GaitCombinedForTraining.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "UB0c4YxQYVPt",
        "dLhyjh72YX6U",
        "yHRknbpCYZwF",
        "iDqr47LNRxLP",
        "eZvVXvXHcKDa",
        "4r1jLTe5cMCw",
        "2dImq6M2s9ll",
        "wVJATu_oIBjS",
        "_KxWfpqg6KAs",
        "vsQ_Ke-W6QA7",
        "kVuivAg76WMy"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BT-Y06IXRsd",
        "outputId": "d363d4c3-a6f8-40c3-aa84-100d866929e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Library"
      ],
      "metadata": {
        "id": "UB0c4YxQYVPt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import signal\n",
        "from scipy.stats import pearsonr\n",
        "from scipy.signal import find_peaks\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "%matplotlib inline\n",
        "\n",
        "font_size = 16\n",
        "# Specify the path to save containing data CSV files\n",
        "root_path = '/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait'\n",
        "# path = '/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin'\n",
        "# path2 = '/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti'\n",
        "# path3 = '/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee'\n",
        "# path4 = '/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida'\n",
        "fs = 104\n",
        "cutoff_frequency = 2  # Hz\n",
        "filter_types = ['butterworth']\n",
        "# Specify the path to save the result CSV file\n",
        "result_path = '/content/drive/MyDrive/VLSIDC/Result'"
      ],
      "metadata": {
        "id": "ITPKilsoX2VG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ],
      "metadata": {
        "id": "dLhyjh72YX6U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_lowpass_filters(data, sampling_frequency, cutoff_frequency, filter_types, filter_order=4):\n",
        "    \"\"\"\n",
        "    Apply multiple lowpass filters (including moving average) to the input data and calculate correlation coefficients.\n",
        "\n",
        "    Parameters:\n",
        "    - data (array-like): The input data to filter.\n",
        "    - sampling_frequency (float): The sampling frequency of the input data (in Hz).\n",
        "    - cutoff_frequency (float): The cutoff frequency of the lowpass filters (in Hz).\n",
        "    - filter_types (list of str): A list of filter types to use ('butterworth', 'chebyshev1', 'chebyshev2', 'moving_average').\n",
        "    - filter_order (int): The order of the filters (only applicable to certain filter types).\n",
        "\n",
        "    Returns:\n",
        "    - results (dict): A dictionary containing filtered data and correlation coefficients for each filter type.\n",
        "    \"\"\"\n",
        "    nyquist_frequency = 0.5 * sampling_frequency\n",
        "    results = {}\n",
        "\n",
        "    for filter_type in filter_types:\n",
        "        if filter_type == 'butterworth':\n",
        "            b, a = signal.butter(filter_order, cutoff_frequency / nyquist_frequency, btype='low')\n",
        "            filtered_data = signal.filtfilt(b, a, data)\n",
        "        elif filter_type == 'chebyshev1':\n",
        "            b, a = signal.cheby1(filter_order, 1, cutoff_frequency / nyquist_frequency, btype='low')\n",
        "            filtered_data = signal.filtfilt(b, a, data)\n",
        "        elif filter_type == 'chebyshev2':\n",
        "            b, a = signal.cheby2(filter_order, 30, cutoff_frequency / nyquist_frequency, btype='low')\n",
        "            filtered_data = signal.filtfilt(b, a, data)\n",
        "        elif filter_type == 'moving_average':\n",
        "            filter_window = int(sampling_frequency / cutoff_frequency)  # Window size for moving average\n",
        "            filtered_data = np.convolve(data, np.ones(filter_window) / filter_window, mode='same')\n",
        "        else:\n",
        "            raise ValueError(\"Invalid filter_type. Supported types: 'butterworth', 'chebyshev1', 'chebyshev2', 'moving_average'\")\n",
        "\n",
        "        correlation_coefficient, _ = pearsonr(data, filtered_data)\n",
        "\n",
        "        results[filter_type] = {\n",
        "            'filtered_data': filtered_data,\n",
        "            'correlation_coefficient': correlation_coefficient\n",
        "        }\n",
        "\n",
        "    return results"
      ],
      "metadata": {
        "id": "FQV0Xn1eYZh9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Read"
      ],
      "metadata": {
        "id": "yHRknbpCYZwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "csv_files = []\n",
        "# Iterate through each folder in the root directory\n",
        "for folder_name in os.listdir(root_path):\n",
        "    folder_path = os.path.join(root_path, folder_name)\n",
        "\n",
        "    # Check if the item in the root directory is a folder\n",
        "    if os.path.isdir(folder_path):\n",
        "        # Iterate through each file in the folder\n",
        "        for file_name in os.listdir(folder_path):\n",
        "            file_path = os.path.join(folder_path, file_name)\n",
        "\n",
        "            # Check if the file has a .csv extension\n",
        "            if file_name.endswith('.csv') and os.path.isfile(file_path):\n",
        "                # Append the full file path to the csv_files list\n",
