Classification_EEG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/Tanuj2552/Home-Automation-using-EEG/blob/main/Classification_EEG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "fkaqdSJyAaeM",
    "outputId": "c1d57eaa-900e-4cc6-ab94-81f0346e83c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of EEG:  (59, 190473)\n",
      "sample rate:  100\n",
      "Number of channels:  59\n",
      "Channel names:  ['AF3', 'AF4', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'FC5', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'FC6', 'CFC7', 'CFC5', 'CFC3', 'CFC1', 'CFC2', 'CFC4', 'CFC6', 'CFC8', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'T8', 'CCP7', 'CCP5', 'CCP3', 'CCP1', 'CCP2', 'CCP4', 'CCP6', 'CCP8', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'CP6', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'PO1', 'PO2', 'O1', 'O2']\n",
      "Number of Events:  1\n",
      "Event codes:  [-1  1]\n",
      "Class labels:  ['left', 'right']\n",
      "Number of classes:  2\n",
      "Shape of trials[cl1]:  (59, 200, 100)\n",
      "Shape of trials[cl2]:  (59, 200, 100)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.io \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import mlab\n",
    "#import gdown\n",
    " \n",
    "#m = scipy.io.loadmat(r'/content/drive/MyDrive/BCICIV_1_mat/BCICIV_calib_ds1d.mat',struct_as_record = True)\n",
    "m = scipy.io.loadmat(r'D:\\Rig\\EEG\\BCICIV_1_mat/BCICIV_calib_ds1d.mat',struct_as_record = True)\n",
    " \n",
    "sample_rate = m['nfo']['fs'][0][0][0][0]\n",
    "EEG = m['cnt'].T\n",
    "nchannels, nsamples = EEG.shape\n",
    " \n",
    "channel_names = [s[0] for s in m['nfo']['clab'][0][0][0]]\n",
    "event_onsets = m['mrk'][0][0][0]\n",
    "event_codes = m['mrk'][0][0][1]\n",
    "\n",
    "labels = np.zeros((1,nsamples),int)\n",
    "labels[0, event_onsets] = event_codes\n",
    " \n",
    "cl_lab = [s[0] for s in m['nfo']['classes'][0][0][0]]\n",
    "cl1 = cl_lab[0]\n",
    "cl2 = cl_lab[1]\n",
    " \n",
    "nclasses = len(cl_lab)\n",
    "nevents = len(event_onsets)\n",
    " \n",
    "print('shape of EEG: ',EEG.shape)\n",
    "print('sample rate: ',sample_rate)\n",
    "print('Number of channels: ',nchannels)\n",
    "print('Channel names: ',channel_names)\n",
    "print('Number of Events: ',len(event_onsets))\n",
    "print('Event codes: ',np.unique(event_codes))\n",
    "print('Class labels: ',cl_lab)\n",
    "print('Number of classes: ',nclasses)\n",
    " \n",
    " \n",
    " \n",
    "trials = {}\n",
    " \n",
    "win = np.arange(int(0.5*sample_rate),int(2.5*sample_rate))\n",
    " \n",
    "nsamples = len(win)\n",
    " \n",
    "for cl, code in zip(cl_lab, np.unique(event_codes)):\n",
    " \n",
    "    cl_onsets = event_onsets[event_codes == code]\n",
    " \n",
    "    trials[cl] = np.zeros((nchannels, nsamples, len(cl_onsets)))\n",
    " \n",
    "    for i,onset in enumerate(cl_onsets):\n",
    "        trials[cl][:,:,i] = EEG[:, win+int(onset*0.1)]\n",
    " \n",
    "print('Shape of trials[cl1]: ',trials[cl1].shape)\n",
    "print('Shape of trials[cl2]: ',trials[cl2].shape)\n",
    " \n",
    "def psd(trials):\n",
    "    ntrials = trials.shape[2]\n",
    "    trials_PSD = np.zeros((nchannels, 101, ntrials))\n",
    " \n",
    "    for trial in range(ntrials):\n",
    "        for ch in range(nchannels):\n",
    " \n",
    "            (PSD,freqs) = mlab.psd(trials[ch,:,trial],NFFT=int(nsamples),Fs=sample_rate)\n",
    "            trials_PSD[ch,:,trial] = PSD.ravel()\n",
    " \n",
    "    return trials_PSD,freqs\n",
    " \n",
    "psd_r, freqs = psd(trials[cl1])\n",
    "psd_f, freqs = psd(trials[cl2])\n",
    "trials_PSD = {cl1: psd_r, cl2: psd_f}\n",
    " \n",
    "def plot_psd(trials_PSD, freqs, chan_ind, chan_lab = None, maxy = None):\n",
    " \n",
    "    plt.figure(figsize = (12,12))\n",
    "    nchans = len(chan_ind)\n",
    " \n",
    "    ncols = np.ceil(nchans/3)\n",
    "    nrows = min(3,nchans)\n",
    " \n",
