n",
       "      <td>-0.499566</td>\n",
       "      <td>1.211470</td>\n",
       "      <td>1.196149</td>\n",
       "      <td>-0.494159</td>\n",
       "      <td>0.299594</td>\n",
       "      <td>1.610081</td>\n",
       "      <td>-0.667691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890896</td>\n",
       "      <td>1.833872</td>\n",
       "      <td>-2.388668</td>\n",
       "      <td>0.253342</td>\n",
       "      <td>0.373756</td>\n",
       "      <td>0.014098</td>\n",
       "      <td>-0.104731</td>\n",
       "      <td>-1.126859</td>\n",
       "      <td>-0.730858</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11978</th>\n",
       "      <td>-1.184578</td>\n",
       "      <td>-2.568759</td>\n",
       "      <td>0.428397</td>\n",
       "      <td>-2.726551</td>\n",
       "      <td>0.050671</td>\n",
       "      <td>-1.557598</td>\n",
       "      <td>5.013297</td>\n",
       "      <td>-1.949727</td>\n",
       "      <td>2.883147</td>\n",
       "      <td>-0.259917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843601</td>\n",
       "      <td>1.357118</td>\n",
       "      <td>1.288548</td>\n",
       "      <td>1.310552</td>\n",
       "      <td>0.544532</td>\n",
       "      <td>-1.765730</td>\n",
       "      <td>-1.644703</td>\n",
       "      <td>1.665526</td>\n",
       "      <td>-0.011404</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11979</th>\n",
       "      <td>0.015046</td>\n",
       "      <td>-1.975628</td>\n",
       "      <td>3.922272</td>\n",
       "      <td>0.912108</td>\n",
       "      <td>0.879783</td>\n",
       "      <td>-1.885222</td>\n",
       "      <td>0.324253</td>\n",
       "      <td>2.105937</td>\n",
       "      <td>1.808690</td>\n",
       "      <td>0.708242</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.038848</td>\n",
       "      <td>-0.598950</td>\n",
       "      <td>2.258736</td>\n",
       "      <td>0.598385</td>\n",
       "      <td>-1.817312</td>\n",
       "      <td>0.741694</td>\n",
       "      <td>-2.362624</td>\n",
       "      <td>-0.231939</td>\n",
       "      <td>-0.145460</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11980</th>\n",
       "      <td>-1.316850</td>\n",
       "      <td>0.243110</td>\n",
       "      <td>-0.509079</td>\n",
       "      <td>1.318225</td>\n",
       "      <td>2.187964</td>\n",
       "      <td>0.232560</td>\n",
       "      <td>1.659723</td>\n",
       "      <td>-0.938251</td>\n",
       "      <td>-0.380137</td>\n",
       "      <td>1.573549</td>\n",
       "      <td>...</td>\n",
       "      <td>2.757254</td>\n",
       "      <td>0.362615</td>\n",
       "      <td>0.243536</td>\n",
       "      <td>1.051807</td>\n",
       "      <td>-1.328285</td>\n",
       "      <td>2.058283</td>\n",
       "      <td>0.551110</td>\n",
       "      <td>0.116031</td>\n",
       "      <td>-0.050912</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11981</th>\n",
       "      <td>1.782254</td>\n",
       "      <td>0.375871</td>\n",
       "      <td>2.528275</td>\n",
       "      <td>-0.821058</td>\n",
       "      <td>1.113120</td>\n",
       "      <td>-0.286526</td>\n",
       "      <td>-1.886957</td>\n",
       "      <td>-0.641512</td>\n",
       "      <td>0.656085</td>\n",
       "      <td>-1.475655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.638565</td>\n",
       "      <td>-1.561951</td>\n",
       "      <td>-0.632813</td>\n",
       "      <td>0.744074</td>\n",
       "      <td>0.387719</td>\n",
       "      <td>0.823973</td>\n",
       "      <td>-0.904534</td>\n",
       "      <td>0.022580</td>\n",
       "      <td>-1.284544</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11982</th>\n",
       "      <td>0.769441</td>\n",
       "      <td>-0.366257</td>\n",
       "      <td>3.814583</td>\n",
       "      <td>0.464026</td>\n",
       "      <td>-1.054832</td>\n",
       "      <td>0.367153</td>\n",
       "      <td>1.264617</td>\n",
       "      <td>0.696934</td>\n",
       "      <td>-1.257668</td>\n",
       "      <td>0.607366</td>\n",
       "      <td>...</td>\n",
       "      <td>1.880389</td>\n",
       "      <td>1.903960</td>\n",
       "      <td>2.293422</td>\n",
       "      <td>0.255141</td>\n",
       "      <td>0.143228</td>\n",
       "      <td>-0.829351</td>\n",
       "      <td>0.144316</td>\n",
       "      <td>-1.667281</td>\n",
       "      <td>-0.038695</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11983</th>\n",
       "      <td>-2.681913</td>\n",
       "      <td>1.259877</td>\n",
       "      <td>-2.944210</td>\n",
       "      <td>-2.329851</td>\n",
       "      <td>0.091478</td>\n",
       "      <td>0.155399</td>\n",
       "      <td>-0.327652</td>\n",