        "                csv_files.append(file_path)\n",
        "\n",
        "# Print the list of full file paths for CSV files\n",
        "for csv_file in csv_files:\n",
        "  print(csv_file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8NV35g1o2DD-",
        "outputId": "b3c85dbd-2be3-4789-e630-1e6dc8ca234a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data01.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data03.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data04.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data05.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data02.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data06.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data07.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data08.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data09.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data10.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data11.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data12.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data15.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data13.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Kartikee/data14.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data01.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data02.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data03.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data04.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data06.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data07.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data05.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data08.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data09.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data10.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data11.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data12.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data13.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data14.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Moin/data15.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data08.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data01.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data02.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data03.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data10.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data05.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data04.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data07.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data06.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data09.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data11.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data12.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data13.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data14.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Avanti/data15.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data04.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data01.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data05.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data03.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data02.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data06.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data07.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data08.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data09.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data10.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data12.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data11.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data13.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data14.csv\n",
            "/content/drive/MyDrive/VLSIDC/VLSIDesignContestDataset/healthy gait/Rufaida/data15.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# csv_files = [file for file in os.listdir(path) if file.endswith('.csv')]\n",
        "dataframes = []\n",
        "# Loop through each CSV file and read it into a separate dataframe\n",
        "for csv_file in csv_files:\n",
        "  print(csv_file)\n",
        "  # # Construct the full file path\n",
        "  # file_path = os.path.join(path, csv_file)\n",
        "\n",
        "  # Read the CSV file into a dataframe\n",
        "  data = pd.read_csv(csv_file)\n",
        "  # # Store the dataframe in the dictionary with the file name as the key\n",
        "  # dataframes[csv_file] = data\n",
        "\n",
        "  HeelADCRaw = data['Heel ADC0']\n",
        "  MidFootADCRaw = data['MidFoot ADC1']\n",
        "  ToeADCRaw = data['Toe ADC2']\n",
        "\n",
        "  GyroXRaw = data[' GyroX (deg/s)']\n",
        "  GyroXRaw = GyroXRaw - np.min(GyroXRaw)\n",
        "  GyroYRaw = data[' GyroY (deg/s)']\n",
        "  GyroYRaw = GyroYRaw - np.min(GyroYRaw)\n",
        "  GyroZRaw = data[' GyroZ (deg/s)']\n",
        "  GyroZRaw = GyroZRaw - np.min(GyroZRaw)\n",
        "\n",
        "  AccXRaw = data[' AccX (g)']\n",
        "  AccYRaw = data[' AccY (g)']\n",
        "  AccZRaw = data[' AccZ (g)']\n",
        "\n",
        "  start, end = 0, len(GyroZRaw)\n",
        "  GyroXRawSeg = GyroXRaw[start: end]\n",
        "  GyroYRawSeg = GyroYRaw[start: end]\n",