    "    for i,ch in enumerate(chan_ind):\n",
    "        plt.subplot(nrows,ncols,i+1)\n",
    " \n",
    "        for cl in trials.keys():\n",
    "            plt.plot(freqs, np.mean(trials_PSD[cl][ch,:,:],axis = 1), label = cl)\n",
    " \n",
    "        plt.xlim(1,30)\n",
    " \n",
    "        if(maxy != None):\n",
    "            plt.ylim(0,maxy)\n",
    "        else:\n",
    "            plt.ylim(1,1200)\n",
    "        \n",
    "        plt.grid()\n",
    " \n",
    "        plt.xlabel('Frequency (Hz)')\n",
    " \n",
    "        if(chan_lab == None):\n",
    "            plt.title(f'Channel {ch+1}')\n",
    "        else:\n",
    "            plt.title(chan_lab[i])\n",
    "\n",
    "        plt.xticks([4,8,12,15,18,30,50], [4,8,12,15,18,30,50])\n",
    " \n",
    "        plt.legend()\n",
    " \n",
    "    plt.tight_layout()\n",
    " \n",
    "plot_psd(trials_PSD,freqs, [channel_names.index(ch) for ch in ['C3','Cz','C4']], chan_lab = ['left','center','right'],maxy = 500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 887
    },
    "id": "3IoK-GaIAgJc",
    "outputId": "a2da0a8e-c8c9-4ace-bcbc-d5479299113d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_diff_freqs(trials_PSD, useful_chan, max_x = None, max_y = None):\n",
    "    #useful_chan = ['C3', 'Cz', 'C4']\n",
    " \n",
    "    fig = plt.figure(figsize = (12,12))\n",
    "    nchans = len(useful_chan)\n",
    "    ncols = np.ceil(nchans/3)\n",
    "    nrows = min(3,nchans)\n",
    " \n",
    "    \n",
    "    for i in range(len(useful_chan)):\n",
    "        plt.subplot(nrows,ncols,i+1)\n",
    "        \n",
    "        chan = useful_chan[i] \n",
    "        plt.plot(freqs, abs(np.mean(trials_PSD['left'][channel_names.index(chan),:,:],axis = 1) - np.mean(trials_PSD['right'][channel_names.index(chan),:,:],axis = 1)))\n",
    "        if(max_x):\n",
    "            plt.xlim(1,max_x)\n",
    "        else:\n",
    "            plt.xlim(1,30)\n",
    "        if(max_y):\n",
    "            plt.ylim(1,max_y)\n",
    "        else:\n",
    "            plt.ylim(0,1200)\n",
    "        plt.grid()\n",
    "        plt.title(str(useful_chan[i]))\n",
    "        \n",
    "        plt.xticks([4,8,12,15,18,30,50], ['delta', 'theta', 'alpha', 'low-beta','mid-beta','high-beta', 'gamma'])\n",
    " \n",
    "    plt.tight_layout()\n",
    "    plt.xlabel(\"Difference between psd values of the different classes\")\n",
    " \n",
    "plot_diff_freqs(trials_PSD,['C3', 'Cz', 'C4'])\n",
    "# https://nhahealth.com/brainwaves-the-language/#:~:text=The%20raw%20EEG%20has%20usually,(less%20than%204%20Hz).\n",
    "#From various resources from the net, we conclude that mostly alpha and low beta waves (8-15 hz) contribute to the mind/body coordination, integration & associated tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "id": "zAnEj1pvXHOM",
    "outputId": "c1581051-554b-4f05-8051-ebb241812d7d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "def bpf(trials, bpf_range, N):\n",
    "    a, b = scipy.signal.iirfilter(6, [float(float(x)/50.0) for x in  bpf_range])\n",
    "    \n",
    "    trial_keys = list(trials.keys())\n",
    "    ntrials = trials[trial_keys[0]].shape[2]\n",
    "    trials_bpf = {}\n",
    "    for j in trial_keys:\n",
    "        trials_bpf[j] = np.zeros((nchannels, nsamples, ntrials))\n",
    "        for i in range(ntrials):\n",
    "            trials_bpf[j][:,:,i] = signal.lfilter(a, b, trials[j][:,:,i], axis=1)\n",
    "    \n",
    "    return trials_bpf\n",
    "    \n",
    "trials_bpf = bpf(trials, [8,15], 6)\n",
    "\n",
    "psd_r, freqs = psd(trials_bpf[cl1])\n",
    "psd_f, freqs = psd(trials_bpf[cl2])\n",
    "\n",
    "trials_psd_bpf = {cl1:psd_r, cl2:psd_f}\n",
    "\n",
    "\n",
    "\n",
    "plot_psd(trials_psd_bpf,freqs, [channel_names.index(ch) for ch in ['C3','Cz','C4']], chan_lab = ['left','center','right'],maxy = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 887
    },
    "id": "JP6RlBWHDhrr",