       "      <td>-0.238453</td>\n",
       "      <td>-1.174989</td>\n",
       "      <td>2.794780</td>\n",
       "      <td>...</td>\n",
       "      <td>1.693842</td>\n",
       "      <td>1.079681</td>\n",
       "      <td>-0.373334</td>\n",
       "      <td>-0.855395</td>\n",
       "      <td>-0.281392</td>\n",
       "      <td>-0.863031</td>\n",
       "      <td>-1.221451</td>\n",
       "      <td>0.446727</td>\n",
       "      <td>-0.810825</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11984</th>\n",
       "      <td>-1.554008</td>\n",
       "      <td>-0.085892</td>\n",
       "      <td>0.173961</td>\n",
       "      <td>0.442471</td>\n",
       "      <td>0.116692</td>\n",
       "      <td>1.140826</td>\n",
       "      <td>-1.187225</td>\n",
       "      <td>1.421712</td>\n",
       "      <td>-2.476839</td>\n",
       "      <td>0.113366</td>\n",
       "      <td>...</td>\n",
       "      <td>1.911012</td>\n",
       "      <td>0.696126</td>\n",
       "      <td>-0.260654</td>\n",
       "      <td>-0.787283</td>\n",
       "      <td>-0.862120</td>\n",
       "      <td>-0.482057</td>\n",
       "      <td>-0.256165</td>\n",
       "      <td>-0.728528</td>\n",
       "      <td>-0.736768</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11985</th>\n",
       "      <td>-0.506369</td>\n",
       "      <td>-0.781386</td>\n",
       "      <td>1.062079</td>\n",
       "      <td>-2.374161</td>\n",
       "      <td>1.090062</td>\n",
       "      <td>0.212695</td>\n",
       "      <td>0.073522</td>\n",
       "      <td>4.499905</td>\n",
       "      <td>-1.537522</td>\n",
       "      <td>1.006884</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861024</td>\n",
       "      <td>-0.923334</td>\n",
       "      <td>-2.309468</td>\n",
       "      <td>0.847486</td>\n",
       "      <td>0.249020</td>\n",
       "      <td>0.627576</td>\n",
       "      <td>-1.524187</td>\n",
       "      <td>-1.443826</td>\n",
       "      <td>0.450419</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11986</th>\n",
       "      <td>-1.393544</td>\n",
       "      <td>1.473588</td>\n",
       "      <td>-0.006081</td>\n",
       "      <td>0.159911</td>\n",
       "      <td>0.383777</td>\n",
       "      <td>-0.634338</td>\n",
       "      <td>0.926198</td>\n",
       "      <td>-2.104552</td>\n",
       "      <td>-1.784736</td>\n",
       "      <td>0.264489</td>\n",
       "      <td>...</td>\n",
       "      <td>2.435680</td>\n",
       "      <td>0.195342</td>\n",
       "      <td>0.708646</td>\n",
       "      <td>1.328031</td>\n",
       "      <td>0.360355</td>\n",
       "      <td>-0.374496</td>\n",
       "      <td>1.017975</td>\n",
       "      <td>0.360272</td>\n",
       "      <td>1.712496</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11987</th>\n",
       "      <td>-3.973178</td>\n",
       "      <td>-0.988518</td>\n",
       "      <td>0.829963</td>\n",
       "      <td>0.550453</td>\n",
       "      <td>-2.416701</td>\n",
       "      <td>-1.471810</td>\n",
       "      <td>-0.055511</td>\n",
       "      <td>0.486027</td>\n",
       "      <td>-5.384121</td>\n",
       "      <td>-0.422543</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.773844</td>\n",
       "      <td>-0.321205</td>\n",
       "      <td>-0.991462</td>\n",
       "      <td>-1.774826</td>\n",
       "      <td>-0.691040</td>\n",
       "      <td>-0.540465</td>\n",
       "      <td>-1.037108</td>\n",
       "      <td>-0.090556</td>\n",
       "      <td>-1.583376</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11988</th>\n",
       "      <td>-1.959899</td>\n",
       "      <td>-3.760835</td>\n",
       "      <td>-0.907681</td>\n",
       "      <td>-1.565596</td>\n",
       "      <td>1.675875</td>\n",
       "      <td>-1.639891</td>\n",
       "      <td>-4.595915</td>\n",
       "      <td>0.021654</td>\n",
       "      <td>0.166208</td>\n",
       "      <td>3.480695</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323036</td>\n",
       "      <td>2.227095</td>\n",
       "      <td>-0.846622</td>\n",
       "      <td>-0.197227</td>\n",
       "      <td>-0.183365</td>\n",
       "      <td>-1.318605</td>\n",
       "      <td>-0.045012</td>\n",
       "      <td>-0.019285</td>\n",
       "      <td>-0.085404</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11989</th>\n",
       "      <td>-1.095015</td>\n",
       "      <td>-1.266144</td>\n",
       "      <td>0.428989</td>\n",
       "      <td>3.028102</td>\n",
       "      <td>2.313566</td>\n",
       "      <td>-1.195695</td>\n",
       "      <td>-2.302849</td>\n",
       "      <td>-1.091309</td>\n",
       "      <td>-1.290842</td>\n",
       "      <td>-2.321850</td>\n",
