Different_Speed_DNN.ipynb

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      "name": "Different_Speed_DNN.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vsemwal/human-walking-activities/blob/master/Different_Speed_DNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq7sPN5WQv6L",
        "colab_type": "code",
        "colab": {},
        "outputId": "198f91a9-262a-4263-a9a1-e694b454294a"
      },
      "source": [
        "from scipy import stats\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn import preprocessing\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Reshape\n",
        "from keras.layers import Conv1D, MaxPooling1D\n",
        "from keras.utils import np_utils\n",
        "from keras import regularizers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzk1SJBIQv6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sns.set()\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGfun6KbQv6Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(\"1,3,5,7mphnorwal walk.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "morFg9oFQv6S",
        "colab_type": "code",
        "colab": {},
        "outputId": "435a2d9f-13b4-40f9-e29c-d71419936fc9"
      },
      "source": [
        "data.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jointType</th>\n",
              "      <th>depthX</th>\n",
              "      <th>depthY</th>\n",
              "      <th>colorX</th>\n",
              "      <th>colorY</th>\n",
              "      <th>cameraX</th>\n",
              "      <th>cameraY</th>\n",
              "      <th>cameraZ</th>\n",
              "      <th>orientationX</th>\n",
              "      <th>orientationY</th>\n",
              "      <th>orientationZ</th>\n",
              "      <th>orientationW</th>\n",
              "      <th>time</th>\n",
              "      <th>activity</th>\n",
              "      <th>sid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.632764</td>\n",
              "      <td>0.585317</td>\n",
              "      <td>0.607712</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.629250</td>\n",
              "      <td>0.431639</td>\n",
              "      <td>0.603402</td>\n",
              "      <td>0.427327</td>\n",
              "      <td>0.387807</td>\n",
              "      <td>0.142325</td>\n",
              "      <td>2.237754</td>\n",
              "      <td>0.002097</td>\n",
              "      <td>0.996260</td>\n",
              "      <td>0.067203</td>\n",
              "      <td>0.054268</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.627228</td>\n",
              "      <td>0.289618</td>\n",
              "      <td>0.600105</td>\n",
              "      <td>0.266107</td>\n",
              "      <td>0.387825</td>\n",
              "      <td>0.519097</td>\n",
              "      <td>2.281877</td>\n",
              "      <td>0.014851</td>\n",
              "      <td>0.955451</td>\n",
              "      <td>0.044675</td>\n",
              "      <td>0.291370</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.652865</td>\n",
              "      <td>0.267710</td>\n",
              "      <td>0.619328</td>\n",
              "      <td>0.240958</td>\n",
              "      <td>0.480619</td>\n",
              "      <td>0.590595</td>\n",
              "      <td>2.339035</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.599162</td>\n",
              "      <td>0.327201</td>\n",
              "      <td>0.579418</td>\n",
              "      <td>0.309074</td>\n",
              "      <td>0.285866</td>\n",
              "      <td>0.402272</td>\n",
              "      <td>2.181561</td>\n",
              "      <td>0.732545</td>\n",
              "      <td>-0.582669</td>\n",
              "      <td>0.226117</td>\n",
              "      <td>-0.269716</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>0.607448</td>\n",
              "      <td>0.383479</td>\n",
              "      <td>0.588015</td>\n",
              "      <td>0.372856</td>\n",
              "      <td>0.278177</td>\n",
              "      <td>0.232619</td>\n",
              "      <td>1.948169</td>\n",
              "      <td>0.864103</td>\n",
              "      <td>0.096156</td>\n",
              "      <td>-0.217281</td>\n",
              "      <td>-0.443700</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>0.680003</td>\n",
              "      <td>0.382112</td>\n",
              "      <td>0.645399</td>\n",
              "      <td>0.370257</td>\n",
              "      <td>0.434405</td>\n",
              "      <td>0.215129</td>\n",
              "      <td>1.782735</td>\n",
              "      <td>0.705689</td>\n",
              "      <td>0.583907</td>\n",