    "outputId": "704147bc-a63b-4a48-ad69-1f9823643d2b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-2-551990861fc4>:11: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_diff_freqs(trials_psd_bpf, ['C3', 'Cz', 'C4'], max_y = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YaaSemlXd5ss"
   },
   "outputs": [],
   "source": [
    "trails_logvar_cl1 = np.log(np.var(trials_psd_bpf[cl1], axis = 1))\n",
    "trails_logvar_cl2 = np.log(np.var(trials_psd_bpf[cl2], axis = 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vIB1ShGqeCGX"
   },
   "outputs": [],
   "source": [
    "trails_logvar_feat = {cl1: trails_logvar_cl1, cl2: trails_logvar_cl2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jsjJYA1uiuq9"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "e9sHNK08fig5",
    "outputId": "0c42ff19-1d15-4d92-9872-4f381a90f0ef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_logvar(trails):\n",
    "    plt.figure(figsize = (12,6))\n",
    "\n",
    "    x_data = list(range(nchannels))\n",
    "\n",
    "    y1 = np.mean(trails[cl1], axis = 1)\n",
    "    y2 = np.mean(trails[cl2], axis = 1)\n",
    "\n",
    "    plt.plot(x_data, y1)\n",
    "    plt.plot(x_data, y2)\n",
    "\n",
    "    plt.legend(['left', 'right'])\n",
    "\n",
    "    plt.xlabel('channels')\n",
    "    plt.ylabel('log variances values')\n",
    "    plt.title('log variances of each channel')\n",
    "\n",
    "plot_logvar(trails_logvar_feat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Yl-AZFAcpmOu"
   },
   "outputs": [],
   "source": [
    "\n",
    "from numpy import linalg\n",
    "\n",
    "def cov(data):\n",
    "    n = data.shape[2]\n",
    "    covs = [ data[:,:,i].dot(data[:,:,i].T) / nsamples for i in range(n) ]\n",
    "    return np.mean(covs, axis=0)\n",
    "\n",
    "def whitening(sigma):\n",
    "    U, l, _ = linalg.svd(sigma)\n",
    "    return U.dot( np.diag(l ** -0.5) )\n",
    "\n",
    "def csp(data_1, data_2):\n",
    "    cov_1 = cov(data_1)\n",
    "    cov_2 = cov(data_2)\n",
    "    P = whitening(cov_1 + cov_2)\n",
    "    B, _, _ = linalg.svd( P.T.dot(cov_2).dot(P) )\n",
    "    W = P.dot(B)\n",
    "    return W\n",
    "\n",
    "def apply_mix(W, data):\n",
    "    n = data.shape[2]\n",
    "    data_csp = np.zeros((nchannels, nsamples, n))\n",
    "    for i in range(n):\n",
    "        data_csp[:,:,i] = W.T.dot(data[:,:,i])\n",
    "    return data_csp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "EBG71lynyyfm"
   },
   "outputs": [],
   "source": [
    "W = csp(trials_bpf[cl1], trials_bpf[cl2])\n",
    "trials_bpf_csp = {cl1: apply_mix(W, trials_bpf[cl1]),\n",
    "              cl2: apply_mix(W, trials_bpf[cl2])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "rfbzQzJX1BrX",
    "outputId": "6e0a136f-fd26-4146-d874-367d6657b5b8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trials_logvar_csp = {cl1: np.log(np.var(trials_bpf_csp[cl1],axis = 1)),\n",
    "                 cl2: np.log(np.var(trials_bpf_csp[cl2], axis = 1))}\n",
    "plot_logvar(trials_logvar_csp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "id": "x_s35y8P1CPT",
    "outputId": "407d41f4-54e9-4e00-952c-484df341927a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n",
      "<ipython-input-1-64e65b60f39b>:83: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows,ncols,i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psd_r, freqs = psd(trials_bpf_csp[cl1])\n",
    "psd_f, freqs = psd(trials_bpf_csp[cl2])\n",
    "\n",
    "trials_psd_bpf_csp = {cl1:psd_r, cl2:psd_f}\n",
    "\n",
    "plot_psd(trials_psd_bpf_csp, freqs, [0,58,-1], chan_lab=['first component', 'middle component', 'last component'], maxy = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ykxh17gM4G9n"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "rKsypaGIjdVa"
   },
   "outputs": [],
   "source": [
    "split_percentage = 0.75\n",
    "\n",
    "ntrain_r = int(trials_bpf[cl1].shape[2] * split_percentage)\n",
    "ntrain_f = int(trials_bpf[cl2].shape[2] * split_percentage)\n",
