1D_CNN_3_Layer.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vipkas/AIoT-ElderyFallDetection/blob/main/1D_CNN_3_Layer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMfLgupbvYtH",
        "outputId": "cbbc2a6c-db9d-468c-bb5f-79ed608c671e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder"
      ],
      "metadata": {
        "id": "a-kl4DZXv5CS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pd.read_csv('/content/drive/MyDrive/WISDM_ar_latest/WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt')"
      ],
      "metadata": {
        "id": "GWn5UUkrCuCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "IbkXvZvQke3p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "feba9b6c-0a1d-494e-cf10-c4d704733a17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "directory = os.getcwd()+\"/drive/MyDrive/coba/\"\n",
        "single_filename = \"single_file_kfalldataset_3labels.csv\"\n",
        "\n",
        "#Complementary Filter with acc*acc_sen and with 3 label class\n",
        "path = os.path.join(directory,single_filename)\n",
        "df_chunk = pd.read_csv(path,chunksize=1000000)\n",
        "df_list= []\n",
        "\n",
        "for chunk in df_chunk:\n",
        "  df_list.append(chunk)\n",
        "  df = pd.concat(df_list,axis=0)\n",
        "\n",
        "df"
      ],
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          "height": 488
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        "outputId": "ea379d27-700e-496e-a1e3-010133c3b185"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <th>1</th>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 136
        }
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        "columns = ['Timestamp', 'Ax', 'Ay', 'Az', 'Gx', 'Gy', 'Gz', 'Condition']"
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      "metadata": {
        "id": "pBggcS_ozuMc"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(data = df, columns = columns)\n",
        "data.head()"
      ],
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          "height": 250
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              "   Timestamp     Ax     Ay     Az         Gx         Gy        Gz  Condition\n",
              "0       0.01 -0.250 -0.809 -0.188 -35.122325  14.954204  4.755551          0\n",
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              "      <th>Timestamp</th>\n",
              "      <th>Ax</th>\n",
              "      <th>Ay</th>\n",
              "      <th>Az</th>\n",
              "      <th>Gx</th>\n",
              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
              "      <th>Condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>-0.809</td>\n",
              "      <td>-0.188</td>\n",
              "      <td>-35.122325</td>\n",
              "      <td>14.954204</td>\n",
              "      <td>4.755551</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.070</td>\n",
              "      <td>-1.023</td>\n",
              "      <td>0.056</td>\n",
              "      <td>-2.578311</td>\n",
              "      <td>-0.171887</td>\n",
              "      <td>-0.114592</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.070</td>\n",
              "      <td>-1.019</td>\n",
              "      <td>0.056</td>\n",
              "      <td>-3.781523</td>\n",
              "      <td>0.859437</td>\n",
              "      <td>-0.286479</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.070</td>\n",
              "      <td>-1.019</td>\n",
              "      <td>0.044</td>\n",
              "      <td>-5.385805</td>\n",
              "      <td>1.948057</td>\n",
              "      <td>-0.458366</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.05</td>\n",
              "      <td>-0.069</td>\n",
              "      <td>-1.025</td>\n",
              "      <td>0.028</td>\n",
              "      <td>-6.531721</td>\n",
              "      <td>2.578311</td>\n",
              "      <td>-0.687550</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-174cd519-6437-4aca-bdb3-d04e0030fd98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-174cd519-6437-4aca-bdb3-d04e0030fd98 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-174cd519-6437-4aca-bdb3-d04e0030fd98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "L9zHJVdZ0BMR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d89efb7-4c13-4227-edec-858a3a651972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(166763, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "oQcAko6D0I2q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca7f5804-9201-4666-e6b6-c88c606af033"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 166763 entries, 0 to 166762\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   Timestamp  166763 non-null  float64\n",
            " 1   Ax         166763 non-null  float64\n",
            " 2   Ay         166763 non-null  float64\n",
            " 3   Az         166763 non-null  float64\n",
            " 4   Gx         166763 non-null  float64\n",
            " 5   Gy         166763 non-null  float64\n",
            " 6   Gz         166763 non-null  float64\n",
            " 7   Condition  166763 non-null  int64  \n",
            "dtypes: float64(7), int64(1)\n",
            "memory usage: 10.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "id": "PLI7b6oq0Uob",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "420ed0bb-0be1-4e86-cee7-e9989483a900"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp    0\n",
              "Ax           0\n",
              "Ay           0\n",
              "Az           0\n",
              "Gx           0\n",
              "Gy           0\n",
              "Gz           0\n",
              "Condition    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Condition'].value_counts()"
      ],
      "metadata": {
        "id": "CFWog8-q0cFv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c32de6d7-bb61-4ae7-8842-1c5e2eb8204f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    82290\n",
              "2    70126\n",
              "1    14347\n",
              "Name: Condition, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Balance This Data"
      ],
      "metadata": {
        "id": "4093lGUf0zXy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Ax'] = data['Ax'].astype('float')\n",
        "data['Ay'] = data['Ay'].astype('float')\n",
        "data['Az'] = data['Az'].astype('float')\n",
        "data['Gx'] = data['Gx'].astype('float')\n",
        "data['Gy'] = data['Gy'].astype('float')\n",
        "data['Gz'] = data['Gz'].astype('float')"
      ],
      "metadata": {
        "id": "Z6z3Oui90kuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "nVE5Jws20xY5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cce457f0-4d77-47ca-acd9-e2dd89c165d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 166763 entries, 0 to 166762\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   Timestamp  166763 non-null  float64\n",
            " 1   Ax         166763 non-null  float64\n",
            " 2   Ay         166763 non-null  float64\n",
            " 3   Az         166763 non-null  float64\n",
            " 4   Gx         166763 non-null  float64\n",
            " 5   Gy         166763 non-null  float64\n",
            " 6   Gz         166763 non-null  float64\n",
            " 7   Condition  166763 non-null  int64  \n",
            "dtypes: float64(7), int64(1)\n",
            "memory usage: 10.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Fs = 100"
      ],
      "metadata": {
        "id": "Pnm9pwlL1Fm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "activities = data['Condition'].value_counts().index"
      ],
      "metadata": {
        "id": "nA-TjTya2cy5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_activity(condition, data):\n",
        "  # fig, (ax0, ax1, ax2, gx0, gx1, gx2) = plt.subplots(nrows=6, figsize=(15, 7), sharex=True)\n",
        "  fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(15, 7), sharex=True)\n",
        "  plot_axis(ax0, data['Timestamp'], data['Ax'], 'Ax-Axis')\n",
        "  plot_axis(ax1, data['Timestamp'], data['Ay'], 'Ay-Axis')\n",
        "  plot_axis(ax2, data['Timestamp'], data['Az'], 'Az-Axis')\n",
        "  # plot_axis(gx0, data['Timestamp'], data['Gx'], 'Gx-Axis')\n",
        "  # plot_axis(gx1, data['Timestamp'], data['Gy'], 'Gx-Axis')\n",
        "  # plot_axis(gx2, data['Timestamp'], data['Gz'], 'Gx-Axis')\n",
        "  plt.subplots_adjust(hspace=0.2)\n",
        "  fig.suptitle(condition)\n",
        "  plt.subplots_adjust(top=0.90)\n",
        "  plt.show()\n",
        "\n",
        "def plot_axis(ax, x, y, title):\n",
        "  ax.plot(x, y, 'g')\n",
        "  ax.set_title(title)\n",
        "  ax.xaxis.set_visible(False)\n",
        "  ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])\n",
        "  ax.set_xlim([min(x), max(x)])\n",
        "  ax.grid(True)\n",
        "\n",
        "for activity in activities:\n",
        "  data_for_plot = data[(data['Condition'] == activity)][:Fs*5]\n",