              "      <td>-0.200805</td>\n",
              "      <td>-0.347467</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>0.689147</td>\n",
              "      <td>0.380264</td>\n",
              "      <td>0.652529</td>\n",
              "      <td>0.368030</td>\n",
              "      <td>0.454080</td>\n",
              "      <td>0.217461</td>\n",
              "      <td>1.771405</td>\n",
              "      <td>0.662416</td>\n",
              "      <td>0.691619</td>\n",
              "      <td>-0.100483</td>\n",
              "      <td>-0.269760</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>0.647253</td>\n",
              "      <td>0.325373</td>\n",
              "      <td>0.615782</td>\n",
              "      <td>0.306334</td>\n",
              "      <td>0.457578</td>\n",
              "      <td>0.430487</td>\n",
              "      <td>2.311362</td>\n",
              "      <td>0.658763</td>\n",
              "      <td>0.709686</td>\n",
              "      <td>0.205329</td>\n",
              "      <td>0.142185</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>0.594062</td>\n",
              "      <td>0.409911</td>\n",
              "      <td>0.574839</td>\n",
              "      <td>0.403139</td>\n",
              "      <td>0.304120</td>\n",
              "      <td>0.217628</td>\n",
              "      <td>2.449216</td>\n",
              "      <td>0.488150</td>\n",
              "      <td>0.066179</td>\n",
              "      <td>0.787886</td>\n",
              "      <td>0.369548</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   jointType    depthX    depthY    colorX    colorY   cameraX   cameraY  \\\n",
              "0          0  0.632764  0.585317  0.607712  0.602342  0.389040 -0.249903   \n",
              "1          1  0.629250  0.431639  0.603402  0.427327  0.387807  0.142325   \n",
              "2          2  0.627228  0.289618  0.600105  0.266107  0.387825  0.519097   \n",
              "3          3  0.652865  0.267710  0.619328  0.240958  0.480619  0.590595   \n",
              "4          4  0.599162  0.327201  0.579418  0.309074  0.285866  0.402272   \n",
              "5          5  0.607448  0.383479  0.588015  0.372856  0.278177  0.232619   \n",
              "6          6  0.680003  0.382112  0.645399  0.370257  0.434405  0.215129   \n",
              "7          7  0.689147  0.380264  0.652529  0.368030  0.454080  0.217461   \n",
              "8          8  0.647253  0.325373  0.615782  0.306334  0.457578  0.430487   \n",
              "9          9  0.594062  0.409911  0.574839  0.403139  0.304120  0.217628   \n",
              "\n",
              "    cameraZ  orientationX  orientationY  orientationZ  orientationW  time  \\\n",
              "0  2.184664     -0.013381      0.982128      0.065891     -0.175798  67.0   \n",
              "1  2.237754      0.002097      0.996260      0.067203      0.054268  67.0   \n",
              "2  2.281877      0.014851      0.955451      0.044675      0.291370  67.0   \n",
              "3  2.339035      0.000000      0.000000      0.000000      0.000000  67.0   \n",
              "4  2.181561      0.732545     -0.582669      0.226117     -0.269716  67.0   \n",
              "5  1.948169      0.864103      0.096156     -0.217281     -0.443700  67.0   \n",
              "6  1.782735      0.705689      0.583907     -0.200805     -0.347467  67.0   \n",
              "7  1.771405      0.662416      0.691619     -0.100483     -0.269760  67.0   \n",
              "8  2.311362      0.658763      0.709686      0.205329      0.142185  67.0   \n",
              "9  2.449216      0.488150      0.066179      0.787886      0.369548  67.0   \n",
              "\n",
              "   activity  sid  \n",
              "0       1.0  0.0  \n",
              "1       1.0  0.0  \n",
              "2       1.0  0.0  \n",
              "3       1.0  0.0  \n",
              "4       1.0  0.0  \n",
              "5       1.0  0.0  \n",
              "6       1.0  0.0  \n",
              "7       1.0  0.0  \n",
              "8       1.0  0.0  \n",
              "9       1.0  0.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
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        "colab": {},
        "outputId": "5b3bf78b-7bfb-4bba-89ae-5aed783a3576"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2266783 entries, 0 to 2266782\n",
            "Data columns (total 15 columns):\n",
            "jointType       int64\n",
            "depthX          float64\n",
            "depthY          float64\n",
            "colorX          float64\n",
            "colorY          float64\n",
            "cameraX         float64\n",
            "cameraY         float64\n",
