Classification_of_Hand_Posture_Analysis_Using_a_V_ELM.ipynb

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    "colab": {
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<h1>1. Import Dataset</h1>"
      ],
      "metadata": {
        "id": "jmPaAg5xoGFv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNBo83wulp9a",
        "outputId": "b5fe5a6f-02ba-4569-f558-0cd93cbbb361"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-08-02 06:09:05--  https://docs.google.com/uc?export=download&id=1JVZLOn1BJ7oKwMTaAgk4aM8tEAZsqpWI\n",
            "Resolving docs.google.com (docs.google.com)... 142.250.159.102, 142.250.159.113, 142.250.159.101, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.250.159.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-04-5c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/ulq8n4bpup9v29s2lvtjog30jml9lqnm/1690956525000/07590719626580236638/*/1JVZLOn1BJ7oKwMTaAgk4aM8tEAZsqpWI?e=download&uuid=e7b66849-e390-42fe-88e7-4ca5ae83ef75 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-08-02 06:09:07--  https://doc-04-5c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/ulq8n4bpup9v29s2lvtjog30jml9lqnm/1690956525000/07590719626580236638/*/1JVZLOn1BJ7oKwMTaAgk4aM8tEAZsqpWI?e=download&uuid=e7b66849-e390-42fe-88e7-4ca5ae83ef75\n",
            "Resolving doc-04-5c-docs.googleusercontent.com (doc-04-5c-docs.googleusercontent.com)... 142.251.172.132, 2607:f8b0:4001:c61::84\n",
            "Connecting to doc-04-5c-docs.googleusercontent.com (doc-04-5c-docs.googleusercontent.com)|142.251.172.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 33961749 (32M) [text/csv]\n",
            "Saving to: ‘MoCapHandPostures.csv’\n",
            "\n",
            "MoCapHandPostures.c 100%[===================>]  32.39M  67.2MB/s    in 0.5s    \n",
            "\n",
            "2023-08-02 06:09:08 (67.2 MB/s) - ‘MoCapHandPostures.csv’ saved [33961749/33961749]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1JVZLOn1BJ7oKwMTaAgk4aM8tEAZsqpWI' -O MoCapHandPostures.csv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statistics"
      ],
      "metadata": {
        "id": "8P8-VMvR_0HR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('MoCapHandPostures.csv')\n",
        "data = data.iloc[1: , :]"
      ],
      "metadata": {
        "id": "cRIQXT4fl69a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "data"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "uZ2fFrq0mdpq",
        "outputId": "6e8bb20d-4004-4f5a-b52b-d0f506ab303c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
              "                     X9                Y9                 Z9 X10 Y10 Z10 X11  \\\n",
              "1                     ?                 ?                  ?   ?   ?   ?   ?   \n",
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              "...                 ...               ...                ...  ..  ..  ..  ..   \n",
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              "\n",
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              "...    ..  ..  \n",
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              "78092   ?   ?  \n",
              "78093   ?   ?  \n",
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              "78095   ?   ?  \n",
              "\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>2. Imputasi Missing Value</h1>"
      ],
      "metadata": {
        "id": "McrI_fcloOho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.replace('?',np.nan)"
      ],
      "metadata": {
        "id": "9QP6VTS-oFDb"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
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        "id": "dCDoISn0ozNA",
        "outputId": "eb876412-4214-4c4b-8d3c-7f3ceac294ee"
      },
      "execution_count": null,
      "outputs": [
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>78094</th>\n",
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              "      <td>27.915311</td>\n",
              "      <td>108.007390</td>\n",
              "      <td>10.814957</td>\n",
              "      <td>-0.910435</td>\n",
              "      <td>122.464093</td>\n",
              "      <td>-47.271248</td>\n",
              "      <td>-30.084588</td>\n",
              "      <td>77.705861</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78095</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>27.898705</td>\n",
              "      <td>108.092877</td>\n",
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              "      <td>-30.031402</td>\n",
              "      <td>77.740235</td>\n",
              "      <td>-17.453099</td>\n",
              "      <td>-1.091566</td>\n",
              "      <td>122.827638</td>\n",
              "      <td>...</td>\n",
              "      <td>-63.0265744155222</td>\n",
              "      <td>78.8796525666014</td>\n",
              "      <td>31.6030997578398</td>\n",
              "      <td>-47.0137357205196</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>78095 rows × 38 columns</p>\n",
              "</div>\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ea77bf4-abd3-46d7-b679-cd393079eb7a');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "def imputasi(df_input):\n",