    "\n",
    "train = {cl1: trials_bpf[cl1][:,:,:ntrain_r],\n",
    "         cl2: trials_bpf[cl2][:,:,:ntrain_f]}\n",
    "\n",
    "test = {cl1: trials_bpf[cl1][:,:,ntrain_r:],\n",
    "        cl2: trials_bpf[cl2][:,:,ntrain_f:]}\n",
    "\n",
    "\n",
    "W = csp(train[cl1], train[cl2])\n",
    "\n",
    "# Apply the CSP on both the training and test set\n",
    "train[cl1] = apply_mix(W, train[cl1])\n",
    "train[cl2] = apply_mix(W, train[cl2])\n",
    "test[cl1] = apply_mix(W, test[cl1])\n",
    "test[cl2] = apply_mix(W, test[cl2])\n",
    "\n",
    "comp = np.array([0,-1,30,40,20])\n",
    "comp = np.array([channel_names.index(ch) for ch in ['C3','Cz','C4']])\n",
    "comp = np.array(list(range(59)))\n",
    "train[cl1] = train[cl1][comp,:,:]\n",
    "train[cl2] = train[cl2][comp,:,:]\n",
    "test[cl1] = test[cl1][comp,:,:]\n",
    "test[cl2] = test[cl2][comp,:,:]\n",
    "\n",
    "\n",
    "train[cl1] = np.log(np.var((train[cl1]), axis = 1))\n",
    "train[cl2] = np.log(np.var((train[cl2]),axis = 1))\n",
    "test[cl1] = np.log(np.var((test[cl2]),axis = 1))\n",
    "test[cl2] = np.log(np.var((test[cl2]),axis = 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CtEQu4UM1CnN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0TkECyM89DSY",
    "outputId": "7e01b1c6-6876-4a6e-9b4d-9c8761ad1e22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         AF3       AF4        F5        F3        F1        Fz        F2  \\\n",
      "0  -1.293883 -0.558199 -1.424345 -0.710245 -0.750049 -0.424271 -0.818224   \n",
      "1  -1.142830 -1.262179 -1.141020 -0.871982 -1.032266 -0.657553 -0.055954   \n",
      "2  -1.632907 -0.546396 -1.324356 -0.933269 -0.728962 -0.601624 -0.564179   \n",
      "3  -1.039638 -0.748650 -0.975612 -1.085733  0.122446 -1.040057 -0.859503   \n",
      "4  -1.024207 -1.181740 -1.393440 -1.766592 -0.941281 -0.472310 -0.750300   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "95 -0.435012 -0.691531 -1.156020 -0.328291 -0.713197 -0.319916 -1.031483   \n",
      "96  0.114394 -0.485024 -0.977653 -0.403726 -0.449793 -0.409868 -0.256878   \n",
      "97 -0.493389 -0.577490 -0.151336 -0.404734 -0.475092 -0.392159 -0.935342   \n",
      "98 -0.126374 -1.404032 -0.315866 -0.873370 -0.743405 -0.643290 -0.726891   \n",
      "99 -1.277867 -1.357191 -0.219122 -1.336714 -0.814259  0.303466 -1.709560   \n",
      "\n",
      "          F4        F6       FC5  ...        P3        P1        Pz        P2  \\\n",
      "0  -0.805464 -0.631454 -0.403140  ... -0.556578 -0.268644 -0.701171 -0.373840   \n",
      "1  -0.426621 -0.394428 -0.586738  ... -1.118756 -1.186719 -1.541847 -1.033864   \n",
      "2  -0.568390 -0.918987 -0.254017  ... -0.865423 -0.860572 -1.011851 -1.046974   \n",
      "3  -0.632651 -0.669631 -0.763850  ... -0.642996 -0.283527 -0.380720 -0.778480   \n",
      "4  -0.983456 -1.232506 -0.328985  ... -0.587052 -0.650431 -0.944507 -0.177590   \n",
      "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
      "95 -1.041770 -0.051012 -0.511051  ... -0.757473 -0.493277 -0.719251 -0.564798   \n",
      "96 -1.185228 -1.219207 -0.147218  ... -0.909179 -0.727027 -0.741061 -0.663856   \n",
      "97 -0.699518 -0.409748 -0.671898  ... -1.418995 -0.715574 -0.452103 -0.789972   \n",
      "98 -1.011031 -0.888210 -0.625605  ... -1.259864  0.058567 -1.080819 -1.797905   \n",
      "99 -1.000955 -0.241187 -1.022003  ... -1.476146 -1.271962 -0.720025 -1.717757   \n",
      "\n",
      "          P4        P6       PO1       PO2        O1        O2  \n",
      "0  -0.502208 -0.351063 -0.918450 -1.246159  0.463444  0.412153  \n",
      "1  -0.698653 -0.670782 -1.204806 -0.411696 -1.193933 -0.803508  \n",
      "2  -1.026684 -1.174555 -0.892355 -0.372195 -0.864807 -1.708206  \n",
      "3  -1.085982 -1.019791 -0.248511  1.857521 -0.009875 -1.394664  \n",
      "4  -0.421526 -0.094456 -0.705518 -1.637738  0.865947 -1.100723  \n",
      "..       ...       ...       ...       ...       ...       ...  \n",