        "  GyroZRawSeg = GyroZRaw[start: end]\n",
        "\n",
        "  results1 = apply_lowpass_filters(AccXRaw, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    AccXFiltSeg = result['filtered_data']\n",
        "  results1 = apply_lowpass_filters(AccYRaw, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    AccYFiltSeg = result['filtered_data']\n",
        "  results1 = apply_lowpass_filters(AccZRaw, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    AccZFiltSeg = result['filtered_data']\n",
        "\n",
        "  results1 = apply_lowpass_filters(GyroXRawSeg, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    GyroXFiltSeg = result['filtered_data']\n",
        "  results1 = apply_lowpass_filters(GyroYRawSeg, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    GyroYFiltSeg = result['filtered_data']\n",
        "  results1 = apply_lowpass_filters(GyroZRawSeg, fs, cutoff_frequency, filter_types, 2)\n",
        "  for filter_type, result in results1.items():\n",
        "    GyroZFiltSeg = result['filtered_data']\n",
        "\n",
        "  # plt.figure(figsize=(20, 10))\n",
        "  # plt.subplot(211)\n",
        "  # plt.title('Filtered Signal', fontsize = font_size)\n",
        "  # plt.plot(GyroZRawSeg, label='Raw Data', alpha=0.6, linewidth=2)\n",
        "  # plt.ylabel('Amplitude', fontsize = font_size)\n",
        "  # plt.legend(fontsize = font_size)\n",
        "  # plt.grid(True)\n",
        "  # plt.tight_layout()\n",
        "  # plt.subplot(212)\n",
        "  # plt.plot(GyroZFiltSeg, label='Filtered Data', alpha=0.6, linewidth=2)\n",
        "  # plt.xlabel('Samples', fontsize = font_size)\n",
        "  # plt.ylabel('Amplitude', fontsize = font_size)\n",
        "  # plt.legend(fontsize = font_size)\n",
        "  # plt.grid(True)\n",
        "  # plt.tight_layout()\n",
        "  # plt.show()\n",
        "\n",
        "  ## Find max of the given window\n",
        "  max_window = max(GyroZFiltSeg)\n",
        "  # print(max_window)\n",
        "\n",
        "  ## Find the lower threshold based on max found\n",
        "  thr = 0.32\n",
        "  low_thr = int(max_window - (0.32 * max_window))\n",
        "  # print(max_window, low_thr)\n",
        "\n",
        "  ## Begin MSw Finding based on Max peak detetion and locallized peak detection\n",
        "  ## Find the local peaks based on lower threshold\n",
        "  GyroZPksPos, _ = find_peaks(GyroZFiltSeg, height=low_thr, distance=fs//1)\n",
        "  GyroZPksVal = GyroZFiltSeg[GyroZPksPos]\n",
        "  # print(GyroZPksVal, GyroZPksPos)\n",
        "\n",
        "  # plt.figure(figsize=(20, 5))\n",
        "  # plt.title('Peak Finding [Major Swing]', fontsize = font_size)\n",
        "  # plt.plot(GyroZFiltSeg, label='Filtered Data', alpha=0.6, linewidth=2)\n",
        "  # plt.plot(GyroZPksPos, GyroZFiltSeg[GyroZPksPos], \"x\", label='MSw')\n",
        "  # plt.xlabel('Samples', fontsize = font_size)\n",
        "  # plt.ylabel('Amplitude', fontsize = font_size)\n",
        "  # plt.legend(fontsize = font_size)\n",
        "  # plt.grid(True)\n",
        "  # plt.tight_layout()\n",
        "  # plt.show()\n",
        "\n",
        "  ## Begin finding the TO (towards Left of MSw) and IC (towards Right of MSw)\n",
        "  GyroZPksPosLeftMin, GyroZPksPosRightMin = [], []\n",
        "  for i in range(len(GyroZPksPos)):\n",
        "    if (i == 0):\n",
        "      low, high = (GyroZPksPos[i]), (GyroZPksPos[i]+50)\n",
        "      b = GyroZPksPos[i] + np.argmin(GyroZFiltSeg[low : high])\n",
        "      #print(b, GyroZPksPos[i])\n",
        "      GyroZPksPosRightMin.append(b)\n",
        "    else:\n",
        "      #print(i)\n",
        "      low, high = (GyroZPksPos[i]-50), (GyroZPksPos[i])\n",
        "      a = GyroZPksPos[i] - 50 + np.argmin(GyroZFiltSeg[low : high])\n",
        "      #print(a, GyroZPksPos[i])\n",
        "      GyroZPksPosLeftMin.append(a)\n",
        "\n",
        "      if ((GyroZPksPos[i]+50) > len(GyroZFiltSeg)):\n",
        "        continue\n",
        "      else:\n",
        "        low, high = (GyroZPksPos[i]), (GyroZPksPos[i]+50)\n",
        "        b = GyroZPksPos[i] + np.argmin(GyroZFiltSeg[low : high])\n",
        "        #print(b, GyroZPksPos[i])\n",
        "        GyroZPksPosRightMin.append(b)\n",
        "\n",
        "  # plt.figure(figsize=(20, 5))\n",
        "  # plt.title('Peak Finding [Major-Swing (MSw), Toe-Off (TO), Initial Contact (IC)]', fontsize = font_size)\n",
        "  # plt.plot(GyroZFiltSeg, label='Filtered Data', alpha=0.6, linewidth=2)\n",
        "  # plt.plot(GyroZPksPos, GyroZFiltSeg[GyroZPksPos], \"x\", label='MSw')\n",
        "  # plt.plot(GyroZPksPosLeftMin, GyroZFiltSeg[GyroZPksPosLeftMin], \"x\", label='TO')\n",
        "  # plt.plot(GyroZPksPosRightMin, GyroZFiltSeg[GyroZPksPosRightMin], \"x\", label='IC')\n",
        "  # plt.xlabel('Samples', fontsize = font_size)\n",
        "  # plt.ylabel('Amplitude', fontsize = font_size)\n",
        "  # plt.legend(fontsize = font_size)\n",
        "  # plt.grid(True)\n",
        "  # plt.tight_layout()\n",
        "  # plt.show()\n",
        "\n",
        "  ## Begin Finding MSt (Between IC and TO)\n",
        "  if (GyroZPksPosLeftMin > GyroZPksPosRightMin):\n",
        "    loop_len = len(GyroZPksPosLeftMin)\n",
        "  else:\n",
        "    loop_len = len(GyroZPksPosRightMin)\n",
        "\n",
        "  GyroZPksPosMSt = []\n",