        "  plot_activity(activity, data_for_plot)\n",
        "    "
      ],
      "metadata": {
        "id": "DGzkijIM0-CX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "df86bdee-295c-4cf1-91e5-118d3b15dd7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 3 Axes>"
            ],
            "image/png": 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CN1ZV2tltzpYkffDHBzqYcbDCdd9Z9442JW5SiE+I3hr1luKC4+Rp8dT0MdPl4+GjhfsX6u11b1e7hp0ndmrwx4O1N3Wv2ga11W+TflPrwNal1rOYLZp68VTdO+BeOeXUzXNurrNhngAAAAAANAWEZGh0diXv0v60/fKyeOnjyz9W6xatdSz7mGZsneHu0kqY0HOCzmp1ltLy03Tdt9fJ5rCVud6xrGP6z+L/SJKmXDBF4f7hxrIuYV304oUvSpL+tfBf2nliZ5WPv+bwGg35ZIgOZx5Wl7AuWn7TcsUGxZa7vslk0msXv6bbz7xdDqdDE7+bqEmzJ2lvyt4qHxMAAAAAgKaKkAyNjquL7Px25yvEN0QPnP2AJOl/K/6nPSl7FJ8Wr+M5x91ZoiTJy+KlmdfMVKB3oFYcWqHHFz9e5nr/XPhPZRZk6qxWZ+nWM28ttfyus+7SyA4jlW/L1/hvxyvPmlfpsWdtm6ULPrtAqXmpGtB6gJbftFwxQTGVbmcymfTOpe/ojn53yOF06LPNn6nLW110/XfXa2nC0hoP+QQAAAAA4HRncjqdTncXUZcyMzMVFBSkjIwMBQYGursc1MC5n5yr5QeX661Rb+mus+5SZkGmYl+NVUZBycn7Y4NiNSxumIa1HaZru18rfy9/t9T71favNO6bcTLJpJ+u+0mXdLrEWPbssmf1n8X/kUkmrb1trfq36l/mPhKzE9X73d46nnNcd/W/S2+NfqvM9fJt+Xrg5wf07oZ3JUkjO4zUt9d+qwCvgGrXve7IOj217KkS87+1CWyjcd3H6eKOF2tQm0FuO6cAAAAAANSVqmZFhGTNlNPp1GtrXlOvyF46v935NdpHen66Fscv1sL9C7UreZeeH/G8BrQeUKu6UnJTFPFShBxOhxLuS1Db4LaSpHfXv6vHlzyuAluBrA6r8qx5curkW7dzy86adc0s9Y7qXavj19QdP96h9/94XxaTRY8OfVT/Ofc/enX1q3ro14ckSS9e+KL+ec4/K9zHgn0LdPGMiyVJ3177rcZ0HVNi+c4TO3Xdd9dpU+ImmWTSv4f+W08Me0IeZo9a1b7h6Aa9ve5tfbvz2xJBpIfZQwNaD9CVna/U+B7jq9SpBgAAAABAY0NIRkhWoRUHV2jIJ0Pk4+GjLX/bok4tO1V5W6vdqn8t/JfeWPtGieF50QHR2njHxhJ3VqyuGVtm6Prvr1evyF7a/LfN5a6XU5ijlYdWaumBpfpk0yc6mnVU3hbvojm3+t0uk8lU4xqqw+F0yGwyK8+aV+5k+M8Mf0aPnvtohfuxO+xKzk3WK6te0f9W/k/BPsF685I3dUWXK+Tj4aOXV75cFBLaCxTmF6bpV03XRR0vqtPnkm/L1/y98/X9ru+1OGGxDmceNpaZZNK5bc/VeW3PU9fwruoW3k2tW7RWsE+wLGZLndaBslntVjmcDnl7eLu7FDRBOYU5OpF7QtmF2couzFZOYY7sTrvsDruccirML0xRAVGKCoiSl8Wr1sdzOB2yOWzyMHvIbCp75odCe6GyCrJkMVvkbfGWt4d3uevWNafTKZvDpjxbnnKtucZHnrXoa4fTIU+LpzzMHvI0e8rT4mn8v/hjFpNFHmYPWcwWWUwWWcyWCp9z8eM7nA45nA45VezzCh43mUzy8fCRj4dPlf944nQ6ZXfaZXPYZHPYZLVbjdclwCtAnhbPGp07q8OqfFu+CmwFMplMMpvMspgsMpvMRZ+bLSUek2Q8F4fTIbvTXuJrh9Mhu6P0YzVd12KyyMviJU+Lp7wsXkWfm4t9bvGU2WQ2zkmBvUAZ+RnKLMhUdmG28VxNJpNCfELU0q+lwvzC5OfpV+3zVd75K7QXqtBeKJvDJrPJLJNMxvkr/uE6v8bXMsnmsKnAXmC8BgX2AhXY/vr6r8/tTrvx/nS9R43Py3i8onWLvxdd5951rl1fSypzW9fr75RTTqez2v+3OWzGuSr+YXfajWO4vv9O/V60mCylvpdO/T6r6HuwvG1dz7X4zwPXcz71MdfXp/5csDvsZT4v10eBveDkc3XYjfeB631Sla9dnxc//2V97npfFv/jtFT0u6Hr+6Cyz4v/nLE7in1ezuMmk6ncn6/FP4q/rq46i9dd1mPFL31Pfaw665S33ak/b8r6nqgJ1/ks/toVP8eudSSV+Vjxx8t6zGK2yMfDR74evkX/9/Q1PveyeFXp2srusCvflm9cH1nMRT9rq/Nvt9PpVHZhttLz05VZkFniuVf2Xi6+zGQq+lmYXZitrIIs5dvyjdfAarcav+/kWnNlc9iM7+cWXi0U6B2oYJ9gRfhHKCogSuH+4Q32+wfq12kfkr311lt68cUXlZiYqN69e+uNN97QgAGVdykRklXNm2vf1L3z75UkDY0dqiWTlxjf/K5/dMsawnci54TGfj1WSw8slVQ08fyIdiP0a/yv2pW8S+e3O1+/XP9LjYOT8d+M16zts/TvIf/Wsxc8W6VtknOTNWn2JM3bO0+SdEXnK/T26LfVqkWrGtVQFU6nUy+tfElPLXtKfx/wd6PWmdtmasK3E4z1HhnyiJ674LkK95WSm6JLv7xUqw+v1hWdr9CyA8uUlp8mSfLz9FObwDbak7JHknRxx4v14WUflnkHy7rkdDoVnx6vBfsWaOb2mVp2YFm56wZ6B6pDSAed3eZsnd3mbA2LG1bhDQRQPqvdqs1Jm7U5cbO2Hd+mbSe26VDGIR3POW68J3w9fBXiG6JI/0h1CeuibuHddEbLMxQVEKUI/wgF+wSrwFagXGuusgqzlJSdpMTsRCXlJCkjP0NZhVlFHwVl/9/1C7crkAv3C1e4f7jC/cIV5hdm/D/IJ0iB3oEK8v7r/6d87QrzXBcxWYVZxkWmxWyRn6ef/Dz9FOQdJF9P3xqfM6fTqRO5J3Qk84hS8lKUlpem9Px02Z32Er/UV/S5VPRzL8eao5zCHOVYc5RrzVWONUcFtgLjQsbb4q0wvzBF+kcaF8TeHt7ysniVe0FjMVvk6+Fb4pdN1+eu/1vMFiN8ySzIVFJOkpKyk5SUc/K1S8lNMS4ail+sFr/w8jR7yilnlS9C7E67cgpzlJidqKzCrCqf81DfUEUHRCsqIEqhvqFlvuZ5tjyl5acpLS+txP9dv6haHVZjf2aTucSFkMVsUXZhtvJt+aWO3dK3pWKCYhQTGKPogGgjnPA0e5YIs3KtuaUDrmJfF9gKZDFbjAtVV6CQZ80r+r8tr97naHRdqEsqcfF96oVoTXiYPYyLHW8Pb+N1tzqsxuvv+qiIp9lT/l7+8vf0l7+Xv3w8fIxwyymn8m35JT5c568unsPpyMfDR2F+YWrp21K+nr7G+8vhdJQIvk79sNpPLiv+vYHmxWwyy8PsIbvDLruzZkEKUF9cf6zytngbwZlrdI/r383y/k3x8fAx/h1x/d/b4m2Eh4X2QmXkZyg9P10ZBRmNbo5ki8liBGbRLaIV4hOiAK8ABXgFGD/jTw1DC2wFyirMUmZBpvF7tusPLa7fOWwOmwK8Asr8XdrD7FEq9DPLLJvTZvwBpPgfPgrsBcYfupxyGq+Xl8WrxO8XxX8HDvQOVIhPiEJ8Qozfab0t3sbrmmvNVa7t5B8JzSazgn2CS334e/obf3go6w9hrt91MvIzlJqXaoxcKuuPLzaHzXg+TqezzD+uSao0jHY6nUXPzzdEob6hat2itWKCYuRp9VRwcPDpGZLNmjVLN954o959910NHDhQU6dO1ddff63du3crIiKiwm0bIiRbf3S9ErMTNbrT6AbrWKprruGBLq9f/LruHXiv5u6Zq5t+uEmeZk+tu21diTBmS9IWXfblZTqYcVAtvFpo+pjpurzz5ZKKhgKe9cFZyrHm6D9D/6P/G/x/WnpgqVYeWqmWvi3VO6q3ekX2UoR/+a/fvtR96vVOL+XZ8rTqllU6u83ZVX4+DqdDr656VQ8velg2h01B3kF6eeTLurnvzXX+GmXkZ2jyD5M1e9ds4zHX8MjjOccV+dLJTrppV0zTpD6Tyt3XkcwjGjl9pHac2FFqWXRAtI5lH5NU9ENs6kVTNbnPZLe85w5mHNTsXbO1JWmLdpzYoV3Ju4zQpizntztfN/e5WWO6jqlVANLUpealatWhVVpxaIVWHlqptUfWKs9W+Y0bTgfFA5vK+Hv6lwzi/MMV4BlQ+h/Av36RcgUuKbkpOpx5WAX2ggZ4Rk2ft8Vbgd6BCvAKkL+Xv9HZ4AoiE7MTq/R6NjUmmYxQ18/TT76evjKbzLLarUbo5Pq8+P+5yK0fp3alnfrh+kW6zGUmi0wmkxxOR7UCKrPJbFy4BHgFGH9UtDvtSstLU0peigrthQ15GqrMw+xhXNQWv8A1m8xGt2hFgXrxxxors8ksb4u30Qno6npxhU3Fn0vxzx1OR6kOvbI688pbXt6y/2/vvuOdrO/+j7+zx9lwOIe9VRw4GQoq4MJVB8WBq9rW2tbR3rbVWq0Vb6ttXXi7qm0dP0FFrdaJglsLLhQRXKDscRackzOyc/3+OOYyZ+fkJOSM15NHHkmuXLnyTUhyknc+n+8V/3HIrNBMCKfj7xGdlVjpmHiIV9jGv/g2D9yTOZ9MRZgk84u6pDarzto63V6VYmvL4xWVzd9b23puxqvp4uNMHHdry9qrvurMOq0tax4OJL7/JD5XOiuxgjIx8Ig/xonrJS5LtiouEos0+cEj259HHVaH8l35slqsHT6f27rMbrUrz5mnXGeuWWVttVjlsDnMkMvr8Db5vFMXqpMv6FN1oFpl9WWqqK/osz/89EoBSX9RzwzJJk+erIkTJ+quu+6SJMViMQ0bNkyXXnqpfv/73zdZNxgMKhj8/guSz+fTsGHDVFlZmZGQrC5Up5F3jpQv6NOFB1yoecfMS6kdQZJ8QZ/+svQvOmjgQZo1btYuDT8Of/hwvbflPc0YMUNvbHhDXodXc/aeo3+t+Je5znFjjtN/Tv+PLBaLttZu1eQHJqusvkxji8bqqdlPaa8BezXZ5sLVC3Xus+dKkpkEN3f2PmfrXz/4V4uS1ZgR01Hzj9K7m97VjBEz9PJZL6f0eKwsX6mLXrxIy7ctlyQVugs1PH+4hhcM14iCERpeMFzD8oc1ns4frpKcEtWH6/Xupnf15oY39VXVV43pdSSoAneBrpt2nfYt2dfc/oqyFTrr6bO0dudaOW1OHTrsUL2+/nUVuAr0wU8+0G9f/a2e//p5OW1OhaIh7TNgHy3/6XJZLBYZhqHLl1yuT7Z/onHF4zSu/zjd/dHd2lCzQYNzB+uOmXfowU8f1EtrGyviLplwiebsM0cry1Zq5piZGpo/VJJUXl+uhasX6vk1jXsBHeAdoJKcEu1ZvKcOH3649ui/R6uP3YaaDfpo60dma0h/T2MFhtvubvWxNAxDO/w7zA9zMSOmsvoybfJt0hbfFrnsLhW4CpTnytOXlV/qlW9e0ZJ1S1psZ8aIGbps0mXKc+Z16v+0OlCtT7Z/ohVlK/RZ+WcKRUNmG4zb5pbX4VWOI0dj+43VrHGzNL5kfJsfcNJ5vrVWl/b4w36tr1mvddXr9O3ObxuPq7/Vmqo1+nrH1y3WL3IX6cCBB2rvAXtr7wF7a1ThKJXklKgkp0R2q107A42VUpt9m/Vl1Zf6svJLfbvzW5U3lKuioULVgWqzSinHkaMB3gEamDtQpTmlKnQXKteZqzxnnvmhIc+VcNqZZ355slqs8kf8qmyoVEVDhSobKs3TO/w75Av6Gg8hn1kh5gs1bUVqzmP3KN+Vr5gRkz/iV32oPi0fPCyyqDSnVP29/VXkLlKBu0AOq8O8TGr9Q37iZXarXbmOxnAo/tzKceTIZXeZX2z8Eb8q/ZWqqK9Qlb9K/rC/8Re8aGO1mdPqbNKu5bA6FDWiTT5wxn9tTVwWNaKNAYzdqxxnjkpzSlWSU9LkuL+nv9k6EI1FFTGaVoXFv0DEP4gnftlIbDVq3p7isXtUmlOqgbkDO3yNxoyYdvp3alvdNpXVl2lb3TbVBGpUE2z8/0/84ueyu8xfJwvdhern7qciT5H5HHPZXObfiYgRaREu5Tpzle/MV54rz/xF1h/xa3v9dm32bdZm32aV15drh3+HKv2VCkfDjf93dq9ZoWeGWt+9HhLPu2wuRY2o+aury+4yq67Mir/vtpFsm0lz8VbG+Bf1+P9d82VNvnirZStJa+0lrX1ZjxkxBaPBFs+1eEWj3dK03ctutctusbfawhSOhhsrK7+rroxX4AWigSbtQvHHymV3ma2eie068dbc5i2QiS2Sia1irYVanXm/7arE9r2oEW3SNtvebce/WFX5q8xDIBJo8rpsEm5YE94nbI4m7x3N2z/tVnuTL8ZttfklfkG0W+1mIJbOKRHibdKttcZ11FJryGjxWogHHYktZJ09Tmy160mavH8nhmixsPn3JF4F4rA6euyP8ui5DMNQKBqSP+L/vm07oWopFA3JYXV8/77v8Mhtazxts9rM13swEjT/njSEGxr/pkS+r+iOV5MXugvNNsdCV6Hcdne3eN5HYhHzh8L4oSZYY7ZrRo2o+ffJDEctNjlsDuU7880fH/Nd+cpz5plV2fHPQXWhuu+7LUI+8/N1vCKsteDPbfvuR4/v3ufjVWMOq8P8/BuKhRSIBBSKhlq0psa/l/pCPlX7q7UzsFOBaMD8DuywORo/N333mSp+HI1FVR2sNlthqwPVqg5Wqz5UL3/Eb3alxO9DtwwXe2pIFgqF5PV69dRTT+mUU04xl//oRz9SdXW1nn322SbrX3fddZo7d26L7Tz66KPyers+N0Rzr1W9pjs33WmeH587XleMvEJ59rxObSccC+uGb2/Qp3WN824dWnioLhp6UYfbCcaC+rzuc20KbNLm4GYZMjSrZJYGuQYlfduGYeisz86SP+bXHXvcofs336/V9avNy4/sd6Te3vm2wkZYlw67VIcXHa5r1l6jrxq+0gj3CP157J+Va299b4r3b75fL1U2hjylzlLtk7uPGqINWu9fr22hxqqocwado9mls5tc74WKF/TPLf+U2+rWHXvcoVJX6vOaRY2onq94Xo9tf0zBWPsVJk6Ls/ELi1r/xd9pcerCoRfqyH5H6rmK5zR/23xFjIgGOAboilFXaJRnlK5ec7W+avhKRfYi7YzslN1i159G/0l/XvdnBWIBXTfmOu2ft79eqnhJ92+5v8VtDHYN1p9G/8m8z0url+pv6/8mu8Wuu8bdpYGugZKkylCl7tt8n5b7liumtsuQC+2FOrjgYJ0+8HT1c/STYRh6dcer+sfmfyhktPyl22V1Kd+Wrzx7nvLseXJb3SoPlWtbcJsCsZbtTmjKIov5xTZidP2XdrvF3uTXy1THlC2GDIWNlr+S22RrMU+SYRitPic7y25p/LKfTj35/8AcQxo+XPaKx6ELYxjjHaM/jflTGkcDAACw6xlG42f0QCzQeIgGvj+dePhueTAWlD/mN48D0abLEtdv67t0uthkawxhrW55rB65rC65rW5zWYtDwnKX1SWP1SObxaaQP6TrLryu54VkW7du1ZAhQ7R06VIdcsgh5vIrrrhCb731lt5///0m6+/qSrIjHjlC7256Vz/Y/Qd6Y/0bqgvVaWzRWD192tMaVzwuqW0YhqEfP/9jLVi1QB67x/zFcnDuYD3wgwfa3Nvkuup1Ov6x4/XNzm+aLM915urWo27V+fs1tuKtr16vNTvWaPqI6a1WuW2o2aDd7t5NDqtD1b+r1kbfRk15cIosFov+eeI/deJuJ+rmZTfr6jeuVoGrQMeOOVYLP1+oQnehll2wTGOKxrR536KxqN7Y8IZGF47W6KLRTS57YMUD+vlLP5fVYtWiOYs0Y+QMSdI3O7/RQf88SA3hBt05805ddNBFST2OHWkIN2hd9TptrNmojTUbtcG3wTy9sWajttVtMxPuUYWjNG3ENE0YNKGxJ9vm0iOfPaKXv3nZvHxd9TpJ0g92/4HuP/5+9ff2Nx/PSf+aZLYf3jD9Bl0x5Qr9z+L/0d0f3a1jRh+jvx35Nx384MEKRAK6ZMIlynPlaXXFahW6C/Xn6X9usbODEx47QUvWLdHpe52u+afMV0O4QdP/33StKFshSZo4eKLO3OtMFecUq7K+Utvqt+mjrR9p2eZlZuuZ1+HV5ZMv19qda/X46sYdCozrP05Wi9X8pTuZ9onEapsB3gEamj9UQ/KGqD5Ubz6m0Vi0sWLEU6hcR6456fH7W97vYOsA0H1NGjxJ757/braHAQAA+gjDaJzzsy5Up/pwvXlcH6pXXbiuyRy6iTsgSFzPvCxcp4ZQQ+P1vtspUyY5bc7GaTu+68gwTztbno+fjre9Jp431/muyyMdO42SGrOi4uLi3h+SNZfJOcnW7lir3e7cTVaLVRt/vVE7/Dt00uMnaX31ehW4CvTEaU/omDHHdLida16/Rn9+58+yWWx64awX1N/TX+c8c46+rvpaVotV82bO0yWTLmlSBfBFxRc66pGjtLV2q0pySnTo8EO1Z/Geenfju+Yk+ocMPUTb67abYc7Vh12tG464ocXtv/D1C/rBYz/Q+JLxWvmLlZKknf6dZqms1FhaOvWBqfpgyweSGoOSF856QcfvdnzKj59hGPrJcz/Rgyse1ADvAC2cvVAfb/tYD6x4QJ9XfK4ZI2fo1fNe3WV7DwlFQ9pUs0kOm6PVieZjRkx/++/fdM3r1yhqROWxe3T7zNtb3Xvmc189p1kLZ+nQ4YfqtfNek81q07c7v9Vud+6mmBHTmKIx+mbnN5o5ZqYWnb2owwqPFdtX6MD7DpQhQx9e+KHmvTdPCz5boGJvsV477zXtW7pvq9cLRAJ6e8Pb+tObf9J7m98zl9ssNt145I367ZTffr8nI8OQL+hTlb/KbKWraqhSbahWw/KHabf+u2lU4agO96YYn8+irfsUiAR0z4f36L7l95k7IGjL0PyhmjFyho4afZSOHHVk0jso8AV9euHrF/TE6ie0aO2iJnPDnDLuFF0y8RIdPuJwc1lHezNq73xFQ4U+3PKh3t/yvp776rkWofW44nE6eY+TNaZojIbkD9GQvCHq5+nX4f95Ot6Ku1rW3NUxpKOsmjEwhnTdfjrG4La7W/zgAwAAYBiGuaOn+J7Bzb2EN1uWGGi1e9l3AVemd14Q35lCfB7axICqSVjV2mXNliWeT3Uaql2lx+7dsrPtls1lMiSLh1vHjj1Wi85eJKlxfqhZC2fpv5v+K5vFpttn3q5LJ1/a5jZuWXqLfrfkd5Kkf530L/34gB9Laqx6uvili/XQiockSb+Y8Avdcewd2uHfoU+2f6JznzlXlQ2V2nvA3lp87mJzz43RWFS3v3e7rn79ajMYsMgiQ4aKvcXa/D+bW4QcN71zk/7w+h901viztGDWgjbH+kXFFzrgvgMUjAY1d/pcXTvt2tQeuAT+sF+H/OsQfVr2aZPl+a58fXLRJ93yy8h/N/5XT6x+Qj+f8HPtOWDPNtfb4tuiYm9xk8d79hOz9e8v/i1JKvYWa+XPV2pQXnKtsec+c67mr5yvkpwSldeXy2ax6dXzXtX0kdM7vK5hGHrq86d09etXK2pE9cipj2jKsClJ3W6mGIahz8o/05Orn9RH2z5SnjNPRe4iFXuLdeCgAzV56GRz3rWu8AV9ev6r5/WvT/6lN9a/YS4fXzJel0y6RGePP1s5zpyktlUdqNbyrcv10daP9NG2j/TR1o+0vnp9k3UssmjayGk6Y+8zNGvPWe3unAIAAADArhMzYt9XWjULplINtuLVW5med8tj97QbTMUrrVq9rI0gK75zpr6ox4ZkUuPE/ZMmTdKddzbO/RWLxTR8+HBdcsklLSbuby5TIVk0FtWIeSO0pXaLFs5eqNP3Pt28LBgJ6ucv/twMuP53xv/qmsOvaXJ9wzB0w9s36No3G4Om66dfrz9O+2OLdW5ZeouufPVKc08wie1wEwZP0Mtnv2y2+SVaXb5ai9Yu0t4D9tbBQw/W3vfsrW1121qMVZLO+vdZemzVY7rpyJv0+0PbfzyXfLNEX1d9rV9M/EXaKrzW7lirqQ9MVV2oTtNGTNNRo4/S7L1mt1rN1dMt27RMUx5oDKeePfNZc2+gyVhfvV573LWHGX7OmzlPvzr4V526/fjLuztMfJkNq8pX6a4P7tIjKx9RQ7hBkpTnzNO+pftqj/57aGy/sXLYHOZkxL6gTzv8O1Tlr9LKspVau2Ntq9vdvf/umjB4gqYOm6pZe87SwNyBu/JuAQAAAL1KNBY1w6xkKq6atyO2dVn8O0AmNW8XbDXYauey1pZ5Hd4euWOS7qxHh2QLFy7Uj370I913332aNGmS5s2bpyeeeEJffvmlSkvbn9A9UyHZK2tf0bELjlU/Tz9tvXxri+oswzB007s36erXr5Yk3XHsHbps8mXmZVe/frVuevcmSdINM27Q1Ydf3eZtPfvlszrnmXNUF6qTRRYNzhus6SOn654T7lG+K7n79MfX/6gb3rlBR446Uq+e92qTy/a9d199Vv6ZXpjzgk7Y/YSkH4N0CkaC5i54e7t7P7xXLrvLrBrsjN+/+nv99b9/1Xn7naeHTn6oz4ZdXVUdqNaDnzyouz+8u0V7ZEdGF43WhMETNGHQBE0YPEEHDjpQBe6CDI0UAAAA6L4isUiTea/S1Wboj/gzPvYOK66SrMZKDLa8Du8umy4IXdOjQzJJuuuuu3TzzTdr+/bt2n///fV///d/mjx5cofXy1RIduZTZ2rh6oW6ZOIluvP4O9tcb+6bc3XdW9dJaqz8CUVDevjTh7W6onHvkbcec6suP+TyDm+vLlSnsroyDSsYltJEdRuqN2jUHaNkyNCaS9dobL+xkqRwNKycG3MUjoW1/lfrNaJwRKe3jV0nGotq+bblmjB4Am++aRAzYlpZtlJfVn6pryq/0rrqdTJkNO6yXlblu/JV5ClSkbtI44rH6cBBB7ZauQkAAAB0Z+FouHPVWPGwK9x+W2J8J2GZYrVYO54Tq5U2w3bbEp258tg9FBz0cT0+JEtVJkKyulCdiv9WrGA0qOU/W64DBx3Y5rqGYei3i3+r2967rclyt92teTPn6aIJ6dlzYzKOX3C8Fq1dpCunXqm/HPUXSY3tZ+PvHa98V76qr6zmjQIAAAAAsiQUDbVbjZVqhVbijqwywWaxJV9x1YmJ4N12N99RkRHJZkV9c8a2Tvrvxv8qGA1qZOFIHTDwgHbXtVgsuuWYWxr36PfRPZo6bKrO3/98nbbXabu8RetnB/1Mi9Yu0oMrHtT1M66X0+bUZ2WfSZL2KdmHNx8AAAAA6IBhGApFQ8lXY7UyX1ZboVc4Fs7o2O1We5fmy2orCHPZXHyfRK9ESJaEN9e/KUmaPnJ6Um8EFotFd59wt/529N+S3oteJpyw2wkalDtI2+q26T9f/ken7326PitvDMnGl4zP2rgAAAAAIN0Mw1AgEuhUm2Gyk8RHjWhGx+60OdNWjZUYbKUydQ/QlxGSJeGtDW9JkqaNmNap62UzIJMkh82hHx/wY/35nT/rt4t/q2kjpmlV+SpJjZVkAAAAALCrGYYhf8SffDVWJ4KtmBHL6NhdNlf7wVQr82V11JaY48whzAK6CUKyDtSH6vXh1g8ldT4k6w5+O+W3evLzJ/V11dc6deGp2uzbLIlKMgAAAADtixkx+cP+5KqxmoddHbQlGsrs1Nhuu7vDiqtUKrTsVr5CA70Zr/AOLN20VJFYRMMLhmtk4chsD6fTCt2Fen7O85r8z8latnmZuXx8KSEZAAAA0BvEjJjqQ/Vpq8aKL6sP12d87F6Ht/2KqySrsRKDrRxHjmxWW8bHDqD3ISTrQGKrZU+dmHD3/rvrydOe1LHzj1XUiGpw3mD18/TL9rAAAACAPiUai7YZTLW7h8Nw++v7I/6Mj72tYKq9NsOO5svyOryyWqwZHzsAJIuQrAOpzkfW3Rw1+ijdedyd+uVLv9T0kdOzPRwAAACg24rEIslXY3WizTAQCWR03BZZkq+46kSbocfhIcwC0CcQkrWjIdyg9ze/L0m9Ilj6xcRf6KjRR2lo/tBsDwUAAADosnA0nNT8Vy3Crg6qt4LRYEbHbbVYO664SrIaK3GZx+7psd0vANAdEJK1473N7ykcC2tI3hCNLhqd7eGkxW79d8v2EAAAANCHGIahUDTUuWqsdtoMEy8Px8IZHbvNYmt/0ndnTtJthonLXDYXYRYAdEN9PiSLGTEtXLVQM0bN0MDcgU0ue2v9d62WI3vufGQAAABAMgzDUDAa7PR8WclMBB+JRTI6drvVrjxnXqfmy0qmLdFpc/I9AAD6kD4fkj3y6SM6/9nzdfxux+vFs15sctmbG96UJE0fMX3XDwwAAABohWEYCkQCGWkzjBrRjI7daXN2qc2wrdZDp82Z0XEDAPqGPh+SvfLNK43Ha19RVUOV+nv7S5ICkYA5H9m0kT170n4AAADseoZhqCHckLZqrPiy+nC9YkYso2N3293tV1x1ohorvizHkSOHzZHRcQMA0BV9OiQzDMPce2XUiOq5r57TBQdcIEl68esXFYwGNSRviHbrxzxeAAAAvVXMiKkh3JDWNsO6UJ0awg0yZGR07B67Jy3zZSWGXV6HV3Zrn/6aAADoo/r0X79vd36rrbVbzfNPffGUGZLdt/w+SdJ5+53HPAQAAADdQDQWNcOsZCquWmszbO2yhnBDxsfudXiTq7jqoBorcZnX4ZXNasv42AEA6Cv6dEgWryIblj9Mm3ybtOSbJaoOVKuqoUpLvl0iiyy68MALszxKAACAniUSi5htgV2txkpc5o/4MzpuiyxmW2Aq82W11XrodXhltVgzOnYAANB1hGSSzt33XD371bNaXbFaz3/1vFZXrJYkHTPmGI0qGpXNIQIAAGRMOBruXDVWPLwKt9+WGIwGMzpuq8XaccVVkm2Gics8dg8dBAAA9GF9OyRb3xiSTRs5TQ6bQ6vfWq1HVz2qj7d9LEm66KCLsjk8AAAASVIoGmq3GivVieBD0VBGx22z2JKvuOrERPBuu5swCwAApF2fDck2VG/QhpoNsllsmjJsigbnDdbct+bq5bUvS5IG5Q7SibufmOVRAgCAnsIwDIWioeSrsVqZL6ut0CscC2d07HarveNWwiSrsRKDLZfNRZgFAAB6jD4bksVbLScMnqBcZ672HrC39ui/h76q+kqS9JMDfsIuqgEA6IUMw1AgEuhUm2Gyk8RHjWhGx+60OdNWjZW4zGlzZnTcAAAAPUHfDcnirZYjpkmSLBaLfrjnD3XjuzfKIot+euBPszk8AAD6PMMw5I/4k6/G6kSwFTNiGR27y+ZKy3xZzcMufsADAADInL4bkm34fj6yuAsOuEB3fnCnZu81WyMKR2RraAAA9CgxIyZ/2J9cNVbzsKuDtkRDRkbH7ra7k6u4SqIaK3Edu7XPfsQCAADosfrkJ7gtvi36Zuc3slqsOnT4oebysf3Gqvr31bKIuTMAAL1PzIipPlSftmqs+LL6cH3Gx+51eFOeL6ut1sMcR45sVlvGxw4AAICeoU+GZG9veFuSdMDAA5Tvym9ymdVizcaQAAAwRWPRTs2XZYZd4fbbEv0Rf8bH3mHFVQpthl6Hl7/PAAAAyLg+GZK9sOYFSdKMkTOyPBIAQE8WiUWSr8bqRJthIBLI6LgtsiRfcdWJieA9Dg9hFgAAAHqsPheS+cN+PffVc5KkH+71wyyPBgCwK4Sj4eSrsVoJstoKwoLRYEbHbbVYO664aueytoIwj90ji4WpBQAAAIBEfS4ke+WbV1QXqtPwguGaPGRytocDAPiOYRgKRUNpazNMvDwcC2d07DaLrUUo1aLiypFcNVbiMpfNRZgFAAAA7CJ9LiR7YvUTkqTZe87miwcApMAwDAWjweSrsToxEXwkFsno2B1WR9rny8px5Mhpc/I3BQAAAOjh+lRI5g/79fzXz0uSTt/79CyPBgAyyzAMBSKBjLQZRo1oRsfutDmTq7hKohorcR2nzZnRcQMAAADoufpUSPby2pfNVstJQyZlezgAIKkxzGoIN6StGiu+rD5cr5gRy+jY3XZ3yvNltdV6mOPIkcPmyOi4AQAAAKC5PhWSPfn5k5Kk0/Y6jbYYAJ0WM2JqCDektc2wLlSnhnCDDBkZHbvH7kl7m6HX4ZXd2qf+jAAAAADoxfrMt5vEvVrSagn0btFY1Ayzkqm4aq3NsLXLGsINGR97jiMnuYqrDqqxEs97HV7ZrLaMjx0AAAAAerI+E5ItWrtI9eF6jSgYoYmDJ2Z7OAAkRWIRsy2wo2qszsyX5Y/4MzpuiyxmW2AqbYZtVWh5HV5ZLdaMjh0AAAAA0Lo+EZJFY1HdvPRmSdLsvdirJdBZ4Wi4c9VY8fAq3H5bYjAazOi4rRZrxxVXjuSqsRKXeewe3kcAAAAAoJfpEyHZXR/cpfc2v6c8Z55+ffCvsz0cIGNC0VDy1VidmAg+FA1ldNw2iy35id07MRG82+4mzAIAAAAAJKXXh2Trdq7TH17/gyTp5qNv1tD8oVkeEfo6wzAUioaSr8bqRJthOBbO6NjtVntyFVdJVGMlruOyuQizAAAAAABZ1atDMsMwdNELF6kh3KBpI6bpwoMuzPaQ0IMYhqFAJNCpNsNkJ4mPGtGMjt1pc6Y8X1Z7rYdOmzOj4wYAAAAAIFt6dUj28KcPa8m3S+S2u/WPH/yDCbF7KcMw5I/4k6/G6kSwFTNiGR27y+Zqv+LKkXw1VmKw5bA5MjpuAAAAAAB6m14bkgUjQV3z+jWSpOumXafd+u+W5REhZsTkD/uTq8ZqHnZ10JZoyMjo2D12T6fmy0pmIvgcZ47s1l77EgQAAAAAoEfptd/Q56+cry21WzQkbwiT9XdSzIipPlSftmqs+LL6cH3Gx+51eNPeZpjjyJHNasv42AEAAAAAQPb02pDstvdukyRdOfVKueyuLI8mM6KxaKfmyzLDrnD7bYn+iD/jY++w4sqRXDVW4jKvw0tLLQAAAAAASEm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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Condition'].value_counts()"
      ],
      "metadata": {
        "id": "CYupxLkQ9bnE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bf953c1-465f-4d30-c748-46a6e76293f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    82290\n",
              "2    70126\n",
              "1    14347\n",
              "Name: Condition, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ADL = df[df['Condition'] == 0].head(14347).copy()\n",
        "Fall = df[df['Condition'] == 1].copy()\n",
        "FallImpact = df[df['Condition'] == 2].head(14347).copy()"
      ],
      "metadata": {
        "id": "77Hxc70A9i2R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_data = pd.DataFrame()\n",
        "balanced_data = balanced_data.append([ADL, Fall, FallImpact])\n",
        "balanced_data.shape"
      ],
      "metadata": {
        "id": "NBnP9kYr-Ro5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d9c641e-e0a0-447a-946c-0add012d7aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-150-6668e87ba592>:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  balanced_data = balanced_data.append([ADL, Fall, FallImpact])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(43041, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_data['Condition'].value_counts()"
      ],
      "metadata": {
        "id": "TUjkqpNI-e1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a66b982-20e4-4cb5-fb92-c8133f52a65c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14347\n",
              "1    14347\n",
              "2    14347\n",
              "Name: Condition, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_data.head()"
      ],
      "metadata": {
        "id": "9Uk6Pz25-n7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "6835a1be-f8bc-46b5-fdaa-b78a41e12599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Timestamp     Ax     Ay     Az         Gx         Gy        Gz        C1  \\\n",
              "0       0.01 -0.250 -0.809 -0.188 -35.122325  14.954204  4.755551  0.312800   \n",
              "1       0.02 -0.070 -1.023  0.056  -2.578311  -0.171887 -0.114592  0.089644   \n",
              "2       0.03 -0.070 -1.019  0.056  -3.781523   0.859437 -0.286479  0.089644   \n",
              "3       0.04 -0.070 -1.019  0.044  -5.385805   1.948057 -0.458366  0.082680   \n",
              "4       0.05 -0.069 -1.025  0.028  -6.531721   2.578311 -0.687550  0.074465   \n",
              "\n",
              "         C2      Roll     Pitch         Yaw  Condition  \n",
              "0 -0.351223  0.877754 -0.432819    0.149542          0  \n",
              "1 -0.025783 -0.881074 -0.045101  359.998281          0  \n",
              "2 -0.037815 -0.882723 -0.057149    0.008594          0  \n",
              "3 -0.053858 -0.887782 -0.073123    0.019481          0  \n",