       "      <td>...</td>\n",
       "      <td>1.382571</td>\n",
       "      <td>-0.279537</td>\n",
       "      <td>0.913290</td>\n",
       "      <td>0.668961</td>\n",
       "      <td>-1.028463</td>\n",
       "      <td>-2.072461</td>\n",
       "      <td>-1.036551</td>\n",
       "      <td>1.476911</td>\n",
       "      <td>0.218957</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11990</th>\n",
       "      <td>0.236500</td>\n",
       "      <td>-1.345926</td>\n",
       "      <td>2.442085</td>\n",
       "      <td>-1.230989</td>\n",
       "      <td>-0.510544</td>\n",
       "      <td>-1.793463</td>\n",
       "      <td>0.023324</td>\n",
       "      <td>-1.402546</td>\n",
       "      <td>-4.083741</td>\n",
       "      <td>-0.333655</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.318570</td>\n",
       "      <td>0.936120</td>\n",
       "      <td>-0.403180</td>\n",
       "      <td>-0.494488</td>\n",
       "      <td>0.374408</td>\n",
       "      <td>0.091484</td>\n",
       "      <td>-0.658851</td>\n",
       "      <td>-0.621025</td>\n",
       "      <td>0.986703</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11991</th>\n",
       "      <td>-2.499848</td>\n",
       "      <td>1.073839</td>\n",
       "      <td>-0.593883</td>\n",
       "      <td>0.262796</td>\n",
       "      <td>-2.273250</td>\n",
       "      <td>2.027045</td>\n",
       "      <td>1.878394</td>\n",
       "      <td>-1.157621</td>\n",
       "      <td>-0.367089</td>\n",
       "      <td>-0.539423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.772118</td>\n",
       "      <td>-0.852295</td>\n",
       "      <td>0.620302</td>\n",
       "      <td>-0.200528</td>\n",
       "      <td>0.927405</td>\n",
       "      <td>1.375492</td>\n",
       "      <td>-0.006011</td>\n",
       "      <td>1.187555</td>\n",
       "      <td>0.359324</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11992</th>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.405644</td>\n",
       "      <td>0.210443</td>\n",
       "      <td>2.402584</td>\n",
       "      <td>2.156206</td>\n",
       "      <td>1.011167</td>\n",
       "      <td>0.623059</td>\n",
       "      <td>3.463784</td>\n",
       "      <td>-0.900735</td>\n",
       "      <td>0.441167</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.002505</td>\n",
       "      <td>1.094751</td>\n",
       "      <td>1.334342</td>\n",
       "      <td>-1.960769</td>\n",
       "      <td>0.437693</td>\n",
       "      <td>0.879606</td>\n",
       "      <td>1.365089</td>\n",
       "      <td>-1.186302</td>\n",
       "      <td>-0.464061</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11993</th>\n",
       "      <td>0.955993</td>\n",
       "      <td>-0.261009</td>\n",
       "      <td>0.209048</td>\n",
       "      <td>1.163657</td>\n",
       "      <td>0.889022</td>\n",
       "      <td>-0.056930</td>\n",
       "      <td>-2.777939</td>\n",
       "      <td>0.183170</td>\n",
       "      <td>1.409227</td>\n",
       "      <td>-0.838794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031458</td>\n",
       "      <td>-1.053852</td>\n",
       "      <td>-0.080237</td>\n",
       "      <td>0.293498</td>\n",
       "      <td>0.283078</td>\n",
       "      <td>1.246638</td>\n",
       "      <td>-0.342219</td>\n",
       "      <td>0.558138</td>\n",
       "      <td>0.349336</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11994</th>\n",
       "      <td>-0.716095</td>\n",
       "      <td>0.756280</td>\n",
       "      <td>-0.741979</td>\n",
       "      <td>1.897242</td>\n",
       "      <td>-2.333882</td>\n",
       "      <td>2.007778</td>\n",
       "      <td>2.111074</td>\n",
       "      <td>-0.241472</td>\n",
       "      <td>2.266714</td>\n",
       "      <td>0.400536</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.065921</td>\n",
       "      <td>-0.409353</td>\n",
       "      <td>0.789271</td>\n",
       "      <td>0.617187</td>\n",
       "      <td>0.873003</td>\n",
       "      <td>0.525367</td>\n",
       "      <td>-0.944774</td>\n",
       "      <td>1.169370</td>\n",
       "      <td>0.150578</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>1.168007</td>\n",
       "      <td>0.241298</td>\n",
       "      <td>2.122488</td>\n",
       "      <td>-0.621670</td>\n",
       "      <td>0.282304</td>\n",
       "      <td>0.766493</td>\n",
       "      <td>-1.776512</td>\n",
       "      <td>-0.762112</td>\n",
       "      <td>-1.790033</td>\n",
       "      <td>0.057198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305715</td>\n",
       "      <td>2.011013</td>\n",
       "      <td>1.180712</td>\n",
       "      <td>0.708146</td>\n",