      "95 -0.352211 -0.648511 -0.360611 -0.807005 -0.820161 -1.426626  \n",
      "96 -0.139919 -0.230762 -0.839122 -0.428150 -1.513420 -0.785331  \n",
      "97  0.063582 -0.831441 -0.182353 -0.747821 -1.258630 -0.279989  \n",
      "98 -0.870330 -0.473377 -1.640435 -1.951425 -1.768477 -1.159696  \n",
      "99 -0.757026 -0.755378 -0.801790 -0.856081 -1.728683 -0.576869  \n",
      "\n",
      "[100 rows x 59 columns]\n",
      "         AF3       AF4        F5        F3        F1        Fz        F2  \\\n",
      "0   0.040202 -0.935722 -0.320776 -0.407005 -0.535445  0.096681 -0.541115   \n",
      "1  -0.497102 -0.579643 -0.946564 -0.820780 -0.133221 -0.414933 -0.436355   \n",
      "2   0.666788 -0.256731 -0.016054 -0.953943 -0.332284  0.916238 -0.575104   \n",
      "3   1.453898  0.460351 -0.623823 -0.208477 -0.267191 -0.363829 -0.728494   \n",
      "4  -0.076255 -0.642764 -0.779341 -0.421205 -0.802313 -0.177459  0.068451   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "95 -0.435012 -0.691531 -1.156020 -0.328291 -0.713197 -0.319916 -1.031483   \n",
      "96  0.114394 -0.485024 -0.977653 -0.403726 -0.449793 -0.409868 -0.256878   \n",
      "97 -0.493389 -0.577490 -0.151336 -0.404734 -0.475092 -0.392159 -0.935342   \n",
      "98 -0.126374 -1.404032 -0.315866 -0.873370 -0.743405 -0.643290 -0.726891   \n",
      "99 -1.277867 -1.357191 -0.219122 -1.336714 -0.814259  0.303466 -1.709560   \n",
      "\n",
      "          F4        F6       FC5  ...        P3        P1        Pz        P2  \\\n",
      "0   0.089349 -0.413701 -0.615638  ... -1.011133 -1.045185 -1.744306 -0.984432   \n",
      "1   0.000910 -0.298185 -0.511526  ... -1.142229 -1.077719 -1.282681 -1.514419   \n",
      "2  -0.327890 -0.563775 -0.555546  ... -0.570766 -0.553105 -0.616405 -1.381510   \n",
      "3  -0.745805 -0.452485 -0.483399  ... -1.024340 -1.234091 -1.552142 -0.830874   \n",
      "4  -0.414559 -0.607996 -0.698187  ... -1.443765 -1.026435 -1.082632 -0.852267   \n",
      "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
      "95 -1.041770 -0.051012 -0.511051  ... -0.757473 -0.493277 -0.719251 -0.564798   \n",
      "96 -1.185228 -1.219207 -0.147218  ... -0.909179 -0.727027 -0.741061 -0.663856   \n",
      "97 -0.699518 -0.409748 -0.671898  ... -1.418995 -0.715574 -0.452103 -0.789972   \n",
      "98 -1.011031 -0.888210 -0.625605  ... -1.259864  0.058567 -1.080819 -1.797905   \n",
      "99 -1.000955 -0.241187 -1.022003  ... -1.476146 -1.271962 -0.720025 -1.717757   \n",
      "\n",
      "          P4        P6       PO1       PO2        O1        O2  \n",
      "0  -0.839242 -1.022593 -1.046342 -0.608096 -0.884127 -0.951995  \n",
      "1  -0.594756 -1.530525 -1.236790 -0.746496 -1.498652 -1.486636  \n",
      "2  -0.691742 -0.695017 -0.278695 -1.100933 -1.146983 -1.477963  \n",
      "3  -0.759489 -1.037906 -1.584709 -1.168941 -2.155282 -1.777411  \n",
      "4  -1.798569 -0.626692 -1.066754 -1.133811 -0.850520 -0.633697  \n",
      "..       ...       ...       ...       ...       ...       ...  \n",
      "95 -0.352211 -0.648511 -0.360611 -0.807005 -0.820161 -1.426626  \n",
      "96 -0.139919 -0.230762 -0.839122 -0.428150 -1.513420 -0.785331  \n",
      "97  0.063582 -0.831441 -0.182353 -0.747821 -1.258630 -0.279989  \n",
      "98 -0.870330 -0.473377 -1.640435 -1.951425 -1.768477 -1.159696  \n",
      "99 -0.757026 -0.755378 -0.801790 -0.856081 -1.728683 -0.576869  \n",
      "\n",
      "[100 rows x 59 columns]\n",
      "         AF3       AF4        F5        F3        F1        Fz        F2  \\\n",
      "0  -1.293883 -0.558199 -1.424345 -0.710245 -0.750049 -0.424271 -0.818224   \n",
      "1  -1.142830 -1.262179 -1.141020 -0.871982 -1.032266 -0.657553 -0.055954   \n",
      "2  -1.632907 -0.546396 -1.324356 -0.933269 -0.728962 -0.601624 -0.564179   \n",
      "3  -1.039638 -0.748650 -0.975612 -1.085733  0.122446 -1.040057 -0.859503   \n",
      "4  -1.024207 -1.181740 -1.393440 -1.766592 -0.941281 -0.472310 -0.750300   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "95 -0.435012 -0.691531 -1.156020 -0.328291 -0.713197 -0.319916 -1.031483   \n",