              "4 -0.065317 -0.894579 -0.084145    0.025783          0  "
            ],
            "text/html": [
              "\n",
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              "      <th>Gy</th>\n",
              "      <th>Gz</th>\n",
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              "      <th>C2</th>\n",
              "      <th>Roll</th>\n",
              "      <th>Pitch</th>\n",
              "      <th>Yaw</th>\n",
              "      <th>Condition</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.03</td>\n",
              "      <td>-0.070</td>\n",
              "      <td>-1.019</td>\n",
              "      <td>0.056</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.070</td>\n",
              "      <td>-1.019</td>\n",
              "      <td>0.044</td>\n",
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              "      <td>0.019481</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.05</td>\n",
              "      <td>-0.069</td>\n",
              "      <td>-1.025</td>\n",
              "      <td>0.028</td>\n",
              "      <td>-6.531721</td>\n",
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              "      <td>-0.687550</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bb4bf21-e683-4555-baa5-e657bdbae04a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5bb4bf21-e683-4555-baa5-e657bdbae04a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5bb4bf21-e683-4555-baa5-e657bdbae04a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "8YENW8ml-rGZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = LabelEncoder()\n",
        "balanced_data['label'] = label.fit_transform(balanced_data['Condition'])\n",
        "balanced_data.head()"
      ],
      "metadata": {
        "id": "Uw-pvGME-04m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "4e25f35a-9dda-4f35-d4ce-c2bcdb463e8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Timestamp     Ax     Ay     Az         Gx         Gy        Gz        C1  \\\n",
              "0       0.01 -0.250 -0.809 -0.188 -35.122325  14.954204  4.755551  0.312800   \n",
              "1       0.02 -0.070 -1.023  0.056  -2.578311  -0.171887 -0.114592  0.089644   \n",
              "2       0.03 -0.070 -1.019  0.056  -3.781523   0.859437 -0.286479  0.089644   \n",
              "3       0.04 -0.070 -1.019  0.044  -5.385805   1.948057 -0.458366  0.082680   \n",
              "4       0.05 -0.069 -1.025  0.028  -6.531721   2.578311 -0.687550  0.074465   \n",
              "\n",
              "         C2      Roll     Pitch         Yaw  Condition  label  \n",
              "0 -0.351223  0.877754 -0.432819    0.149542          0      0  \n",
              "1 -0.025783 -0.881074 -0.045101  359.998281          0      0  \n",
              "2 -0.037815 -0.882723 -0.057149    0.008594          0      0  \n",
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              "      <td>0.04</td>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ff64bce-24bc-4554-9a29-f5c8f6e4f425 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ff64bce-24bc-4554-9a29-f5c8f6e4f425');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label.classes_"
      ],
      "metadata": {
        "id": "nw0UShs-_BzA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6858a3d5-ae62-4adf-c9ef-6cfc7af30e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = balanced_data[['Ax', 'Ay', 'Az', 'Gx', 'Gy', 'Gz']]\n",
        "y = balanced_data['label']"
      ],
      "metadata": {
        "id": "MZXiw0eE_RE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "scaled_X = pd.DataFrame(data = X, columns = ['Ax', 'Ay', 'Az', 'Gx', 'Gy', 'Gz'])\n",
        "scaled_X['label'] = y.values\n",
        "\n",
        "scaled_X"
      ],
      "metadata": {
        "id": "8XjMRBDz_o-K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "outputId": "e07c8c3d-bb3d-40b7-9a4a-8f3a33de63af"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "             Ax        Ay        Az        Gx        Gy        Gz  label\n",
              "0     -0.579541 -0.552600 -0.091675 -0.335211  0.215264  0.176989      0\n",
              "1     -0.232233 -0.889737  0.241918  0.122363 -0.052420  0.042113      0\n",
              "2     -0.232233 -0.883436  0.241918  0.105446 -0.034169  0.037353      0\n",
              "3     -0.232233 -0.883436  0.225511  0.082889 -0.014904  0.032593      0\n",
              "4     -0.230303 -0.892888  0.203636  0.066778 -0.003750  0.026246      0\n",
              "...         ...       ...       ...       ...       ...       ...    ...\n",
              "43036 -0.211008  0.597449  1.550313  0.158615 -0.043294  0.043700      2\n",
              "43037 -0.212938  0.599024  1.555781  0.158615 -0.041266  0.045287      2\n",
              "43038 -0.211008  0.599024  1.555781  0.157809 -0.042280  0.043700      2\n",
              "43039 -0.212938  0.595873  1.554414  0.157004 -0.042280  0.045287      2\n",
              "43040 -0.211008  0.597449  1.553047  0.157004 -0.042280  0.042113      2\n",
              "\n",
              "[43041 rows x 7 columns]"
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              "      <th>Ax</th>\n",
              "      <th>Ay</th>\n",
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              "      <th>Gx</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.579541</td>\n",
              "      <td>-0.552600</td>\n",
              "      <td>-0.091675</td>\n",
              "      <td>-0.335211</td>\n",
              "      <td>0.215264</td>\n",
              "      <td>0.176989</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.232233</td>\n",
              "      <td>-0.889737</td>\n",
              "      <td>0.241918</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>-0.052420</td>\n",
              "      <td>0.042113</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.232233</td>\n",
              "      <td>-0.883436</td>\n",
              "      <td>0.241918</td>\n",
              "      <td>0.105446</td>\n",
              "      <td>-0.034169</td>\n",
              "      <td>0.037353</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.232233</td>\n",
              "      <td>-0.883436</td>\n",
              "      <td>0.225511</td>\n",
              "      <td>0.082889</td>\n",
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              "      <td>0.032593</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.230303</td>\n",
              "      <td>-0.892888</td>\n",
              "      <td>0.203636</td>\n",
              "      <td>0.066778</td>\n",
              "      <td>-0.003750</td>\n",
              "      <td>0.026246</td>\n",
              "      <td>0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>43036</th>\n",
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              "      <td>0.158615</td>\n",
              "      <td>-0.043294</td>\n",
              "      <td>0.043700</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>43037</th>\n",
              "      <td>-0.212938</td>\n",
              "      <td>0.599024</td>\n",
              "      <td>1.555781</td>\n",
              "      <td>0.158615</td>\n",
              "      <td>-0.041266</td>\n",
              "      <td>0.045287</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>43038</th>\n",
              "      <td>-0.211008</td>\n",
              "      <td>0.599024</td>\n",
              "      <td>1.555781</td>\n",
              "      <td>0.157809</td>\n",
              "      <td>-0.042280</td>\n",
              "      <td>0.043700</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>43039</th>\n",
              "      <td>-0.212938</td>\n",
              "      <td>0.595873</td>\n",
              "      <td>1.554414</td>\n",
              "      <td>0.157004</td>\n",
              "      <td>-0.042280</td>\n",
              "      <td>0.045287</td>\n",
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              "      <th>43040</th>\n",
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              "      <td>0.157004</td>\n",
              "      <td>-0.042280</td>\n",
              "      <td>0.042113</td>\n",
              "      <td>2</td>\n",
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              "      <script>\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-df80e4d4-8f23-4ea9-85e4-4f36d9eb781f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FRAME PREPARATION\n",
        "\n",
        "import scipy.stats as stats\n",
        "\n",
        "frame_size = 24\n",
        "hop_size = 24"
      ],
      "metadata": {
        "id": "s7E2N2eo__33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_frames(df, frame_size, hop_size):\n",
        "\n",
        "    N_FEATURES = 6\n",
        "\n",
        "    frames = []\n",
        "    labels = []\n",
        "    for i in range(0, len(df) - frame_size, hop_size):\n",
        "        Ax = df['Ax'].values[i: i + frame_size]\n",
        "        Ay = df['Ay'].values[i: i + frame_size]\n",