       "      <td>0.685965</td>\n",
       "      <td>0.995940</td>\n",
       "      <td>0.659285</td>\n",
       "      <td>-1.182510</td>\n",
       "      <td>-1.014396</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>-1.934941</td>\n",
       "      <td>2.799332</td>\n",
       "      <td>0.397737</td>\n",
       "      <td>1.472630</td>\n",
       "      <td>2.196302</td>\n",
       "      <td>-0.643940</td>\n",
       "      <td>-1.093721</td>\n",
       "      <td>1.155401</td>\n",
       "      <td>-2.511705</td>\n",
       "      <td>-0.274012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.749570</td>\n",
       "      <td>-1.492058</td>\n",
       "      <td>0.106827</td>\n",
       "      <td>-0.664854</td>\n",
       "      <td>-0.019064</td>\n",
       "      <td>1.081311</td>\n",
       "      <td>-0.382820</td>\n",
       "      <td>1.766676</td>\n",
       "      <td>-1.427814</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>-0.406794</td>\n",
       "      <td>0.396411</td>\n",
       "      <td>-2.241750</td>\n",
       "      <td>-2.222946</td>\n",
       "      <td>-2.353107</td>\n",
       "      <td>0.699552</td>\n",
       "      <td>4.074507</td>\n",
       "      <td>0.706102</td>\n",
       "      <td>0.029120</td>\n",
       "      <td>1.258924</td>\n",
       "      <td>...</td>\n",
       "      <td>1.477645</td>\n",
       "      <td>-1.040513</td>\n",
       "      <td>0.720096</td>\n",
       "      <td>-1.018004</td>\n",
       "      <td>0.968699</td>\n",
       "      <td>-1.467148</td>\n",
       "      <td>0.505201</td>\n",
       "      <td>1.037273</td>\n",
       "      <td>1.179134</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>2.385417</td>\n",
       "      <td>-1.977389</td>\n",
       "      <td>0.220792</td>\n",
       "      <td>-1.613214</td>\n",
       "      <td>1.272667</td>\n",
       "      <td>-2.468381</td>\n",
       "      <td>-0.281892</td>\n",
       "      <td>-2.008536</td>\n",
       "      <td>-4.170027</td>\n",
       "      <td>-0.176729</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.253062</td>\n",
       "      <td>-0.046685</td>\n",
       "      <td>-0.860414</td>\n",
       "      <td>-1.126852</td>\n",
       "      <td>0.869450</td>\n",
       "      <td>-0.811977</td>\n",
       "      <td>0.599380</td>\n",
       "      <td>-0.946950</td>\n",
       "      <td>-0.002886</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>-0.733237</td>\n",
       "      <td>1.075118</td>\n",
       "      <td>0.352473</td>\n",
       "      <td>-2.343764</td>\n",
       "      <td>-0.847463</td>\n",
       "      <td>1.028252</td>\n",
       "      <td>0.817279</td>\n",
       "      <td>0.347465</td>\n",
       "      <td>0.949326</td>\n",
       "      <td>-2.039905</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.396074</td>\n",
       "      <td>0.494003</td>\n",
       "      <td>0.566663</td>\n",
       "      <td>0.221667</td>\n",
       "      <td>-0.299306</td>\n",
       "      <td>-0.354392</td>\n",
       "      <td>3.040238</td>\n",
       "      <td>0.192354</td>\n",
       "      <td>-1.293034</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12000</th>\n",
       "      <td>-1.432069</td>\n",
       "      <td>1.103715</td>\n",
       "      <td>0.755288</td>\n",
       "      <td>-2.131283</td>\n",
       "      <td>-3.366171</td>\n",
       "      <td>-0.265366</td>\n",
       "      <td>0.185350</td>\n",
       "      <td>0.264093</td>\n",
       "      <td>0.750939</td>\n",
       "      <td>-0.623580</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095448</td>\n",
       "      <td>-0.001635</td>\n",
       "      <td>-0.546501</td>\n",
       "      <td>-1.109757</td>\n",
       "      <td>-1.731317</td>\n",
       "      <td>-0.150037</td>\n",
       "      <td>0.361946</td>\n",
       "      <td>1.394608</td>\n",
       "      <td>-0.361351</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24002 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "0     -0.019155 -0.002557 -0.002534 -0.005877  0.004355 -0.015456  0.002539   \n",
       "1      0.880227  0.043249  0.810463 -0.525779 -0.070669 -1.536267  1.231977   \n",
       "2     -2.814768  2.133738 -0.342221 -0.509715  0.330817 -1.616263  3.084825   \n",
       "3      0.233915 -0.675901 -1.989435 -0.846981  2.306992 -1.112637  0.247026   \n",
       "4     -0.878836 -2.494738 -0.051249 -2.474288  3.120653  0.013880  0.314216   \n",
       "5      0.465490  3.503006  0.811173 -0.062527  2.731544  2.028730 -0.283411   \n",
       "6     -0.724232  1.185434 -2.082349 -1.768147 -1.950397 -1.736932  3.031381   \n",
       "7      2.008882  2.062266 -0.947977 -1.153657  0.297901 -2.117108  0.266630   \n",
       "8     -2.612084 -0.455263  0.264269 -0.705556  1.724489 -0.234065  1.068617   \n",