      "96  0.114394 -0.485024 -0.977653 -0.403726 -0.449793 -0.409868 -0.256878   \n",
      "97 -0.493389 -0.577490 -0.151336 -0.404734 -0.475092 -0.392159 -0.935342   \n",
      "98 -0.126374 -1.404032 -0.315866 -0.873370 -0.743405 -0.643290 -0.726891   \n",
      "99 -1.277867 -1.357191 -0.219122 -1.336714 -0.814259  0.303466 -1.709560   \n",
      "\n",
      "          F4        F6       FC5  ...        P3        P1        Pz        P2  \\\n",
      "0  -0.805464 -0.631454 -0.403140  ... -0.556578 -0.268644 -0.701171 -0.373840   \n",
      "1  -0.426621 -0.394428 -0.586738  ... -1.118756 -1.186719 -1.541847 -1.033864   \n",
      "2  -0.568390 -0.918987 -0.254017  ... -0.865423 -0.860572 -1.011851 -1.046974   \n",
      "3  -0.632651 -0.669631 -0.763850  ... -0.642996 -0.283527 -0.380720 -0.778480   \n",
      "4  -0.983456 -1.232506 -0.328985  ... -0.587052 -0.650431 -0.944507 -0.177590   \n",
      "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
      "95 -1.041770 -0.051012 -0.511051  ... -0.757473 -0.493277 -0.719251 -0.564798   \n",
      "96 -1.185228 -1.219207 -0.147218  ... -0.909179 -0.727027 -0.741061 -0.663856   \n",
      "97 -0.699518 -0.409748 -0.671898  ... -1.418995 -0.715574 -0.452103 -0.789972   \n",
      "98 -1.011031 -0.888210 -0.625605  ... -1.259864  0.058567 -1.080819 -1.797905   \n",
      "99 -1.000955 -0.241187 -1.022003  ... -1.476146 -1.271962 -0.720025 -1.717757   \n",
      "\n",
      "          P4        P6       PO1       PO2        O1        O2  \n",
      "0  -0.502208 -0.351063 -0.918450 -1.246159  0.463444  0.412153  \n",
      "1  -0.698653 -0.670782 -1.204806 -0.411696 -1.193933 -0.803508  \n",
      "2  -1.026684 -1.174555 -0.892355 -0.372195 -0.864807 -1.708206  \n",
      "3  -1.085982 -1.019791 -0.248511  1.857521 -0.009875 -1.394664  \n",
      "4  -0.421526 -0.094456 -0.705518 -1.637738  0.865947 -1.100723  \n",
      "..       ...       ...       ...       ...       ...       ...  \n",
      "95 -0.352211 -0.648511 -0.360611 -0.807005 -0.820161 -1.426626  \n",
      "96 -0.139919 -0.230762 -0.839122 -0.428150 -1.513420 -0.785331  \n",
      "97  0.063582 -0.831441 -0.182353 -0.747821 -1.258630 -0.279989  \n",
      "98 -0.870330 -0.473377 -1.640435 -1.951425 -1.768477 -1.159696  \n",
      "99 -0.757026 -0.755378 -0.801790 -0.856081 -1.728683 -0.576869  \n",
      "\n",
      "[200 rows x 59 columns]\n",
      "         AF3       AF4        F5        F3        F1        Fz        F2  \\\n",
      "36 -1.442562 -1.776588 -1.438607 -1.195796 -2.009606 -0.907694 -0.454188   \n",
      "35 -1.477718 -1.406400 -1.303634 -1.472235 -0.927031 -1.719175 -1.428233   \n",
      "71 -0.797766 -0.837022 -1.015692 -0.372553 -0.160139 -0.777016  1.469782   \n",
      "76 -0.332034 -0.301276 -0.881502 -0.217289  0.734441 -0.179987  0.041904   \n",
      "1  -0.497102 -0.579643 -0.946564 -0.820780 -0.133221 -0.414933 -0.436355   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "84 -0.006934 -1.024188 -1.197401 -0.839145 -0.666453 -0.764855 -1.412478   \n",
      "22 -1.466843 -1.260629 -1.246043 -1.255359 -1.555435 -1.395492 -1.592361   \n",
      "7  -1.946834 -0.628481 -0.199438 -1.130297  0.516478 -0.263306 -0.558446   \n",
      "36  0.738429 -0.649594  0.782756 -0.725287 -0.628969 -1.200204 -1.826366   \n",
      "69  0.187616 -0.386072  0.630747 -0.694584 -0.775658 -0.306361 -0.754180   \n",
      "\n",
      "          F4        F6       FC5  ...        P1        Pz        P2        P4  \\\n",
      "36 -1.296648 -1.530137 -1.539953  ...  0.013634 -1.210835 -0.711828 -1.423770   \n",
      "35 -0.939224 -1.234660 -1.633354  ... -0.340977 -1.355320 -1.274914 -0.638820   \n",
      "71 -1.043341  0.403989 -0.109148  ... -0.360893 -0.334674 -0.731743 -0.959849   \n",
      "76 -0.379407 -0.794700 -0.299282  ...  0.296012 -0.040879 -0.622875 -0.340554   \n",
      "1   0.000910 -0.298185 -0.511526  ... -1.077719 -1.282681 -1.514419 -0.594756   \n",