        "  list_columns = df_input.columns[2:]\n",
        "  class_columns = df_input.columns[0]\n",
        "  df_input.dropna(thresh=11, axis=1)\n",
        "  for column in list_columns:\n",
        "    df_input[column] = pd.to_numeric(df_input[column])\n",
        "    val = df_input.groupby(class_columns)[column]\n",
        "    df_input[column] = df_input[column].fillna(value=val.transform('mean'))\n",
        "  if df_input.isnull().any().any():\n",
        "    for column in list_columns:\n",
        "      val = df_input.groupby('User')[column]\n",
        "      df_input[column] = df_input[column].fillna(value=val.transform('mean'))\n",
        "  return df_input"
      ],
      "metadata": {
        "id": "Z2bsvALfpz8m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_impu = imputasi(data)"
      ],
      "metadata": {
        "id": "-3LJs2vXp0tI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_impu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "sYKXB8WEqqxS",
        "outputId": "e87bb139-42f4-49dd-9d54-3ed11bba1e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Class  User         X0          Y0         Z0         X1          Y1  \\\n",
              "1          1     0  54.263880   71.466776 -64.807709  76.895635   42.462500   \n",
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              "4          1     0  55.329647   71.707275 -63.688956  36.561863   81.868749   \n",
              "5          1     0  55.142401   71.435607 -64.177303  36.175818   81.556874   \n",
              "...      ...   ...        ...         ...        ...        ...         ...   \n",
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              "78092      5    14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n",
              "78093      5    14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n",
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              "\n",
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              "78092  11.069842 -30.334054   77.858214  ... -62.130563  78.229847  33.078407   \n",
              "78093  11.020347 -22.574718  104.222208  ...   3.835904  78.591554  32.327738   \n",
              "78094 -47.271248 -30.084588   77.705861  ... -63.221626  50.793322  91.949619   \n",
              "78095 -17.453099  -1.091566  122.827638  ... -63.026574  78.879653  31.603100   \n",
              "\n",
              "              Z9        X10        Y10        Z10        X11        Y11  \\\n",
              "1     -10.663720  54.057026  76.311915 -27.775952 -29.697613  25.963332   \n",
              "2     -10.663720  54.057026  76.311915 -27.775952 -29.697613  25.963332   \n",
              "3     -10.663720  54.057026  76.311915 -27.775952 -29.697613  25.963332   \n",
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              "78095 -47.013736  57.027873  85.807738 -32.768163 -29.697613  25.963332   \n",
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              "      <td>71.435607</td>\n",
              "      <td>-64.177303</td>\n",
              "      <td>36.175818</td>\n",
              "      <td>81.556874</td>\n",
              "      <td>-53.475747</td>\n",
              "      <td>76.986143</td>\n",
              "      <td>42.426849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.077380</td>\n",
              "      <td>37.172842</td>\n",
              "      <td>87.562493</td>\n",
              "      <td>-10.663720</td>\n",
              "      <td>54.057026</td>\n",
              "      <td>76.311915</td>\n",
              "      <td>-27.775952</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78091</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>54.251127</td>\n",
              "      <td>129.177414</td>\n",
              "      <td>-44.252511</td>\n",
              "      <td>27.720784</td>\n",
              "      <td>107.810661</td>\n",
              "      <td>11.099282</td>\n",
              "      <td>-1.270139</td>\n",
              "      <td>122.758679</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.543115</td>\n",
              "      <td>87.733364</td>\n",
              "      <td>61.216363</td>\n",
              "      <td>-62.126278</td>\n",
              "      <td>57.027873</td>\n",
              "      <td>85.807738</td>\n",
              "      <td>-32.768163</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78092</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>54.334883</td>\n",
              "      <td>129.253842</td>\n",
              "      <td>-44.016320</td>\n",
              "      <td>27.767911</td>\n",
              "      <td>107.914808</td>\n",
              "      <td>11.069842</td>\n",
              "      <td>-30.334054</td>\n",
              "      <td>77.858214</td>\n",
              "      <td>...</td>\n",
              "      <td>-62.130563</td>\n",
              "      <td>78.229847</td>\n",
              "      <td>33.078407</td>\n",
              "      <td>-45.249543</td>\n",
              "      <td>57.027873</td>\n",
              "      <td>85.807738</td>\n",
              "      <td>-32.768163</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78093</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>54.151540</td>\n",