       "9      1.139501 -1.847122  0.484010 -1.922296  0.811482  1.849988 -3.331280   \n",
       "10    -0.434687 -3.039034  0.385289  5.994414  1.745476 -1.505454 -1.867837   \n",
       "11    -0.243106  0.900953  2.859788  2.430354  3.376317  0.138108  0.440921   \n",
       "12     2.668533 -0.203530  2.374682 -0.879305 -0.291416 -0.194431 -1.256863   \n",
       "13     1.129439 -0.748461  0.754472  1.577966 -0.406028 -1.071313 -2.207812   \n",
       "14    -1.198524 -1.814017  0.259623  2.529539 -1.565903  0.510546 -3.868862   \n",
       "15     1.785114  0.608356  0.445541  0.693918  0.602376 -0.980224 -1.689105   \n",
       "16    -0.340655  2.218813  1.673745 -0.982037  0.489490 -2.550619 -0.794465   \n",
       "17    -1.566952 -0.261995 -1.706287  1.338536  0.319589 -0.961553 -0.062999   \n",
       "18    -0.988500  0.759884 -0.376780 -0.311833 -0.088986  0.337724  3.130179   \n",
       "19    -1.420992  0.128411  0.241671 -3.200366  0.139792 -0.313606  2.202582   \n",
       "20    -1.332134 -0.619825  2.435713  0.413139  0.713073  0.639056  1.452527   \n",
       "21     1.123624  1.038725  2.044733 -0.768464  0.443983  2.457204  2.161508   \n",
       "22    -0.525457 -0.573934 -1.225599  0.665359 -1.086314  3.004673  0.554974   \n",
       "23    -0.283854 -1.283728  1.750507 -1.049647  2.430185  2.346939  0.165272   \n",
       "24     0.169619 -2.648571  0.451959  0.510172 -1.470874  0.237697  0.847881   \n",
       "25     0.737184 -1.547332 -2.176716 -0.683920 -1.106047  2.265428  0.463112   \n",
       "26    -1.184043 -1.595353 -0.425123  0.887120 -0.739930  0.593320  0.409477   \n",
       "27     0.773512  0.511251 -0.538669 -0.279207  1.709984 -2.620379 -0.681047   \n",
       "28    -1.775510 -1.462693 -0.366784  0.910910 -2.292830 -0.213356  0.783569   \n",
       "29    -0.199381  2.425769  0.696304 -1.785025 -0.087694  2.556051  0.783342   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "11971  0.515300 -1.771668 -0.874923  2.678350  0.161571 -0.470737  0.406064   \n",
       "11972  0.919135 -1.130314  0.513962  2.045402  1.705760  1.856706  2.475524   \n",
       "11973 -0.971012 -0.789201 -2.168889 -2.122721  0.314446 -1.571622  0.841637   \n",
       "11974 -0.685894  1.143085  1.242950 -0.338709 -0.874081 -0.020144 -2.165118   \n",
       "11975 -1.714561  3.477926 -0.515085  0.943537  1.219907 -0.777615 -1.836058   \n",
       "11976 -0.611278  2.571543 -0.715477  1.473162  1.041687  1.626700 -0.090412   \n",
       "11977 -0.951821 -1.977360  1.041777 -0.499566  1.211470  1.196149 -0.494159   \n",
       "11978 -1.184578 -2.568759  0.428397 -2.726551  0.050671 -1.557598  5.013297   \n",
       "11979  0.015046 -1.975628  3.922272  0.912108  0.879783 -1.885222  0.324253   \n",
       "11980 -1.316850  0.243110 -0.509079  1.318225  2.187964  0.232560  1.659723   \n",
       "11981  1.782254  0.375871  2.528275 -0.821058  1.113120 -0.286526 -1.886957   \n",
       "11982  0.769441 -0.366257  3.814583  0.464026 -1.054832  0.367153  1.264617   \n",
       "11983 -2.681913  1.259877 -2.944210 -2.329851  0.091478  0.155399 -0.327652   \n",
       "11984 -1.554008 -0.085892  0.173961  0.442471  0.116692  1.140826 -1.187225   \n",
       "11985 -0.506369 -0.781386  1.062079 -2.374161  1.090062  0.212695  0.073522   \n",
       "11986 -1.393544  1.473588 -0.006081  0.159911  0.383777 -0.634338  0.926198   \n",
       "11987 -3.973178 -0.988518  0.829963  0.550453 -2.416701 -1.471810 -0.055511   \n",
       "11988 -1.959899 -3.760835 -0.907681 -1.565596  1.675875 -1.639891 -4.595915   \n",
       "11989 -1.095015 -1.266144  0.428989  3.028102  2.313566 -1.195695 -2.302849   \n",
       "11990  0.236500 -1.345926  2.442085 -1.230989 -0.510544 -1.793463  0.023324   \n",
       "11991 -2.499848  1.073839 -0.593883  0.262796 -2.273250  2.027045  1.878394   \n",
       "11992  0.003757  0.405644  0.210443  2.402584  2.156206  1.011167  0.623059   \n",
       "11993  0.955993 -0.261009  0.209048  1.163657  0.889022 -0.056930 -2.777939   \n",
       "11994 -0.716095  0.756280 -0.741979  1.897242 -2.333882  2.007778  2.111074   \n",
       "11995  1.168007  0.241298  2.122488 -0.621670  0.282304  0.766493 -1.776512   \n",
       "11996 -1.934941  2.799332  0.397737  1.472630  2.196302 -0.643940 -1.093721   \n",
       "11997 -0.406794  0.396411 -2.241750 -2.222946 -2.353107  0.699552  4.074507   \n",
       "11998  2.385417 -1.977389  0.220792 -1.613214  1.272667 -2.468381 -0.281892   \n",
       "11999 -0.733237  1.075118  0.352473 -2.343764 -0.847463  1.028252  0.817279   \n",
       "12000 -1.432069  1.103715  0.755288 -2.131283 -3.366171 -0.265366  0.185350   \n",
       "\n",
       "              7         8         9  ...        41        42        43  \\\n",