            "cameraZ         float64\n",
            "orientationX    float64\n",
            "orientationY    float64\n",
            "orientationZ    float64\n",
            "orientationW    float64\n",
            "time            float64\n",
            "activity        float64\n",
            "sid             float64\n",
            "dtypes: float64(14), int64(1)\n",
            "memory usage: 259.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaTvjP_pQv6X",
        "colab_type": "code",
        "colab": {},
        "outputId": "4b873535-690a-41bc-c946-a124f5ef70d0"
      },
      "source": [
        "data['activity'].value_counts().plot(kind = 'bar', title = \"Activities\")\n",
        "plt.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
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          "metadata": {
            "tags": []
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          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mb-NQf58Qv6a",
        "colab_type": "code",
        "colab": {},
        "outputId": "ca13cacc-3972-41c1-aebe-1097179ad01e"
      },
      "source": [
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 1)]['time'], data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 1)]['depthX'])\n",
        "plt.plot(data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 1)]['time'], data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 1)]['depthY'])\n",
        "# plt.plot(data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 0)]['time'], data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 0)]['orientationZ'])\n",
        "# plt.plot(data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 0)]['time'], data[(data['sid'] == 0.0) & (data['activity'] == 1.0) & (data['jointType'] == 0)]['orientationW'])\n",
        "plt.legend(['depthX', 'depthY'])#, 'orientationZ', 'orientationW'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x2638013ffd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfejE3rAQv6c",
        "colab_type": "code",
        "colab": {},
        "outputId": "db831f40-8a9e-4fbc-f272-cac9394180e1"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['jointType', 'depthX', 'depthY', 'colorX', 'colorY', 'cameraX',\n",
              "       'cameraY', 'cameraZ', 'orientationX', 'orientationY', 'orientationZ',\n",
              "       'orientationW', 'time', 'activity', 'sid'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o79jKlHXQv6d",
        "colab_type": "code",
        "colab": {},
        "outputId": "59fdf34a-4d95-4471-bb61-bec2f04d1a5d"
      },
      "source": [
        "col = ['jointType', 'depthX', 'depthY', 'time', 'activity', 'sid']\n",
        "use_data = data[col].copy()\n",
        "use_data['activity'] = use_data['activity'].map({1:0, 3:1, 5:2, 7:3})\n",
        "use_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jointType</th>\n",
              "      <th>depthX</th>\n",
              "      <th>depthY</th>\n",
              "      <th>time</th>\n",
              "      <th>activity</th>\n",
              "      <th>sid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.632764</td>\n",
              "      <td>0.585317</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.629250</td>\n",
              "      <td>0.431639</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.627228</td>\n",
              "      <td>0.289618</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.652865</td>\n",
              "      <td>0.267710</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.599162</td>\n",
              "      <td>0.327201</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   jointType    depthX    depthY  time  activity  sid\n",
              "0          0  0.632764  0.585317  67.0       0.0  0.0\n",
              "1          1  0.629250  0.431639  67.0       0.0  0.0\n",
              "2          2  0.627228  0.289618  67.0       0.0  0.0\n",
              "3          3  0.652865  0.267710  67.0       0.0  0.0\n",
              "4          4  0.599162  0.327201  67.0       0.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9T4-Io9Qv6f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "use_data = use_data.groupby(['jointType', 'activity', 'sid', 'time'], as_index = False)[['depthX', 'depthY']].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EV4to3voQv6h",
        "colab_type": "code",
        "colab": {},
        "outputId": "7825073f-68d6-4665-d069-c4bc82079d70"
      },
      "source": [
        "use_data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 959575 entries, 0 to 959574\n",
            "Data columns (total 6 columns):\n",
            "jointType    959575 non-null int64\n",