      "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
      "84 -0.595890 -0.222533 -0.581555  ... -0.942504 -0.761938 -0.802032 -1.389282   \n",
      "22 -2.102296 -0.867838 -1.372270  ... -1.376139 -1.121591 -1.458687 -1.408481   \n",
      "7  -0.407180  0.226450  0.099597  ... -1.229939 -1.101329 -1.109803 -1.395532   \n",
      "36 -1.084613 -0.696043 -1.237328  ... -1.307892 -1.518826 -0.902861 -0.936779   \n",
      "69 -0.301110 -0.653415 -0.386077  ... -0.812600 -1.194704 -1.122319 -0.525043   \n",
      "\n",
      "          P6       PO1       PO2        O1        O2  Y  \n",
      "36 -1.508950 -1.238604 -1.003139  0.297417 -2.001529  0  \n",
      "35 -1.199152 -1.687160 -1.727218 -0.867763 -1.725454  0  \n",
      "71 -1.046807 -0.711483 -0.968223 -0.901983 -1.406901  1  \n",
      "76 -0.134340 -0.244757 -0.556440  0.064261  0.641920  1  \n",
      "1  -1.530525 -1.236790 -0.746496 -1.498652 -1.486636  1  \n",
      "..       ...       ...       ...       ...       ... ..  \n",
      "84 -1.276649 -0.946788 -0.414257 -1.485406  0.650241  0  \n",
      "22 -1.080994 -1.295256 -0.631209 -1.033111 -0.834278  0  \n",
      "7  -0.429284 -1.389370 -0.340563 -0.758627 -0.599120  1  \n",
      "36 -1.300142 -1.740163 -1.561527  0.123074 -2.323712  1  \n",
      "69 -1.151310 -1.232148 -0.795833 -1.507188 -1.399493  1  \n",
      "\n",
      "[200 rows x 60 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df2 = pd.DataFrame()\n",
    "Y1 = []\n",
    "Y2 = []\n",
    "\n",
    "for i in range(len(comp)):\n",
    "    df[channel_names[comp[i]]] = np.append(train[cl1][i],test[cl1][i])\n",
    "    df2[channel_names[comp[i]]] = np.append(train[cl2][i],test[cl2][i])\n",
    "    \n",
    "\n",
    "Y1 = [0]*len(df)\n",
    "Y2 = [1]*len(df2)\n",
    "Y_final = Y1 + Y2\n",
    "\n",
    "df_final = pd.concat([df, df2])\n",
    "df_total = df_final.copy()\n",
    "df_total['Y'] = Y_final\n",
    "\n",
    "print(df)\n",
    "print(df2)\n",
    "print(df_final)\n",
    "df_total = df_total.sample(frac = 1)\n",
    "print(df_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R_xoYhROEelx"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "emHCuvFOQSuB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ITtuQet3Eetg"
   },
   "outputs": [],
   "source": [
    "def my_confusion_matrix(predicted, Y_test):\n",
    "    l = len(predicted)\n",
    "    check = [predicted[i] == Y_test[i] for i in range(l)]\n",
    "\n",
    "    conf = np.array([\n",
    "   [ [predicted[i]==Y_test[i]==1 for i in range(l)].count(1), Y_test.count(1) - [predicted[i]==Y_test[i]==1 for i in range(l)].count(1) ],\n",
    "    [ Y_test.count(0) -[predicted[i]==Y_test[i]==0 for i in range(l)].count(1), [predicted[i]==Y_test[i]==0 for i in range(l)].count(1) ],\n",
    "])\n",
    "    print(\"The Confusion Matrix is given as: \")\n",
    "    print(conf)\n",
    "    acc = [predicted[i] == Y_test[i] for i in range(len(predicted))].count(1) / len(predicted)\n",
    "    print(\"the accuracy is: \",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "oN-ApUu0QS5h"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_final, Y_final, test_size=0.25, random_state=400)\n",
    "classifier = SVC(kernel = 'linear', C=0.9).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uoOygcgmQTCz",
    "outputId": "1dd1564e-9c45-4d88-8313-bc7174ebb54a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CTYoIxsuEfBQ",
    "outputId": "e7f249db-fa4c-4f59-f396-02449896d61a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix is given as: \n",
      "[[19  8]\n",
      " [ 4 19]]\n",
      "the accuracy is:  0.76\n"
     ]
    }
   ],
   "source": [
    "predicted = classifier.predict(X_test)\n",
    "my_confusion_matrix(predicted, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wnNXck09QP9r",