       "0     -0.018969 -0.000474  0.012799  ...  0.007054 -0.006031  0.008233   \n",
       "1     -0.527014  2.041000 -0.925828  ...  0.491917  1.500413  0.988275   \n",
       "2      0.962301  0.519433 -1.424071  ...  0.906236 -0.671340  0.463547   \n",
       "3     -0.095990 -0.300163 -2.010627  ...  1.847089 -0.072382 -0.047417   \n",
       "4      1.555675  1.533230 -0.587836  ... -0.171078 -1.585042  1.116186   \n",
       "5     -0.074541  2.117981  1.559328  ... -0.344765  1.173636  0.382666   \n",
       "6      0.672213  0.125088 -0.935890  ...  2.220851 -0.285988  0.072878   \n",
       "7     -0.287486 -2.614904 -3.071081  ...  0.639649 -0.049073 -1.248601   \n",
       "8      1.261316 -3.511781  1.539717  ...  0.506863 -0.779403 -1.027868   \n",
       "9      1.645649 -0.053200  0.142741  ... -2.074448 -0.874612  1.818556   \n",
       "10    -0.975741  0.074112  0.185986  ... -1.729129  0.892806 -1.670198   \n",
       "11     0.664636  1.524929  0.872408  ...  0.968030  1.295836  0.164649   \n",
       "12    -1.005620  0.204383 -0.113069  ...  0.238028 -1.379133  1.481303   \n",
       "13    -0.537315  0.343557 -0.157957  ...  0.974485  1.426844  1.567520   \n",
       "14     0.903491 -2.332952  0.003312  ... -2.343978 -0.949392 -0.136243   \n",
       "15    -2.801558 -1.523043 -3.646624  ... -1.207269  1.768233  1.833406   \n",
       "16    -0.636273  1.033843  3.059594  ...  0.876730 -0.794915 -1.266747   \n",
       "17    -3.315907 -0.887560  0.470329  ...  0.534998  0.417675 -0.086117   \n",
       "18     2.906400  0.570410 -1.985361  ... -1.407325  0.532786 -0.373575   \n",
       "19    -1.811008  0.773211  1.080640  ... -0.660455  0.394514 -1.365582   \n",
       "20    -2.052653  1.534486  1.825912  ...  1.087778  1.828017  1.321120   \n",
       "21    -1.520998  1.978605 -1.833641  ...  0.325806  0.421032  1.359355   \n",
       "22     1.275830  0.922578 -2.009521  ... -1.400624 -0.723114 -0.155336   \n",
       "23     2.608832 -2.800216  3.249932  ...  0.891580  0.570624 -1.419087   \n",
       "24     2.725744 -0.298856  1.960303  ...  0.489381 -0.497444  0.371637   \n",
       "25    -0.510806 -2.180518  1.276391  ... -0.618635 -0.468226 -0.730568   \n",
       "26    -1.376227  1.495291  0.222539  ... -1.654312 -0.122274  1.468356   \n",
       "27    -0.324749 -0.623412  0.784867  ... -0.161515 -2.106008  0.279960   \n",
       "28    -0.345809  0.230761 -1.483022  ...  0.779214  0.481392  0.517028   \n",
       "29    -0.743730 -2.565174 -1.251055  ... -1.216270  0.808175 -2.582358   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "11971 -0.129174 -1.566306  0.976028  ... -0.673497  0.289047 -0.316365   \n",
       "11972 -0.585109  1.139820  0.699457  ... -0.280176  1.877038 -2.006620   \n",
       "11973  1.192934  3.618536 -1.174175  ... -0.402376  1.145481 -0.020745   \n",
       "11974  0.161211 -0.553350  4.981703  ... -0.198253  0.438768 -1.148970   \n",
       "11975  0.905370  0.590356 -1.373211  ...  0.939826 -1.078713  0.156047   \n",
       "11976  3.661459  0.151983 -1.263478  ...  1.440532 -1.547701 -0.847287   \n",
       "11977  0.299594  1.610081 -0.667691  ...  0.890896  1.833872 -2.388668   \n",
       "11978 -1.949727  2.883147 -0.259917  ...  0.843601  1.357118  1.288548   \n",
       "11979  2.105937  1.808690  0.708242  ... -3.038848 -0.598950  2.258736   \n",
       "11980 -0.938251 -0.380137  1.573549  ...  2.757254  0.362615  0.243536   \n",
       "11981 -0.641512  0.656085 -1.475655  ...  0.638565 -1.561951 -0.632813   \n",
       "11982  0.696934 -1.257668  0.607366  ...  1.880389  1.903960  2.293422   \n",
       "11983 -0.238453 -1.174989  2.794780  ...  1.693842  1.079681 -0.373334   \n",
       "11984  1.421712 -2.476839  0.113366  ...  1.911012  0.696126 -0.260654   \n",
       "11985  4.499905 -1.537522  1.006884  ... -0.861024 -0.923334 -2.309468   \n",
       "11986 -2.104552 -1.784736  0.264489  ...  2.435680  0.195342  0.708646   \n",
       "11987  0.486027 -5.384121 -0.422543  ... -1.773844 -0.321205 -0.991462   \n",
       "11988  0.021654  0.166208  3.480695  ...  0.323036  2.227095 -0.846622   \n",
       "11989 -1.091309 -1.290842 -2.321850  ...  1.382571 -0.279537  0.913290   \n",
       "11990 -1.402546 -4.083741 -0.333655  ... -0.318570  0.936120 -0.403180   \n",
       "11991 -1.157621 -0.367089 -0.539423  ...  0.772118 -0.852295  0.620302   \n",
       "11992  3.463784 -0.900735  0.441167  ... -2.002505  1.094751  1.334342   \n",
       "11993  0.183170  1.409227 -0.838794  ...  0.031458 -1.053852 -0.080237   \n",
       "11994 -0.241472  2.266714  0.400536  ... -1.065921 -0.409353  0.789271   \n",
       "11995 -0.762112 -1.790033  0.057198  ...  0.305715  2.011013  1.180712   \n",