        "  for i in range(loop_len):\n",
        "    low, high = (GyroZPksPosRightMin[i]), (GyroZPksPosLeftMin[i])\n",
        "    a = GyroZPksPosRightMin[i] + np.argmax(GyroZFiltSeg[low : high])\n",
        "    #print(a, GyroZPksPos[i])\n",
        "    GyroZPksPosMSt.append(a)\n",
        "\n",
        "  # plt.figure(figsize=(20, 5))\n",
        "  # plt.title('Peak Finding [Major-Swing (MSw), Toe-Off (TO), Initial Contact (IC), Major-Stance (MSt)]', fontsize = font_size)\n",
        "  # plt.plot(GyroZFiltSeg, label='Filtered Data', alpha=0.6, linewidth=2)\n",
        "  # plt.plot(GyroZPksPos, GyroZFiltSeg[GyroZPksPos], \"x\", label='MSw')\n",
        "  # plt.plot(GyroZPksPosLeftMin, GyroZFiltSeg[GyroZPksPosLeftMin], \"x\", label='TO')\n",
        "  # plt.plot(GyroZPksPosRightMin, GyroZFiltSeg[GyroZPksPosRightMin], \"x\", label='IC')\n",
        "  # plt.plot(GyroZPksPosMSt, GyroZFiltSeg[GyroZPksPosMSt], \"x\", label='MSt')\n",
        "  # plt.xlabel('Samples', fontsize = font_size)\n",
        "  # plt.ylabel('Amplitude', fontsize = font_size)\n",
        "  # plt.legend(fontsize = font_size)\n",
        "  # plt.grid(True)\n",
        "  # plt.tight_layout()\n",
        "  # plt.show()\n",
        "\n",
        "  # MSw = Orange; IC = Red; MSt = Purple; TO = Green\n",
        "  # label ->\n",
        "  # 0 = IC to MSt\n",
        "  # 1 = MSt to TO\n",
        "  # 2 = TO to MSw\n",
        "  # 3 = MSw to IC\n",
        "  # 4 = N/A\n",
        "\n",
        "  label = []\n",
        "  for n in range(0, GyroZPksPos[0]):\n",
        "    label.append(4)\n",
        "\n",
        "  for idx in range(len(GyroZPksPos)-1):\n",
        "    for m in range(GyroZPksPos[idx], GyroZPksPosRightMin[idx]):\n",
        "      label.append(3)\n",
        "    for j in range(GyroZPksPosRightMin[idx], GyroZPksPosMSt[idx]):\n",
        "      label.append(0)\n",
        "    for k in range(GyroZPksPosMSt[idx], GyroZPksPosLeftMin[idx]):\n",
        "      label.append(1)\n",
        "    for i in range(GyroZPksPosLeftMin[idx], GyroZPksPos[idx+1]):\n",
        "      label.append(2)\n",
        "    # print(GyroZPksPos[idx], GyroZPksPosRightMin[idx], GyroZPksPosMSt[idx], GyroZPksPosLeftMin[idx])\n",
        "\n",
        "  for o in range(GyroZPksPos[len(GyroZPksPos)-1], len(GyroZFiltSeg)):\n",
        "    label.append(4)\n",
        "\n",
        "  # labels = tf.constant(label)\n",
        "  # num_classes = tf.reduce_max(labels) + 1\n",
        "  # one_hot_encoded = tf.one_hot(labels, depth=num_classes)\n",
        "  # for i in one_hot_encoded:\n",
        "  #   print(i)\n",
        "\n",
        "  # for i in range(len(label)):\n",
        "  #   label[i] = label[i]*100 + int(np.mean(GyroZFiltSeg))\n",
        "\n",
        "  fig, ax1 = plt.subplots(figsize=(20, 5))\n",
        "  plt.title('Peak Finding [Major-Swing (MSw), Toe-Off (TO), Initial Contact (IC), Major-Stance (MSt)]', fontsize=font_size)\n",
        "  # Plotting on the first y-axis\n",
        "  ax1.plot(GyroZFiltSeg, label='Filtered Data', alpha=0.6, linewidth=2)\n",
        "  ax1.plot(GyroZPksPos, GyroZFiltSeg[GyroZPksPos], \"x\", label='MSw')\n",
        "  ax1.plot(GyroZPksPosLeftMin, GyroZFiltSeg[GyroZPksPosLeftMin], \"x\", label='TO')\n",
        "  ax1.plot(GyroZPksPosRightMin, GyroZFiltSeg[GyroZPksPosRightMin], \"x\", label='IC')\n",
        "  ax1.plot(GyroZPksPosMSt, GyroZFiltSeg[GyroZPksPosMSt], \"x\", label='MSt')\n",
        "  ax1.set_xlabel('Samples', fontsize=font_size)\n",
        "  ax1.set_ylabel('Amplitude', fontsize=font_size)\n",
        "  ax1.legend(loc='upper left', fontsize=font_size)\n",
        "  # Creating a second y-axis\n",
        "  ax2 = ax1.twinx()\n",
        "  ax2.plot(label, \"--\", label='Label', color='orange')\n",
        "  ax2.set_ylabel('Label', fontsize=font_size)\n",
        "  # Manually setting tick labels\n",
        "  tick_labels = ['Heel Strike', 'Mid-Stance', 'Toe-Off', 'Mid Swing', 'N/A']\n",
        "  ax2.set_yticks([0, 1, 2, 3, 4])\n",
        "  ax2.set_yticklabels(tick_labels, fontsize=font_size)\n",
        "  ax2.legend(loc='upper right', fontsize=font_size)\n",
        "  plt.grid(True)\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "\n",
        "  # Create a dataframe with the specified columns\n",
        "  df = pd.DataFrame({\n",
        "        'AccX (g)': AccXFiltSeg,\n",
        "        'AccY (g)': AccYFiltSeg,\n",
        "        'AccZ (g)': AccZFiltSeg,\n",
        "        'GyroX (deg/s)': GyroXFiltSeg,\n",
        "        'GyroY (deg/s)': GyroYFiltSeg,\n",
        "        'GyroZ (deg/s)': GyroZFiltSeg,\n",
        "        'Heel ADC0': HeelADCRaw,\n",
        "        'MidFoot ADC1': MidFootADCRaw,\n",
        "        'Toe ADC2': ToeADCRaw,\n",
        "        'LABEL': label\n",
        "    })\n",
        "\n",
        "  df = df[df['LABEL'] != 4]\n",
        "  # Perform one-hot encoding on the 'LABEL' column\n",
        "  df = pd.get_dummies(df, columns=['LABEL'], prefix='label')\n",
        "  # df['LABEL'].value_counts().plot(kind='bar') #Plot freuqncy of class\n",
        "\n",
        "  # print(df.head())\n",
        "\n",
        "  dataframes.append(df)\n"
      ],
      "metadata": {
        "id": "X3_QkfvpXSnO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcadc54c-3f3c-4a7a-c11e-850fdb2a0ac2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save Labelled data into CSV file"