        "        Az = df['Az'].values[i: i + frame_size]\n",
        "        Gx = df['Gx'].values[i: i + frame_size]\n",
        "        Gy = df['Gy'].values[i: i + frame_size]\n",
        "        Gz = df['Gz'].values[i: i + frame_size]\n",
        "        # Retrieve the most often used label in this segment\n",
        "        label = stats.mode(df['label'][i: i + frame_size])[0][0]\n",
        "        frames.append([Ax, Ay, Az, Gx, Gy, Gz])\n",
        "        labels.append(label)\n",
        "\n",
        "    # Bring the segments into a better shape\n",
        "    frames = np.asarray(frames).reshape(-1, frame_size, N_FEATURES)\n",
        "    labels = np.asarray(labels)\n",
        "\n",
        "    return frames, labels\n",
        "\n",
        "X, y = get_frames(scaled_X, frame_size, hop_size)\n",
        "\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "id": "JnilQEXDCJsW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f81da82-b7bc-4922-e3c5-837cdeb0d7fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-159-367ab2ba1254>:15: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
            "  label = stats.mode(df['label'][i: i + frame_size])[0][0]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1793, 24, 6), (1793,))"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (14347*6)/80"
      ],
      "metadata": {
        "id": "J4Q9d1M7W95W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split data into training and testing set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "etV-h6PKC34C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape\n",
        "# X_train.shape, X_test.shape, X_val.shape\n",
        "# X_train_rest.shape, X_valid.shape"
      ],
      "metadata": {
        "id": "Ho9diY9FEs8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b506d4b9-6e14-43a5-8252-04478ef78c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1434, 24, 6), (359, 24, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0].shape, X_test[0].shape\n",
        "# X_train[0].shape, X_test[0].shape, X_val[0].shape\n",
        "# X_train_rest[0].shape, X_valid[0].shape"
      ],
      "metadata": {
        "id": "R0uwydYPEwAW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27428c6a-c13f-4638-cc5d-8ff3c26cb216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((24, 6), (24, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to match model input\n",
        "# X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
        "# X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "\n",
        "X_train_reshaped = np.reshape(X_train, (1434, 144, 1))\n",
        "X_test_reshaped = np.reshape(X_test, (359, 144, 1))\n",
        "\n",
        "# X_train_reshaped = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], X_train.shape[2], 1))\n",
        "# X_test_reshaped = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], X_test.shape[2], 1))\n"
      ],
      "metadata": {
        "id": "H2Huv1sCIyyR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1D CNN"
      ],
      "metadata": {
        "id": "e9w29XFDtWLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the fourth dimension\n",
        "# X_train = np.squeeze(X_train, axis=3)"
      ],
      "metadata": {
        "id": "y5lfeU-7zTwe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.regularizers import l2\n",
        "\n",
        "# Hapus model yang ada\n",
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# create model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(144, 1), kernel_regularizer=l2(0.01)))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "model.add(Conv1D(filters=128, kernel_size=3, activation='relu', kernel_regularizer=l2(0.01)))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "model.add(Conv1D(filters=256, kernel_size=3, activation='relu', kernel_regularizer=l2(0.01)))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=128, activation='relu', kernel_regularizer=l2(0.01)))\n",
        "model.add(Dense(units=3, activation='softmax'))"
      ],
      "metadata": {
        "id": "p3RykfmMtfZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# menyesuaikan label pada data menjadi one-hot encoding dengan fungsi to_categorical\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(y_train, num_classes=3)\n",
        "y_test = to_categorical(y_test, num_classes=3)\n"
      ],
      "metadata": {
        "id": "qZxeLZLsSd19"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### COMPILE"
      ],
      "metadata": {
        "id": "xdyqV29uv9th"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "INup33KK244A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hE55lR1zKjx",
        "outputId": "e4da713e-a0eb-46b6-af5f-99f02394d836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 142, 64)           256       \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 71, 64)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 69, 128)           24704     \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 34, 128)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 32, 256)           98560     \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 16, 256)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               524416    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 387       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 648,323\n",
            "Trainable params: 648,323\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TRAINING MODEL"
      ],
      "metadata": {
        "id": "4ECW1XAVH370"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train model\n",
        "\n",
        "epochs = 100\n",
        "batch_size = 128\n",
        "\n",
        "history = model.fit(X_train_reshaped, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test_reshaped, y_test))\n",
        "\n",
        "# from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "# early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "# history = model.fit(X_train_reshaped, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test_reshaped, y_test), callbacks=[early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8pdvfYwH2N6",
        "outputId": "118f1ad5-1543-40b2-ee5c-ed550e142b3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 4s 61ms/step - loss: 4.6975 - accuracy: 0.7985 - val_loss: 3.4667 - val_accuracy: 0.8691\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 2.8210 - accuracy: 0.8849 - val_loss: 2.2430 - val_accuracy: 0.8997\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 1.9196 - accuracy: 0.8884 - val_loss: 1.5564 - val_accuracy: 0.9053\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 1.3846 - accuracy: 0.9045 - val_loss: 1.1925 - val_accuracy: 0.9025\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 1.0922 - accuracy: 0.9031 - val_loss: 0.9348 - val_accuracy: 0.9053\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.8749 - accuracy: 0.9142 - val_loss: 0.8030 - val_accuracy: 0.9053\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.7285 - accuracy: 0.9100 - val_loss: 0.6643 - val_accuracy: 0.9164\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.6536 - accuracy: 0.9059 - val_loss: 0.6036 - val_accuracy: 0.9136\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.5682 - accuracy: 0.9121 - val_loss: 0.5759 - val_accuracy: 0.9081\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.5297 - accuracy: 0.9107 - val_loss: 0.5428 - val_accuracy: 0.8997\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.4931 - accuracy: 0.9121 - val_loss: 0.4853 - val_accuracy: 0.9192\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.4598 - accuracy: 0.9121 - val_loss: 0.4556 - val_accuracy: 0.9192\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.4323 - accuracy: 0.9219 - val_loss: 0.4334 - val_accuracy: 0.9136\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.4216 - accuracy: 0.9079 - val_loss: 0.5500 - val_accuracy: 0.8552\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.4821 - accuracy: 0.8933 - val_loss: 0.4571 - val_accuracy: 0.9025\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.4198 - accuracy: 0.9212 - val_loss: 0.4227 - val_accuracy: 0.9192\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3896 - accuracy: 0.9240 - val_loss: 0.4029 - val_accuracy: 0.9248\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3823 - accuracy: 0.9156 - val_loss: 0.4200 - val_accuracy: 0.9081\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.3784 - accuracy: 0.9149 - val_loss: 0.4365 - val_accuracy: 0.9025\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3729 - accuracy: 0.9170 - val_loss: 0.3694 - val_accuracy: 0.9220\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3769 - accuracy: 0.9198 - val_loss: 0.4383 - val_accuracy: 0.8969\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3949 - accuracy: 0.9121 - val_loss: 0.3970 - val_accuracy: 0.9053\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3639 - accuracy: 0.9233 - val_loss: 0.3695 - val_accuracy: 0.9220\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3605 - accuracy: 0.9156 - val_loss: 0.3719 - val_accuracy: 0.9164\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.3520 - accuracy: 0.9233 - val_loss: 0.3535 - val_accuracy: 0.9276\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.3491 - accuracy: 0.9198 - val_loss: 0.3539 - val_accuracy: 0.9164\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3390 - accuracy: 0.9254 - val_loss: 0.3845 - val_accuracy: 0.9109\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3365 - accuracy: 0.9261 - val_loss: 0.3692 - val_accuracy: 0.9109\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3298 - accuracy: 0.9247 - val_loss: 0.3480 - val_accuracy: 0.9220\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3241 - accuracy: 0.9261 - val_loss: 0.3519 - val_accuracy: 0.9164\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3241 - accuracy: 0.9261 - val_loss: 0.3953 - val_accuracy: 0.9081\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3221 - accuracy: 0.9233 - val_loss: 0.3507 - val_accuracy: 0.9192\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.3147 - accuracy: 0.9268 - val_loss: 0.3372 - val_accuracy: 0.9331\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.3090 - accuracy: 0.9310 - val_loss: 0.3506 - val_accuracy: 0.9192\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.3218 - accuracy: 0.9289 - val_loss: 0.3332 - val_accuracy: 0.9276\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.3397 - accuracy: 0.9191 - val_loss: 0.3553 - val_accuracy: 0.9220\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3314 - accuracy: 0.9240 - val_loss: 0.3429 - val_accuracy: 0.9192\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3207 - accuracy: 0.9226 - val_loss: 0.3535 - val_accuracy: 0.9192\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3289 - accuracy: 0.9198 - val_loss: 0.3368 - val_accuracy: 0.9248\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3375 - accuracy: 0.9163 - val_loss: 0.3684 - val_accuracy: 0.9109\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3301 - accuracy: 0.9191 - val_loss: 0.3436 - val_accuracy: 0.9136\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3204 - accuracy: 0.9303 - val_loss: 0.3571 - val_accuracy: 0.9164\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3190 - accuracy: 0.9254 - val_loss: 0.3810 - val_accuracy: 0.8942\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.3141 - accuracy: 0.9254 - val_loss: 0.3593 - val_accuracy: 0.9164\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.3014 - accuracy: 0.9331 - val_loss: 0.3696 - val_accuracy: 0.9053\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3148 - accuracy: 0.9219 - val_loss: 0.3585 - val_accuracy: 0.9053\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3113 - accuracy: 0.9282 - val_loss: 0.3604 - val_accuracy: 0.9081\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3095 - accuracy: 0.9310 - val_loss: 0.3718 - val_accuracy: 0.9025\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.3260 - accuracy: 0.9219 - val_loss: 0.3707 - val_accuracy: 0.9053\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3059 - accuracy: 0.9289 - val_loss: 0.3236 - val_accuracy: 0.9248\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2997 - accuracy: 0.9310 - val_loss: 0.3246 - val_accuracy: 0.9276\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2976 - accuracy: 0.9358 - val_loss: 0.3314 - val_accuracy: 0.9276\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2932 - accuracy: 0.9358 - val_loss: 0.3404 - val_accuracy: 0.9081\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3283 - accuracy: 0.9205 - val_loss: 0.3757 - val_accuracy: 0.8969\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2961 - accuracy: 0.9289 - val_loss: 0.3344 - val_accuracy: 0.9192\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2953 - accuracy: 0.9338 - val_loss: 0.3356 - val_accuracy: 0.9220\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2877 - accuracy: 0.9365 - val_loss: 0.3228 - val_accuracy: 0.9248\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2915 - accuracy: 0.9275 - val_loss: 0.3239 - val_accuracy: 0.9220\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2819 - accuracy: 0.9351 - val_loss: 0.3855 - val_accuracy: 0.9025\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2923 - accuracy: 0.9310 - val_loss: 0.3315 - val_accuracy: 0.9248\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2979 - accuracy: 0.9275 - val_loss: 0.3376 - val_accuracy: 0.9136\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.3018 - accuracy: 0.9365 - val_loss: 0.3369 - val_accuracy: 0.9248\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2922 - accuracy: 0.9317 - val_loss: 0.3313 - val_accuracy: 0.9192\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3269 - accuracy: 0.9191 - val_loss: 0.3853 - val_accuracy: 0.9025\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3145 - accuracy: 0.9254 - val_loss: 0.3396 - val_accuracy: 0.9248\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.3053 - accuracy: 0.9289 - val_loss: 0.3321 - val_accuracy: 0.9276\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.3305 - accuracy: 0.9212 - val_loss: 0.3392 - val_accuracy: 0.9136\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3180 - accuracy: 0.9282 - val_loss: 0.3316 - val_accuracy: 0.9192\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2911 - accuracy: 0.9317 - val_loss: 0.3595 - val_accuracy: 0.9025\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.3155 - accuracy: 0.9226 - val_loss: 0.3552 - val_accuracy: 0.9109\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2976 - accuracy: 0.9324 - val_loss: 0.3343 - val_accuracy: 0.9192\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2812 - accuracy: 0.9338 - val_loss: 0.3306 - val_accuracy: 0.9192\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2895 - accuracy: 0.9351 - val_loss: 0.3307 - val_accuracy: 0.9220\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2792 - accuracy: 0.9338 - val_loss: 0.3225 - val_accuracy: 0.9192\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2702 - accuracy: 0.9386 - val_loss: 0.3197 - val_accuracy: 0.9192\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2718 - accuracy: 0.9393 - val_loss: 0.3380 - val_accuracy: 0.9192\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2712 - accuracy: 0.9331 - val_loss: 0.3093 - val_accuracy: 0.9248\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2749 - accuracy: 0.9351 - val_loss: 0.3370 - val_accuracy: 0.9109\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2690 - accuracy: 0.9379 - val_loss: 0.3583 - val_accuracy: 0.9081\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2812 - accuracy: 0.9338 - val_loss: 0.3210 - val_accuracy: 0.9164\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2688 - accuracy: 0.9372 - val_loss: 0.3320 - val_accuracy: 0.9109\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2628 - accuracy: 0.9414 - val_loss: 0.3072 - val_accuracy: 0.9304\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2617 - accuracy: 0.9428 - val_loss: 0.3353 - val_accuracy: 0.9081\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2718 - accuracy: 0.9344 - val_loss: 0.3148 - val_accuracy: 0.9164\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2703 - accuracy: 0.9372 - val_loss: 0.3215 - val_accuracy: 0.9276\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2720 - accuracy: 0.9358 - val_loss: 0.3264 - val_accuracy: 0.9109\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2602 - accuracy: 0.9393 - val_loss: 0.3374 - val_accuracy: 0.9109\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2649 - accuracy: 0.9372 - val_loss: 0.3454 - val_accuracy: 0.9136\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2930 - accuracy: 0.9331 - val_loss: 0.3240 - val_accuracy: 0.9164\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2745 - accuracy: 0.9365 - val_loss: 0.3616 - val_accuracy: 0.9081\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2812 - accuracy: 0.9282 - val_loss: 0.3365 - val_accuracy: 0.9081\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2743 - accuracy: 0.9317 - val_loss: 0.3138 - val_accuracy: 0.9220\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2807 - accuracy: 0.9338 - val_loss: 0.3599 - val_accuracy: 0.9053\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2878 - accuracy: 0.9310 - val_loss: 0.3113 - val_accuracy: 0.9220\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2874 - accuracy: 0.9324 - val_loss: 0.3246 - val_accuracy: 0.9164\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2742 - accuracy: 0.9351 - val_loss: 0.3234 - val_accuracy: 0.9164\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.2738 - accuracy: 0.9358 - val_loss: 0.3237 - val_accuracy: 0.9164\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.2720 - accuracy: 0.9365 - val_loss: 0.3176 - val_accuracy: 0.9164\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2759 - accuracy: 0.9310 - val_loss: 0.3229 - val_accuracy: 0.9136\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.2792 - accuracy: 0.9338 - val_loss: 0.3059 - val_accuracy: 0.9248\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
        "# history = model.fit(X_train, y_train, epochs = 100, validation_data= (X_test, y_test), verbose=1)"