       "11996  1.155401 -2.511705 -0.274012  ...  0.749570 -1.492058  0.106827   \n",
       "11997  0.706102  0.029120  1.258924  ...  1.477645 -1.040513  0.720096   \n",
       "11998 -2.008536 -4.170027 -0.176729  ... -1.253062 -0.046685 -0.860414   \n",
       "11999  0.347465  0.949326 -2.039905  ... -0.396074  0.494003  0.566663   \n",
       "12000  0.264093  0.750939 -0.623580  ... -0.095448 -0.001635 -0.546501   \n",
       "\n",
       "             44        45        46        47        48        49  Normal?  \n",
       "0      0.000468  0.001704  0.003034  0.006221  0.005144 -0.009466      0.0  \n",
       "1      0.187765 -0.804564  0.887555 -0.013030 -0.080059 -0.393268      0.0  \n",
       "2     -1.069696  0.466183  0.547371 -0.626384  0.000918 -1.585993      0.0  \n",
       "3     -0.359991  0.442304 -0.489900  0.562185  0.248784 -0.505636      0.0  \n",
       "4     -0.219846 -1.054201 -0.708191 -0.891057 -0.433698 -0.861057      0.0  \n",
       "5      1.150170  1.289068 -0.635437  0.272962 -1.250388 -0.285791      0.0  \n",
       "6      0.146341 -0.867118  0.356124  0.146614  0.503536 -0.495361      0.0  \n",
       "7      0.344704 -0.549407  1.553585 -0.081885  0.196795 -0.363517      0.0  \n",
       "8      0.948576  1.100451  1.302081 -0.712222  0.254735  0.279973      0.0  \n",
       "9     -1.569693 -1.498720 -0.299998  0.115941 -1.159921  1.125589      0.0  \n",
       "10    -0.432876 -0.535024  0.635339  1.047735  0.520128  0.333359      0.0  \n",
       "11     0.746021  1.204942  0.526412 -0.071663  0.626179 -0.619189      0.0  \n",
       "12     0.777061 -0.034354  0.709078 -1.572460  0.133502  0.036547      0.0  \n",
       "13     1.171641  0.245582 -0.070390  1.617714  1.120578  0.151736      0.0  \n",
       "14    -0.239554 -0.939848  0.531523 -0.759164  0.668398 -1.185236      0.0  \n",
       "15    -1.008238 -0.327356 -0.658073  0.268712  0.445796 -0.775115      0.0  \n",
       "16     0.729701  1.149351 -0.088523 -1.486449  1.019229 -0.797592      0.0  \n",
       "17    -1.754030 -0.527726 -0.495249 -0.701816 -1.226338  0.726363      0.0  \n",
       "18     0.701886  0.785436 -0.645898 -1.283363  1.056896 -0.570667      0.0  \n",
       "19     1.663064  0.009438  0.487937 -0.542692  0.670331  1.503329      0.0  \n",
       "20    -1.847678 -1.008058  0.996512  0.421598 -0.219942  0.654331      0.0  \n",
       "21    -0.315683 -0.590700  0.398781  0.094830 -0.737189 -1.732351      0.0  \n",
       "22     1.708140  0.457126 -0.475600 -0.906052 -0.830917  0.433808      0.0  \n",
       "23     0.373260  0.872111  0.998091 -0.463426  0.740339  0.725388      0.0  \n",
       "24     0.914163  0.256280 -1.115768  0.388817  0.024428 -0.406505      0.0  \n",
       "25    -0.206562 -0.149883  0.131117  0.124050 -0.152299  2.213474      0.0  \n",
       "26    -1.554461 -0.582255  1.883963  0.763002  0.010577  0.835488      0.0  \n",
       "27     1.314532  0.233539  1.637556 -0.374170 -0.792148  0.571869      0.0  \n",
       "28    -0.744459 -1.208480 -1.451864  0.920813  0.497764 -0.546534      0.0  \n",
       "29     1.124949  0.520550 -0.747573 -0.719888  1.406912 -0.773137      0.0  \n",
       "...         ...       ...       ...       ...       ...       ...      ...  \n",
       "11971  0.264567  2.235367 -1.124104  0.335837  0.917009  0.336346      1.0  \n",
       "11972 -1.933253 -0.301478 -1.367966 -1.191636 -0.206729  0.106547      1.0  \n",
       "11973  0.154186 -0.578581 -1.845163  0.248591 -0.954870 -1.364246      1.0  \n",
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       "11978  1.310552  0.544532 -1.765730 -1.644703  1.665526 -0.011404      1.0  \n",
       "11979  0.598385 -1.817312  0.741694 -2.362624 -0.231939 -0.145460      1.0  \n",
       "11980  1.051807 -1.328285  2.058283  0.551110  0.116031 -0.050912      1.0  \n",
       "11981  0.744074  0.387719  0.823973 -0.904534  0.022580 -1.284544      1.0  \n",
       "11982  0.255141  0.143228 -0.829351  0.144316 -1.667281 -0.038695      1.0  \n",
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       "11984 -0.787283 -0.862120 -0.482057 -0.256165 -0.728528 -0.736768      1.0  \n",
       "11985  0.847486  0.249020  0.627576 -1.524187 -1.443826  0.450419      1.0  \n",
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       "11990 -0.494488  0.374408  0.091484 -0.658851 -0.621025  0.986703      1.0  \n",
       "11991 -0.200528  0.927405  1.375492 -0.006011  1.187555  0.359324      1.0  \n",
       "11992 -1.960769  0.437693  0.879606  1.365089 -1.186302 -0.464061      1.0  \n",
       "11993  0.293498  0.283078  1.246638 -0.342219  0.558138  0.349336      1.0  \n",
       "11994  0.617187  0.873003  0.525367 -0.944774  1.169370  0.150578      1.0  \n",
       "11995  0.708146  0.685965  0.995940  0.659285 -1.182510 -1.014396      1.0  \n",