      ],
      "metadata": {
        "id": "iDqr47LNRxLP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # This code will store the result in same columns.\n",
        "# # Ensure the result directory exists\n",
        "# os.makedirs(result_path, exist_ok=True)\n",
        "\n",
        "# # Concatenate all dataframes in the list\n",
        "# result_df = pd.concat(dataframes, ignore_index=True)\n",
        "\n",
        "# # Save the concatenated dataframe to a CSV file\n",
        "# result_file_path = os.path.join(result_path, 'result.csv')\n",
        "# result_df.to_csv(result_file_path, index=False)\n",
        "\n",
        "# print(f\"Result CSV file saved at: {result_file_path}\")"
      ],
      "metadata": {
        "id": "aBcGDsIscZ7s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # This code will store the result in different columns.\n",
        "# # Set the path to save the result CSV file\n",
        "# result_file = 'result.csv'\n",
        "\n",
        "# # Create the result directory if it doesn't exist\n",
        "# if not os.path.exists(result_path):\n",
        "#     os.makedirs(result_path)\n",
        "\n",
        "# # Assuming 'dataframes' is the list of dataframes from the previous code\n",
        "# # Create a dictionary to store dataframes as columns\n",
        "# df_dict = {'df{}'.format(i): df for i, df in enumerate(dataframes)}\n",
        "\n",
        "# # Create a single dataframe with columns as dataframes\n",
        "# result_df = pd.concat(df_dict, axis=1)\n",
        "\n",
        "# # Save the result dataframe to a CSV file\n",
        "# result_df.to_csv(os.path.join(result_path, result_file), index=False)"
      ],
      "metadata": {
        "id": "NfZTik6Rc1rs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This code will store the result in different CSV files.\n",
        "\n",
        "# Create the result directory if it doesn't exist\n",
        "if not os.path.exists(result_path):\n",
        "    os.makedirs(result_path)\n",
        "else:\n",
        "    # Delete all files in the result_path directory\n",
        "    files_to_delete = os.listdir(result_path)\n",
        "    for file_to_delete in files_to_delete:\n",
        "        file_path = os.path.join(result_path, file_to_delete)\n",
        "        os.remove(file_path)\n",
        "\n",
        "# Save each dataframe to a separate CSV file\n",
        "for i, df in enumerate(dataframes):\n",
        "    csv_file = f'df_{i}.csv'\n",
        "    csv_path = os.path.join(result_path, csv_file)\n",
        "    df.to_csv(csv_path, index=False)\n"
      ],
      "metadata": {
        "id": "_fM3qmvCTcEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning Section"
      ],
      "metadata": {
        "id": "ptvEBc_QR3uQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.version.VERSION)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAW7NxJvRtnY",
        "outputId": "7bdcf686-05c1-4e98-ff2c-f68b4a7d6fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the dataset"
      ],
      "metadata": {
        "id": "eZvVXvXHcKDa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_csv_files = [file for file in os.listdir(result_path) if file.endswith('.csv')]\n",
        "print(new_csv_files)\n",
        "\n",
        "for i, csv_file in enumerate(new_csv_files):\n",
        "    file_path = os.path.join(result_path, csv_file)\n",
        "    df_data = pd.read_csv(file_path)\n",
        "    if (i == 0):\n",
        "      df_data_append = df_data\n",
        "    else:\n",
        "      df_data_append = pd.concat([df_data_append, df_data], ignore_index=True)\n",
        "\n",
        "recordings = df_data_append.to_numpy()\n",
        "print(\"recordings shape:\", recordings.shape)"
      ],
      "metadata": {
        "id": "mkrmtMsITIkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "412730d2-7eaa-4fae-8721-e2a26510c6dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['df_0.csv', 'df_54.csv', 'df_44.csv', 'df_53.csv', 'df_52.csv', 'df_1.csv', 'df_55.csv', 'df_2.csv', 'df_3.csv', 'df_5.csv', 'df_6.csv', 'df_4.csv', 'df_7.csv', 'df_8.csv', 'df_10.csv', 'df_9.csv', 'df_14.csv', 'df_11.csv', 'df_12.csv', 'df_13.csv', 'df_17.csv', 'df_16.csv', 'df_15.csv', 'df_18.csv', 'df_19.csv', 'df_20.csv', 'df_21.csv', 'df_24.csv', 'df_25.csv', 'df_23.csv', 'df_22.csv', 'df_26.csv', 'df_27.csv', 'df_28.csv', 'df_29.csv', 'df_30.csv', 'df_33.csv', 'df_34.csv', 'df_31.csv', 'df_35.csv', 'df_36.csv', 'df_32.csv', 'df_38.csv', 'df_37.csv', 'df_39.csv', 'df_40.csv', 'df_41.csv', 'df_43.csv', 'df_42.csv', 'df_45.csv', 'df_48.csv', 'df_46.csv', 'df_47.csv', 'df_50.csv', 'df_49.csv', 'df_51.csv', 'df_59.csv', 'df_57.csv', 'df_58.csv', 'df_56.csv']\n",
            "recordings shape: (284513, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(recordings[:, -4:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtyozXZis7eL",
        "outputId": "4c234e9e-6a12-4d58-df57-51c7ef1bd15d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 1.]\n",
            " [0. 0. 0. 1.]\n",
            " [0. 0. 0. 1.]\n",
            " ...\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 1. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the 'all_frames' array into 'x_frames' and 'y_frames'\n",
        "x_recording = recordings[:, :-4]  # Take the first 9 columns for each frame\n",
        "y_recording = recordings[:, -4:]   # Take the last column for each frame\n",