      ],
      "metadata": {
        "id": "UYKz_SNwaHGr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_learningCurve(history, epochs):\n",
        "  # Plot training & validation accuracy values\n",
        "  epoch_range = range(1, epochs+1)\n",
        "  plt.plot(epoch_range, history.history['accuracy'])\n",
        "  plt.plot(epoch_range, history.history['val_accuracy'])\n",
        "  plt.title('Model accuracy')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  # Plot training & validation loss values\n",
        "  plt.plot(epoch_range, history.history['loss'])\n",
        "  plt.plot(epoch_range, history.history['val_loss'])\n",
        "  plt.title('Model loss')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc='upper left')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "jCeC3Vc1aPo1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_learningCurve(history, epochs)"
      ],
      "metadata": {
        "id": "Cj6PcK0KaVae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "8bc1fd91-e855-499e-87e9-8f245b6aac68"
      },
      "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": {}
        },
        {
          "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": [
        "### Convusion Matrix"
      ],
      "metadata": {
        "id": "2DB-Tml2bNg_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.metrics import multilabel_confusion_matrix, confusion_matrix, accuracy_score, recall_score, precision_score, classification_report"
      ],
      "metadata": {
        "id": "iZtgTTYyaXAx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(model.predict(X_test_reshaped), axis=-1)\n",
        "# y_pred = model.predict(X_test_reshaped)\n",
        "y_pred = to_categorical(y_pred, num_classes=3)"
      ],
      "metadata": {
        "id": "bNavR1WEbSAO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd073b24-3354-441c-c6b5-274bee62b732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 10ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hitung confusion matrix untuk data test\n",
        "y_pred = np.where(y_pred > 0.5, 1, 0) # binarize output\n",
        "cm = multilabel_confusion_matrix(y_test, y_pred)\n",
        "# fig, ax = plt.subplots(figsize=(5, 5))\n",
        "# Print confusion matrix\n",
        "for i in range(cm.shape[0]):\n",
        "    print(f\"Confusion matrix class {i}\")\n",
        "    print(cm[i])\n",
        "    plot_confusion_matrix(cm[i], show_normed=True)\n",
        "    plt.show()\n",
        "\n",
        "# Visualisasikan confusion matrix\n",
        "# class_names = ['class 0', 'class 1', 'class 2']\n",
        "# fig, ax = plt.subplots(figsize=(5, 5))\n",
        "# plot_confusion_matrix(cm[0], labels=label.classes_, ax=ax)\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "ludtKcRBbTvi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "497a74b0-46db-40c0-9ffc-75170dd250b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix class 0\n",
            "[[228   5]\n",
            " [ 17 109]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix class 1\n",
            "[[249   9]\n",
            " [  7  94]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix class 2\n",
            "[[215  12]\n",
            " [  2 130]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = list(map(str, label.classes_))\n",
        "# print(target)\n",
        "print(classification_report(y_test, y_pred, target_names = target, digits=4))\n",
        "\n",
        "# accuracy score\n",
        "akurasi = accuracy_score(y_test, y_pred)\n",
        "print(\"Akurasi: \",akurasi)\n"
      ],
      "metadata": {
        "id": "BWo_MEYRcjU5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c760160-e9bc-463d-b53f-74c0e279a98c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9561    0.8651    0.9083       126\n",
            "           1     0.9126    0.9307    0.9216       101\n",
            "           2     0.9155    0.9848    0.9489       132\n",
            "\n",
            "   micro avg     0.9276    0.9276    0.9276       359\n",
            "   macro avg     0.9281    0.9269    0.9263       359\n",
            "weighted avg     0.9290    0.9276    0.9270       359\n",
            " samples avg     0.9276    0.9276    0.9276       359\n",
            "\n",
            "Akurasi:  0.9275766016713092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # model.save_weights('model.h5')\n",
        "# # print(tf.data)\n",
        "\n",
        "# # model.summary()\n",
        "\n",
        "# # evaluate the model\n",
        "# scores = model.evaluate(X_test_reshaped, y_pred, verbose=0)\n",
        "# # print(model.metrics_names)\n",
        "# print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))   "
      ],
      "metadata": {
        "id": "kzY9YYpj4o31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ee43157-0bf7-40ca-dc11-982dfeab8765"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyyaml h5py "
      ],
      "metadata": {
        "id": "jyQjaUhEmfsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # save model and architecture to single file\n",
        "# model.save(\"model.h5\")"
      ],
      "metadata": {
        "id": "peVgQUesV7p5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # serialize model to JSON\n",
        "# model_json = model.to_json()\n",
        "# with open('model.json', 'w') as json_file:\n",
        "#     json_file.write(model_json)\n",
        "\n",
        "# # serialize weights to HDF5\n",
        "# model.save_weights('model.h5')"
      ],
      "metadata": {
        "id": "4TAeNjyymjOR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calling `save('my_model')` creates a SavedModel folder `my_model`.\n",
        "model.save(\"my_model\")\n",
        "\n",
        "# It can be used to reconstruct the model identically.\n",
        "reconstructed_model = keras.models.load_model(\"my_model\")"
      ],
      "metadata": {
        "id": "9b2CagDuls4m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check:\n",
        "np.testing.assert_allclose(\n",
        "    model.predict(X_test), reconstructed_model.predict(X_test)\n",
        ")\n",
        "\n",
        "# The reconstructed model is already compiled and has retained the optimizer\n",
        "# state, so training can resume:\n",
        "reconstructed_model.fit(X_test, y_pred)"
      ],
      "metadata": {
        "id": "ZAN2yHy0mk1z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls my_model"
      ],
      "metadata": {
        "id": "OEcnk7DcqJE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calling `save('my_model.h5')` creates a h5 file `my_model.h5`.\n",
        "model.save(\"my_h5_model.h5\")\n",
        "\n",
        "# It can be used to reconstruct the model identically.\n",
        "reconstructed_model2 = keras.models.load_model(\"my_h5_model.h5\")"
      ],
      "metadata": {
        "id": "MBFNsROdr4xH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check:\n",
        "np.testing.assert_allclose(\n",
        "    model.predict(X_test), reconstructed_model2.predict(X_test)\n",
        ")\n",
        "\n",
        "# The reconstructed model is already compiled and has retained the optimizer\n",
        "# state, so training can resume:\n",
        "reconstructed_model2.fit(X_test, y_pred)"
      ],
      "metadata": {
        "id": "SfzYpEblthTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "zjpuUeTPvsIc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)"
      ],
      "metadata": {
        "id": "KFP5nU2ToDqJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LSTM"
      ],
      "metadata": {
        "id": "4JBI6zW6v08F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # lstm model\n",
        "# from keras.models import Sequential\n",
        "# from keras.layers import Dense\n",
        "# from keras.layers import Flatten\n",
        "# from keras.layers import Dropout\n",
        "# from keras.layers import LSTM\n",
        "# from keras.utils import to_categorical\n",
        "# from matplotlib import pyplot "
      ],
      "metadata": {
        "id": "U2pLMyDrx1lY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # fit and evaluate a model\n",
        "# def evaluate_model(trainX, trainy, testX, testy):\n",
        "#  verbose, epochs, batch_size = 0, 100, 64\n",
        "#  n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
        "#  model = Sequential()\n",
        "#  model.add(LSTM(100, input_shape=(n_timesteps,n_features)))\n",
        "#  model.add(Dropout(0.5))\n",
        "#  model.add(Dense(100, activation='relu'))\n",
        "#  model.add(Dense(n_outputs, activation='softmax'))\n",
        "#  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#  # fit network\n",
        "#  model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
        "#  # evaluate model\n",
        "#  _, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)\n",
        "#  return accuracy"
      ],
      "metadata": {
        "id": "VH1crgW0zLM5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # summarize scores\n",