    "outputId": "b9202ccf-d2e8-430f-f88c-0d6711652933"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8 0.6 0.4 0.8 0.8 0.6 1.  0.6 0.8 1. ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(classifier, X_test, y_test, cv=10)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8nxFu41aQS0G",
    "outputId": "728a2e35-5e6e-4d56-8ca5-885de293a9a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 1\n",
      "0 1 1 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 1\n"
     ]
    }
   ],
   "source": [
    "print('')\n",
    "print(*predicted)\n",
    "print(*y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "uE3hfnHVEe0c"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oLIoXa-AEe7B",
    "outputId": "eac58c22-a20e-4276-9769-1a0511381007"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(C=0.9, kernel='linear'),\n",
       "             param_grid={'C': [0.5, 0.4, 0.3, 0.6, 0.7, 0.8, 0.9, 1, 5],\n",
       "                         'kernel': ['linear', 'rbf', 'sigmoid']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_values = {'C':[0.5,0.4,0.3,0.6,0.7,0.8,0.9,1,5], 'kernel':['linear', 'rbf', 'sigmoid'] }\n",
    "grid_classifier = GridSearchCV(classifier, param_grid = grid_values, scoring = 'accuracy')\n",
    "grid_classifier.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dTy_8WLAQTHL",
    "outputId": "547e9af8-caf6-4cc7-ed20-1e057749dbeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.74\n",
      "{'C': 0.6, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "print(grid_classifier.best_score_)\n",
    "print(grid_classifier.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0-muDSXAQTPk",
    "outputId": "874bdf94-6246-4a42-dff2-0cca0eef3ffa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix is given as: \n",
      "[[19  8]\n",
      " [ 1 22]]\n",
      "the accuracy is:  0.82\n"
     ]
    }
   ],
   "source": [
    "trail_classifier = SVC(kernel = 'rbf', C=0.6)\n",
    "trail_classifier.fit(X_train, y_train)\n",
    "trail_predict = trail_classifier.predict(X_test)\n",
    "my_confusion_matrix(trail_predict, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2Jsokn43QTLm",
    "outputId": "12c10df3-795e-4446-d06a-bfcf6df8be27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22  1]\n",
      " [ 8 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "matrix = confusion_matrix(y_test, trail_predict)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Got an Accuracy of 82%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_Pn_zUGwQTTv"
   },
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train, y_train)\n",
    "lda_predict = lda.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Imr3pJbJQTXo",
    "outputId": "6738faea-52d3-4def-a0f6-000d8b7c9178"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Confusion Matrix is given as: \n",
      "[[18  9]\n",
      " [ 3 20]]\n",
      "the accuracy is:  0.76\n"
     ]
    }
   ],
   "source": [
    "my_confusion_matrix(lda_predict, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9afiik_SQTba",
    "outputId": "e9255413-eb87-4507-e9a9-4e5ca8f9277f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=LinearDiscriminantAnalysis(n_components=None,\n",
       "                                                  priors=None, shrinkage=None,\n",
       "                                                  solver='svd',\n",
       "                                                  store_covariance=False,\n",
       "                                                  tol=0.0001),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'solver': ['svd', 'lsqr', 'eigen']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_values_lda = {'solver':['svd','lsqr','eigen'] }\n",
    "grid_classifier_lda = GridSearchCV(lda, param_grid = grid_values_lda, scoring = 'accuracy')\n",
    "grid_classifier_lda.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7_pmvDX2QTfK"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "mount_file_id": "1Q0YmuBfadborKoD52tu0crQ3BNJkmjtn",
   "name": "Classification_EEG",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
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