        "\n",
        "print(\"x_recording shape:\", x_recording.shape)\n",
        "print(\"y_recording shape:\", y_recording.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDsH5Vfbcf0o",
        "outputId": "4498cefc-360e-4183-a64b-d1dcd1eabc41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_recording shape: (284513, 9)\n",
            "y_recording shape: (284513, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Frame data"
      ],
      "metadata": {
        "id": "4r1jLTe5cMCw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def frame(x, frame_len, hop_len):\n",
        "    ''' Slice a 3D data array into (overlapping) frames. '''\n",
        "\n",
        "    assert(x.shape == (len(x), x.shape[1]))\n",
        "    assert(x.shape[0] >= frame_len)\n",
        "    assert(hop_len >= 1)\n",
        "\n",
        "    n_frames = 1 + (x.shape[0] - frame_len) // hop_len\n",
        "    shape = (n_frames, frame_len, x.shape[1])\n",
        "    strides_x = ((hop_len * x.strides[0],) + x.strides)\n",
        "\n",
        "    return np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides_x)\n",
        "\n",
        "# all_frames = frame(recordings, 26, 26) # no overlap\n",
        "x_frames = frame(x_recording, 25, 7) # 50% overlap\n",
        "x_frames = np.array(x_frames)\n",
        "print(x_frames.shape)\n",
        "y_frames = frame(y_recording, 25, 7) # 50% overlap\n",
        "y_frames = np.array(y_frames)\n",
        "print(y_frames.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQKavsmQWnla",
        "outputId": "15e90d1e-deac-4f1e-e02a-034f6de84798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(40642, 25, 9)\n",
            "(40642, 25, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing the dataset"
      ],
      "metadata": {
        "id": "Ri5xZKofs-0u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Normalize x_frames between [-1;1]\n",
        "# min_val = np.min(x_frames)\n",
        "# max_val = np.max(x_frames)\n",
        "# x_frames_normed = -1 + 2 * (x_frames - min_val) / (max_val - min_val)\n",
        "# print(\"Normalized x_frame shape:\", x_frames_normed.shape)\n",
        "\n",
        "# Normalize x_frames between [-1, 1] - for every col\n",
        "min_val = np.min(x_frames, axis=(0, 1))  # Calculate minimum along axes 0 and 1\n",
        "max_val = np.max(x_frames, axis=(0, 1))  # Calculate maximum along axes 0 and 1\n",
        "x_frames_normed = -1 + 2 * (x_frames - min_val) / (max_val - min_val)\n",
        "print(\"Original x_frames shape:\", x_frames.shape)\n",
        "print(\"Normalized x_frames shape:\", x_frames_normed.shape)\n",
        "# x_frames_normed[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyNr2HL8tCMF",
        "outputId": "b6f0d465-3236-402e-a4b6-f2103dd0de2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original x_frames shape: (40642, 25, 9)\n",
            "Normalized x_frames shape: (40642, 25, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "appen_label = []\n",
        "for j in range(0, len(y_frames)):\n",
        "  appen_label.append(np.argmax(np.sum(y_frames[j], axis=0)))\n",
        "  # print(y_frames[j], '\\n', np.argmax(np.sum(y_frames[j], axis=0)), '\\n')\n",
        "  # # print(y_frames[j], '\\n', np.argmax(np.bincount(np.argmax(y_frames[j], axis=1))), '\\n')\n",
        "y_frames = np.array(appen_label)\n",
        "# print(\"x_frames shape:\", x_frames.shape)\n",
        "# print(\"y_frames shape:\", y_frames.shape)"
      ],
      "metadata": {
        "id": "rF7THs3QzlTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the one-hot encoded array\n",
        "print(\"Original array:\")\n",
        "print(y_frames)\n",
        "# Perform one-hot encoding\n",
        "y_frames = tf.keras.utils.to_categorical(y_frames, num_classes=4)\n",
        "print(\"\\nOne-hot encoded array:\")\n",
        "print(y_frames)\n",
        "\n",
        "print(\"x_frames shape:\", x_frames.shape)\n",
        "print(\"y_frames shape:\", y_frames.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eh1dumGh-ycZ",
        "outputId": "3158852b-63d2-4479-9315-853df42f1ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original array:\n",
            "[3 3 3 ... 2 2 2]\n",
            "\n",
            "One-hot encoded array:\n",
            "[[0. 0. 0. 1.]\n",
            " [0. 0. 0. 1.]\n",
            " [0. 0. 0. 1.]\n",
            " ...\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 1. 0.]]\n",
            "x_frames shape: (40642, 25, 9)\n",
            "y_frames shape: (40642, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing the dataset (train, test, split)"
      ],
      "metadata": {
        "id": "2dImq6M2s9ll"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming x_data and y_data are your features and labels\n",
        "x_train_temp, x_test, y_train_temp, y_test = train_test_split(x_frames_normed, y_frames, test_size=0.2, random_state=42)\n",
        "\n",
        "# Split the temporary set into training and validation sets\n",
        "x_train, x_val, y_train, y_val = train_test_split(x_train_temp, y_train_temp, test_size=0.25, random_state=42)\n",
        "\n",
        "# Display the shapes of the resulting sets\n",
        "print(\"Training set shapes:\", x_train.shape, y_train.shape)\n",
        "print(\"Validation set shapes:\", x_val.shape, y_val.shape)\n",