       "11996 -0.664854 -0.019064  1.081311 -0.382820  1.766676 -1.427814      1.0  \n",
       "11997 -1.018004  0.968699 -1.467148  0.505201  1.037273  1.179134      1.0  \n",
       "11998 -1.126852  0.869450 -0.811977  0.599380 -0.946950 -0.002886      1.0  \n",
       "11999  0.221667 -0.299306 -0.354392  3.040238  0.192354 -1.293034      1.0  \n",
       "12000 -1.109757 -1.731317 -0.150037  0.361946  1.394608 -0.361351      1.0  \n",
       "\n",
       "[24002 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training dataset without labels\n",
    "X = audio_pca.iloc[:,1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training labels\n",
    "Y = audio_pca.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1.0/3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver=\"lbfgs\")\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ypredict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2080 1901]\n",
      " [2087 1933]]\n"
     ]
    }
   ],
   "source": [
    "# use \"labels\" argument to re-organize the entries in the confusion matrix\n",
    "# (we typically treat malignant as positive and benign as negative)\n",
    "cmat = confusion_matrix(Y_test,Ypredict, labels=[0.0, 1.0])\n",
    "print(cmat)\n",
    "TP, FP, FN, TN = cmat.ravel()\n",
    "npatients = Y_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4991600671946244\n"
     ]
    }
   ],
   "source": [
    "# True positive rate (TPR), or sensitivity or recall: When it's actually yes, how often does it predict yes?\n",
    "TPR = TP/(TP+FN)\n",
    "print(TPR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5041731872717788\n"
     ]
    }
   ],
   "source": [
    "# True negative rate(TNR) or specificity:  When it's actually no, how often does it predict no?\n",
    "TNR = TN/(FP+TN)   \n",
    "print(TNR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5224817884953529\n"
     ]
    }
   ],
   "source": [
    "# Precision: When it predicts yes, how often is it correct?\n",
    "p = TP/(TP+FP)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dict_classifiers = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "    \"Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"Linear SVM\": SVC(),\n",
    "    \"Gradient Boosting Classifier\": GradientBoostingClassifier(),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"Naive Bayes\": GaussianNB()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine accuracy of classifier given data pairs (X, Y)\n",
    "def accuracy(classifier, X, Y):\n",
    "    Ypredict = classifier.predict(X)\n",
    "    return (Y==Ypredict).mean()\n",
    "\n",
    "def batch_classify(X_train, Y_train, X_test, Y_test, verbose = True):\n",
    "    print(\"{:34s}{:15s}{:12s}{:12s}\".format(\"Classifiers\", \"Test-score\", \"TPR\", \"TNR\"))\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    labels = []\n",
    "\n",
    "    for classifier_name, classifier in list(dict_classifiers.items()):\n",
    "        \n",
    "        # Fit the model\n",
    "        labels.append(classifier_name)\n",
    "        classifier.fit(X_train, Y_train)\n",
    "        # Make predictions on the test data\n",
    "        predictions = classifier.predict(X_test)\n",
    "        cmat = confusion_matrix(Y_test, predictions, labels=[0.0, 1.0])\n",
    "        TP, FP, FN, TN = cmat.ravel()\n",
    "        # Get scores\n",
    "        train_score.append(accuracy(classifier, X_train, Y_train))\n",
    "        test_score.append(accuracy(classifier, X_test, Y_test))\n",
    "        if verbose:\n",
    "            print(\"{c:30}{t1:12f}{t2:12f}{t3:12f}\".format(c=classifier_name,t1=test_score[-1], t2=TP/(TP+FN), t3=TN/(FP+TN) ), )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers                       Test-score     TPR         TNR         \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jiachen Tu\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression               0.501562    0.499160    0.504173\n",
      "Nearest Neighbors                 0.534558    0.520113    0.591811\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jiachen Tu\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVM                        0.645044    0.648917    0.641487\n",
      "Gradient Boosting Classifier      0.570054    0.560773    0.581690\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jiachen Tu\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest                     0.537183    0.528495    0.550752\n",
      "Naive Bayes                       0.563680    0.553447    0.577407\n"
     ]
    }
   ],
   "source": [
    "batch_classify(X_train, Y_train, X_test, Y_test, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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