            "activity     959575 non-null float64\n",
            "sid          959575 non-null float64\n",
            "time         959575 non-null float64\n",
            "depthX       959575 non-null float64\n",
            "depthY       959575 non-null float64\n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 51.2 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hssEzbolQv6j",
        "colab_type": "code",
        "colab": {},
        "outputId": "1e66b3a6-c0c5-41fc-de59-308562151f0f"
      },
      "source": [
        "use_data.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jointType</th>\n",
              "      <th>activity</th>\n",
              "      <th>sid</th>\n",
              "      <th>time</th>\n",
              "      <th>depthX</th>\n",
              "      <th>depthY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.632791</td>\n",
              "      <td>0.584554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.632764</td>\n",
              "      <td>0.585317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>0.631359</td>\n",
              "      <td>0.586576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>0.628691</td>\n",
              "      <td>0.587546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>0.620062</td>\n",
              "      <td>0.587712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>0.614487</td>\n",
              "      <td>0.582819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>257.0</td>\n",
              "      <td>0.613724</td>\n",
              "      <td>0.581864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>0.612480</td>\n",
              "      <td>0.580207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>329.0</td>\n",
              "      <td>0.613446</td>\n",
              "      <td>0.584220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>0.615885</td>\n",
              "      <td>0.582352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   jointType  activity  sid   time    depthX    depthY\n",
              "0          0       0.0  0.0   30.0  0.632791  0.584554\n",
              "1          0       0.0  0.0   67.0  0.632764  0.585317\n",
              "2          0       0.0  0.0  103.0  0.631359  0.586576\n",
              "3          0       0.0  0.0  145.0  0.628691  0.587546\n",
              "4          0       0.0  0.0  183.0  0.620062  0.587712\n",
              "5          0       0.0  0.0  222.0  0.614487  0.582819\n",
              "6          0       0.0  0.0  257.0  0.613724  0.581864\n",
              "7          0       0.0  0.0  294.0  0.612480  0.580207\n",
              "8          0       0.0  0.0  329.0  0.613446  0.584220\n",
              "9          0       0.0  0.0  365.0  0.615885  0.582352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fyx6j0pjQv6l",
        "colab_type": "code",
        "colab": {},
        "outputId": "903bdc18-ffb2-4a0f-9451-1aa46fb2f6b0"
      },
      "source": [
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(use_data[(use_data['jointType'] == 0) & (use_data['activity'] == 3) & (use_data['sid'] == 0)]['time'], use_data[(use_data['jointType'] == 0) & (use_data['activity'] == 3) & (use_data['sid'] == 0)]['depthX'])\n",
        "plt.plot(use_data[(use_data['jointType'] == 0) & (use_data['activity'] == 3) & (use_data['sid'] == 0)]['time'], use_data[(use_data['jointType'] == 0) & (use_data['activity'] == 3) & (use_data['sid'] == 0)]['depthY'])\n",
        "plt.legend(['depthX', 'depthY'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x263802f63c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vG14OjAFQv6n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = use_data[use_data['sid'] <= 10]\n",
        "test_data = use_data[use_data['sid'] > 10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0583H-NSQv6p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_segments_and_labels(df, time_steps, steps):\n",
        "    n_features = 3\n",
        "    segments = []\n",
        "    labels = []\n",
        "    for i in range(0, len(df) - time_steps, steps):\n",
        "        x = df['jointType'].values[i: i + time_steps]\n",
        "        y = df['depthX'].values[i: i + time_steps]\n",
        "        z = df['depthY'].values[i: i + time_steps]\n",
        "\n",
        "        label = stats.mode(df['activity'][i: i + time_steps])[0][0]\n",
        "        segments.append([x, y, z])\n",
        "        labels.append(label)\n",
        "    reshaped_segments = np.asarray(segments, dtype = np.float32).reshape(-1, time_steps, n_features)\n",
        "    labels = np.asarray(labels)\n",
        "    \n",