        "print(\"Test set shapes:\", x_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TulnT_imsPUG",
        "outputId": "10a1e628-0388-4de0-c094-0be4f10b081c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shapes: (24384, 25, 9) (24384, 4)\n",
            "Validation set shapes: (8129, 25, 9) (8129, 4)\n",
            "Test set shapes: (8129, 25, 9) (8129, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVJATu_oIBjS"
      },
      "source": [
        "## Creating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AbNvrqUeIavW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a28fe3bd-c9de-49a7-a90a-5d1a591d5f21"
      },
      "source": [
        "## Conv1D based model\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=8, kernel_size=5, activation='relu', input_shape=(25, 9)),\n",
        "  tf.keras.layers.Conv1D(filters=16, kernel_size=3, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.1), #0.5 too large\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'),\n",
        "  tf.keras.layers.MaxPooling1D(pool_size=2), # maxpool\n",
        "\n",
        "  tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'),\n",
        "  tf.keras.layers.MaxPooling1D(pool_size=2), # maxpool\n",
        "\n",
        "  tf.keras.layers.Flatten(),\n",
        "  # tf.keras.layers.Dense(64, activation='relu'),\n",
        "  tf.keras.layers.Dense(4, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.fit(x_train, y_train, epochs=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "762/762 [==============================] - 5s 5ms/step - loss: 0.3152 - accuracy: 0.8723\n",
            "Epoch 2/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.1787 - accuracy: 0.9259\n",
            "Epoch 3/30\n",
            "762/762 [==============================] - 6s 8ms/step - loss: 0.1534 - accuracy: 0.9348\n",
            "Epoch 4/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.1371 - accuracy: 0.9414\n",
            "Epoch 5/30\n",
            "762/762 [==============================] - 3s 5ms/step - loss: 0.1249 - accuracy: 0.9473\n",
            "Epoch 6/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.1179 - accuracy: 0.9494\n",
            "Epoch 7/30\n",
            "762/762 [==============================] - 6s 8ms/step - loss: 0.1097 - accuracy: 0.9535\n",
            "Epoch 8/30\n",
            "762/762 [==============================] - 5s 7ms/step - loss: 0.1078 - accuracy: 0.9548\n",
            "Epoch 9/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.1020 - accuracy: 0.9572\n",
            "Epoch 10/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.0984 - accuracy: 0.9590\n",
            "Epoch 11/30\n",
            "762/762 [==============================] - 6s 7ms/step - loss: 0.0953 - accuracy: 0.9609\n",
            "Epoch 12/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.0909 - accuracy: 0.9623\n",
            "Epoch 13/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0950 - accuracy: 0.9611\n",
            "Epoch 14/30\n",
            "762/762 [==============================] - 3s 4ms/step - loss: 0.0899 - accuracy: 0.9631\n",
            "Epoch 15/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.0855 - accuracy: 0.9652\n",
            "Epoch 16/30\n",
            "762/762 [==============================] - 5s 7ms/step - loss: 0.0853 - accuracy: 0.9651\n",
            "Epoch 17/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0844 - accuracy: 0.9652\n",
            "Epoch 18/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0807 - accuracy: 0.9679\n",
            "Epoch 19/30\n",
            "762/762 [==============================] - 5s 7ms/step - loss: 0.0827 - accuracy: 0.9665\n",
            "Epoch 20/30\n",
            "762/762 [==============================] - 6s 8ms/step - loss: 0.0789 - accuracy: 0.9681\n",
            "Epoch 21/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0813 - accuracy: 0.9664\n",
            "Epoch 22/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0775 - accuracy: 0.9682\n",
            "Epoch 23/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0754 - accuracy: 0.9684\n",
            "Epoch 24/30\n",
            "762/762 [==============================] - 5s 7ms/step - loss: 0.0758 - accuracy: 0.9694\n",
            "Epoch 25/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.0757 - accuracy: 0.9687\n",
            "Epoch 26/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0720 - accuracy: 0.9717\n",
            "Epoch 27/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0757 - accuracy: 0.9692\n",
            "Epoch 28/30\n",
            "762/762 [==============================] - 5s 6ms/step - loss: 0.0746 - accuracy: 0.9701\n",
            "Epoch 29/30\n",
            "762/762 [==============================] - 6s 7ms/step - loss: 0.0747 - accuracy: 0.9699\n",
            "Epoch 30/30\n",
            "762/762 [==============================] - 4s 5ms/step - loss: 0.0716 - accuracy: 0.9697\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1b550b0af0>"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate Model for Test and Validation data"
      ],
      "metadata": {
        "id": "_KxWfpqg6KAs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)\n",
        "\n",
        "print(\"Test loss:\", test_loss)\n",
        "print(\"Test acc:\", test_acc)\n",