        "# def summarize_results(scores):\n",
        "#  from numpy import mean, std\n",
        "#  print(scores)\n",
        "#  m, s = mean(scores), std(scores)\n",
        "#  print('Accuracy: %.3f%% (+/-%.3f)' % (m, s))"
      ],
      "metadata": {
        "id": "v9huSHo1zm4-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # run an experiment\n",
        "\n",
        "#  # zero-offset class values\n",
        "# repeats = 10\n",
        "# y_train = y_train - 1\n",
        "# y_test = y_test - 1\n",
        "# # one hot encode y\n",
        "# y_train = to_categorical(y_train)\n",
        "# y_test = to_categorical(y_test)\n",
        "# print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
        " \n",
        "# # repeat experiment\n",
        "# scores = list()\n",
        "# for r in range(repeats):\n",
        "#  score = evaluate_model(X_train, y_train, X_test, y_test)\n",
        "#  score = score * 100.0\n",
        "#  print('>#%d: %.3f' % (r+1, score))\n",
        "#  scores.append(score)\n",
        " \n",
        "# # summarize results\n",
        "# summarize_results(scores)\n",
        " "
      ],
      "metadata": {
        "id": "Fb-wWRcxzoi7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2D CNN Model"
      ],
      "metadata": {
        "id": "DEA86mMIZ-i4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NB_START_EPOCHS = 20  # Number of epochs usually start to train with\n",
        "# BATCH_SIZE = 512  # Size of the batches used in the mini-batch gradient descent\n",
        "\n",
        "# def deep_model(model, X_train, y_train, X_valid, y_valid):\n",
        "#     '''\n",
        "#     Function to train a multi-class model. The number of epochs and \n",
        "#     batch_size are set by the constants at the top of the\n",
        "#     notebook. \n",
        "    \n",
        "#     Parameters:\n",
        "#         model : model with the chosen architecture\n",
        "#         X_train : training features\n",
        "#         y_train : training target\n",
        "#         X_valid : validation features\n",
        "#         Y_valid : validation target\n",
        "#     Output:\n",
        "#         model training history\n",
        "#     '''\n",
        "#     model.compile(optimizer='rmsprop'\n",
        "#                   , loss='categorical_crossentropy'\n",
        "#                   , metrics=['accuracy'])\n",
        "    \n",
        "#     history = model.fit(X_train\n",
        "#                        , y_train\n",
        "#                        , epochs=NB_START_EPOCHS\n",
        "#                        , batch_size=BATCH_SIZE\n",
        "#                        , validation_data=(X_valid, y_valid)\n",
        "#                        , verbose=0)\n",
        "#     return history\n",
        "# def eval_metric(model, history, metric_name):\n",
        "#     '''\n",
        "#     Function to evaluate a trained model on a chosen metric. \n",
        "#     Training and validation metric are plotted in a\n",
        "#     line chart for each epoch.\n",
        "    \n",
        "#     Parameters:\n",
        "#         history : model training history\n",
        "#         metric_name : loss or accuracy\n",
        "#     Output:\n",
        "#         line chart with epochs of x-axis and metric on\n",
        "#         y-axis\n",
        "#     '''\n",
        "#     metric = history.history[metric_name]\n",
        "#     val_metric = history.history['val_' + metric_name]\n",
        "#     e = range(1, NB_START_EPOCHS + 1)\n",
        "#     plt.plot(e, metric, 'bo', label='Train ' + metric_name)\n",
        "#     plt.plot(e, val_metric, 'b', label='Validation ' + metric_name)\n",
        "#     plt.xlabel('Epoch number')\n",
        "#     plt.ylabel(metric_name)\n",
        "#     plt.title('Comparing training and validation ' + metric_name + ' for ' + model.name)\n",
        "#     plt.legend()\n",
        "#     plt.show()\n",
        "# def test_model(model, X_train, y_train, X_test, y_test, epoch_stop):\n",
        "#     '''\n",
        "#     Function to test the model on new data after training it\n",
        "#     on the full training data with the optimal number of epochs.\n",
        "    \n",
        "#     Parameters:\n",
        "#         model : trained model\n",
        "#         X_train : training features\n",
        "#         y_train : training target\n",
        "#         X_test : test features\n",
        "#         y_test : test target\n",
        "#         epochs : optimal number of epochs\n",
        "#     Output:\n",
        "#         test accuracy and test loss\n",
        "#     '''\n",
        "#     model.fit(X_train\n",
        "#               , y_train\n",
        "#               , epochs=epoch_stop\n",
        "#               , batch_size=BATCH_SIZE\n",
        "#               , verbose=0)\n",
        "#     results = model.evaluate(X_test, y_test)\n",
        "#     print()\n",
        "#     print('Test accuracy: {0:.2f}%'.format(results[1]*100))\n",
        "#     return results\n",
        "    \n",
        "# def compare_models_by_metric(model_1, model_2, model_hist_1, model_hist_2, metric):\n",
        "#     '''\n",
        "#     Function to compare a metric between two models \n",
        "    \n",
        "#     Parameters:\n",
        "#         model_hist_1 : training history of model 1\n",
        "#         model_hist_2 : training history of model 2\n",
        "#         metrix : metric to compare, loss, acc, val_loss or val_acc\n",
        "        \n",
        "#     Output:\n",
        "#         plot of metrics of both models\n",
        "#     '''\n",
        "#     metric_model_1 = model_hist_1.history[metric]\n",
        "#     metric_model_2 = model_hist_2.history[metric]\n",
        "#     e = range(1, NB_START_EPOCHS + 1)\n",
        "    \n",
        "#     metrics_dict = {\n",
        "#         'acc' : 'Training Accuracy',\n",
        "#         'loss' : 'Training Loss',\n",
        "#         'val_acc' : 'Validation accuracy',\n",
        "#         'val_loss' : 'Validation loss'\n",
        "#     }\n",
        "    \n",
        "#     metric_label = metrics_dict[metric]\n",
        "#     plt.plot(e, metric_model_1, 'bo', label=model_1.name)\n",
        "#     plt.plot(e, metric_model_2, 'b', label=model_2.name)\n",
        "#     plt.xlabel('Epoch number')\n",
        "#     plt.ylabel(metric_label)\n",
        "#     plt.title('Comparing ' + metric_label + ' between models')\n",
        "#     plt.legend()\n",
        "#     plt.show()\n",
        "    \n",
        "# def optimal_epoch(model_hist):\n",
        "#     '''\n",
        "#     Function to return the epoch number where the validation loss is\n",
        "#     at its minimum\n",
        "    \n",
        "#     Parameters:\n",
        "#         model_hist : training history of model\n",
        "#     Output:\n",
        "#         epoch number with minimum validation loss\n",
        "#     '''\n",
        "#     min_epoch = np.argmin(model_hist.history['val_loss']) + 1\n",
        "#     print(\"Minimum validation loss reached in epoch {}\".format(min_epoch))\n",
        "#     return min_epoch"
      ],
      "metadata": {
        "id": "fwdLiwHDo0xI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import regularizers\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(16, (2, 2), activation = 'relu', input_shape = X_train[0].shape))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(Conv2D(32, (2, 2), activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(6, activation='softmax'))\n",
        "\n",
        "# #Reduce the network's capacity by removing one hidden layer and lowering number of elements\n",
        "# reduced_model = models.Sequential()\n",
        "# reduced_model.add(layers.Dense(16, activation='relu', input_shape=X_train[0].shape))\n",
        "# reduced_model.add(layers.Dense(3, activation='softmax'))\n",
        "\n",
        "# reduced_model._name = 'Reduced_model'\n",
        "# reduced_history = deep_model(reduced_model, X_train_rest, y_train_rest, X_valid, y_valid)\n",
        "# reduced_min = optimal_epoch(reduced_history)\n",
        "\n",
        "# eval_metric(reduced_model, reduced_history, 'loss')"
      ],
      "metadata": {
        "id": "uloVFeXmaDIE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model2 = Sequential()\n",
        "\n",
        "# model2.add(Dense(64, input_dim=64, kernel_regularizer=regularizers.l2(0.01)))\n",
        "\n",
        "# # Hidden Layer 1\n",
        "# model2.add(layers.Conv2D(64, (4, 4), activation='relu', kernel_regularizer=regularizers.l2(l=0.01)))\n",
        "# model2.add(layers.MaxPool2D((2, 2)))\n",
        "\n",
        "# # Hidden Layer 2\n",
        "# model2.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_regularizer=regularizers.l2(l=0.01)))\n",
        "# model2.add(layers.MaxPool2D((2,2)))"
      ],
      "metadata": {
        "id": "tI4HjrodxWKe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}