        "    return reshaped_segments, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8w9wyL2wQv6r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, Y_train = create_segments_and_labels(train_data, 80, 80)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waol3rBaQv6t",
        "colab_type": "code",
        "colab": {},
        "outputId": "a15d72ae-a36c-4685-c5a9-a7b87717a974"
      },
      "source": [
        "print('X_train shape: ', X_train.shape)\n",
        "print(X_train.shape[0], 'training samples')\n",
        "print('Y_train shape: ', Y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape:  (8213, 80, 3)\n",
            "8213 training samples\n",
            "Y_train shape:  (8213,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWlIPjO5Qv6v",
        "colab_type": "code",
        "colab": {},
        "outputId": "957030ee-f4c6-4791-f9c6-7ec22126c3b8"
      },
      "source": [
        "n_timesteps, n_features = X_train.shape[1], X_train.shape[2]\n",
        "print(\"n_timesteps: \", n_timesteps)\n",
        "print(\"n_features: \", n_features)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n_timesteps:  80\n",
            "n_features:  3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_hqxcycQv6x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_train = np_utils.to_categorical(Y_train, 4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsEujoVSQv6y",
        "colab_type": "code",
        "colab": {},
        "outputId": "6312f9b1-3e01-437b-aa89-7edf93c61c37"
      },
      "source": [
        "print(\"Y_train shape: \", Y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y_train shape:  (8213, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7Ye6Z7_Qv60",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_outputs = Y_train.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq8des86Qv61",
        "colab_type": "code",
        "colab": {},
        "outputId": "62835755-8585-4d3b-c751-f480df7d759e"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(filters = 128, kernel_size = 5, activation = 'relu', input_shape = (n_timesteps, n_features)))\n",
        "model.add(Conv1D(filters = 128, kernel_size = 5, activation = 'relu'))\n",
        "model.add(MaxPooling1D(pool_size = 3))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1000, activation = 'relu'))\n",
        "model.add(Dense(100, activation = 'relu'))\n",
        "model.add(Dense(n_outputs, activation = 'softmax'))\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_3 (Conv1D)            (None, 76, 128)           2048      \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 72, 128)           82048     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 24, 128)           0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 3072)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1000)              3073000   \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 100)               100100    \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 4)                 404       \n",
            "=================================================================\n",
            "Total params: 3,257,600\n",
            "Trainable params: 3,257,600\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5R0SyJ-Qv63",
        "colab_type": "code",
        "colab": {},
        "outputId": "eb5e0c6c-1376-4b6f-e4da-d3c634e6f099"
      },
      "source": [
        "model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
        "history = model.fit(X_train, Y_train, epochs = 10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "8213/8213 [==============================] - 32s 4ms/step - loss: 1.4063 - accuracy: 0.2563\n",
            "Epoch 2/10\n",
            "8213/8213 [==============================] - 31s 4ms/step - loss: 1.3862 - accuracy: 0.2579\n",
            "Epoch 3/10\n",
            "8213/8213 [==============================] - 31s 4ms/step - loss: 1.3863 - accuracy: 0.2541\n",
            "Epoch 4/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3862 - accuracy: 0.2589\n",
            "Epoch 5/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3863 - accuracy: 0.2589\n",
            "Epoch 6/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3862 - accuracy: 0.2589\n",
            "Epoch 7/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3863 - accuracy: 0.2589\n",
            "Epoch 8/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3863 - accuracy: 0.2589\n",
            "Epoch 9/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3862 - accuracy: 0.2589\n",
            "Epoch 10/10\n",
            "8213/8213 [==============================] - 30s 4ms/step - loss: 1.3862 - accuracy: 0.2589\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dv5t-a5JQv65",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}