        "print()\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "3___g5CdvPiW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a1fe3a9-7057-4b02-c3ee-b672d9e0f119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255/255 - 2s - loss: 0.0632 - accuracy: 0.9738 - 2s/epoch - 7ms/step\n",
            "Test loss: 0.06319670379161835\n",
            "Test acc: 0.9737975001335144\n",
            "\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_27 (Conv1D)          (None, 21, 8)             368       \n",
            "                                                                 \n",
            " conv1d_28 (Conv1D)          (None, 19, 16)            400       \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 19, 16)            0         \n",
            "                                                                 \n",
            " conv1d_29 (Conv1D)          (None, 17, 32)            1568      \n",
            "                                                                 \n",
            " max_pooling1d_13 (MaxPoolin  (None, 8, 32)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_30 (Conv1D)          (None, 6, 64)             6208      \n",
            "                                                                 \n",
            " max_pooling1d_14 (MaxPoolin  (None, 3, 64)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 192)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 4)                 772       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9,316\n",
            "Trainable params: 9,316\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_loss, val_acc = model.evaluate(x_val,  y_val, verbose=2)\n",
        "\n",
        "print(\"Test loss:\", val_loss)\n",
        "print(\"Test acc:\", val_acc)\n",
        "print()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEfxxM4WBoBf",
        "outputId": "ceccafb5-4125-476f-db20-72628d13d387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255/255 - 1s - loss: 0.0682 - accuracy: 0.9720 - 861ms/epoch - 3ms/step\n",
            "Test loss: 0.06820133328437805\n",
            "Test acc: 0.9719522595405579\n",
            "\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_27 (Conv1D)          (None, 21, 8)             368       \n",
            "                                                                 \n",
            " conv1d_28 (Conv1D)          (None, 19, 16)            400       \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 19, 16)            0         \n",
            "                                                                 \n",
            " conv1d_29 (Conv1D)          (None, 17, 32)            1568      \n",
            "                                                                 \n",
            " max_pooling1d_13 (MaxPoolin  (None, 8, 32)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_30 (Conv1D)          (None, 6, 64)             6208      \n",
            "                                                                 \n",
            " max_pooling1d_14 (MaxPoolin  (None, 3, 64)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 192)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 4)                 772       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9,316\n",
            "Trainable params: 9,316\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicted Data"
      ],
      "metadata": {
        "id": "vsQ_Ke-W6QA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "DA0aTrYhuW-d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16fa3146-a494-4649-b35d-e836d258f9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255/255 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(Y_pred)):\n",
        "  for j in range(len(Y_pred[i])):\n",
        "    if (Y_pred[i][j] < 0.5):\n",
        "      Y_pred[i][j] = 0\n",
        "    else:\n",
        "      Y_pred[i][j] = 1\n",
        "\n",
        "# Retrieve the original labels for each row\n",
        "original_labels = np.argmax(Y_pred, axis=1)\n",
        "true_labels = np.argmax(y_test, axis=1)\n",
        "# print(\"Original Labels:\", original_labels)\n",
        "\n",
        "# for i in range(len(Y_pred)):\n",
        "#   print(original_labels[i], Y_pred[i], y_test[i])\n",
        "\n",
        "# k=3\n",
        "# plt.plot(original_labels[k: k+100], '.-', label='Predited Label')\n",
        "# plt.plot(original_labels[k: k+100], '.-', label='True Label')\n",
        "# plt.legend()\n",
        "\n",
        "labels = ['Initial Contact', 'Major Stance', 'Toe-Off', 'Major Swing']\n",
        "confusion_matrix = tf.math.confusion_matrix(true_labels, original_labels)\n",
        "plt.figure()\n",
        "sns.heatmap(confusion_matrix,\n",
        "            annot=True,\n",
        "            xticklabels=labels,\n",
        "            yticklabels=labels,\n",
        "            cmap=plt.cm.Blues,\n",
        "            fmt='d', cbar=False)\n",
        "plt.tight_layout()\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "48l9YaNs5vJv",
        "outputId": "99e5f837-9411-4819-a1f3-32db2a031c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Export the model"
      ],
      "metadata": {
        "id": "kVuivAg76WMy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model into an HDF5 file ‘model.h5’\n",
        "model.save('/content/drive/MyDrive/VLSIDC/Model/model.h5')"
      ],
      "metadata": {
        "id": "XnrBR1OEu76f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}