              "      <td>129.269502</td>\n",
              "      <td>-44.173273</td>\n",
              "      <td>27.725978</td>\n",
              "      <td>108.034006</td>\n",
              "      <td>11.020347</td>\n",
              "      <td>-22.574718</td>\n",
              "      <td>104.222208</td>\n",
              "      <td>...</td>\n",
              "      <td>3.835904</td>\n",
              "      <td>78.591554</td>\n",
              "      <td>32.327738</td>\n",
              "      <td>-46.166556</td>\n",
              "      <td>57.027873</td>\n",
              "      <td>85.807738</td>\n",
              "      <td>-32.768163</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78094</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>27.915311</td>\n",
              "      <td>108.007390</td>\n",
              "      <td>10.814957</td>\n",
              "      <td>-0.910435</td>\n",
              "      <td>122.464093</td>\n",
              "      <td>-47.271248</td>\n",
              "      <td>-30.084588</td>\n",
              "      <td>77.705861</td>\n",
              "      <td>...</td>\n",
              "      <td>-63.221626</td>\n",
              "      <td>50.793322</td>\n",
              "      <td>91.949619</td>\n",
              "      <td>-30.884241</td>\n",
              "      <td>57.027873</td>\n",
              "      <td>85.807738</td>\n",
              "      <td>-32.768163</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78095</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>27.898705</td>\n",
              "      <td>108.092877</td>\n",
              "      <td>11.107857</td>\n",
              "      <td>-30.031402</td>\n",
              "      <td>77.740235</td>\n",
              "      <td>-17.453099</td>\n",
              "      <td>-1.091566</td>\n",
              "      <td>122.827638</td>\n",
              "      <td>...</td>\n",
              "      <td>-63.026574</td>\n",
              "      <td>78.879653</td>\n",
              "      <td>31.603100</td>\n",
              "      <td>-47.013736</td>\n",
              "      <td>57.027873</td>\n",
              "      <td>85.807738</td>\n",
              "      <td>-32.768163</td>\n",
              "      <td>-29.697613</td>\n",
              "      <td>25.963332</td>\n",
              "      <td>1.697312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>78095 rows × 38 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88ed7bdf-1cc2-454b-afcd-53469461b5d8')\"\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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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-06153c2c-6df7-4c93-810e-4748b87278a2')\"\n",
              "              title=\"Suggest charts.\"\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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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-06153c2c-6df7-4c93-810e-4748b87278a2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-88ed7bdf-1cc2-454b-afcd-53469461b5d8 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-88ed7bdf-1cc2-454b-afcd-53469461b5d8');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_impu = data_impu.drop(columns=['User'])"
      ],
      "metadata": {
        "id": "8xZ7X9t4rhX-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def minmax(df_input):\n",
        "  list_fitur = df_input.columns[1:]\n",
        "  for fitur in list_fitur:\n",
        "    grouper = df_input.groupby('Class')[fitur]\n",
        "    maxes = grouper.transform('max')\n",
        "    mins = grouper.transform('min')\n",
        "    df_input = df_input.assign(fitur=(df_input[fitur] - mins)/(maxes - mins))\n",
        "  #df_input = df_input.groupby('Class').transform(lambda x: (x - x.min()) / x.max()- x.min())\n",
        "  #for fitur in list_fitur:\n",
        "    #max = df_input.groupby('Class')[fitur].max()\n",
        "    #min = df_input.groupby('Class')[fitur].min()\n",
        "    #df_input[fitur] = (df_input[fitur]-min)/(max-min)\n",
        "  return df_input"
      ],
      "metadata": {
        "id": "F3M4oplKrgWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>3. Pemisahan Data Latih</h1>"
      ],
      "metadata": {
        "id": "FOHC4oRQsMXY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train70, test30 = train_test_split(data_impu, test_size=0.3)\n",
        "train80, test20 = train_test_split(data_impu, test_size=0.2)\n",
        "\n",
        "trNom70 = minmax(train70)\n",
        "trNom80 = minmax(train80)\n",
        "teNom30 = minmax(test30)\n",
        "teNom20 = minmax(test20)"
      ],
      "metadata": {
        "id": "Loh7IsDhsdZ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats as s\n",
        "class voteELM:\n",
        "  W = []\n",
        "  beta = []\n",
        "  mape = []\n",
        "\n",
        "  def __fitELM(self,train,h,b1,b2):\n",
        "    t = train['Class']\n",
        "    t = np.stack(t.values)\n",
        "    train = train.drop(columns=['Class'])\n",
        "    d = len(train.columns)\n",
        "    X = train.values\n",
        "    W = np.random.uniform(b1,b2, (h,d))\n",
        "    Hinit = X @ W.T\n",
        "    H = 1/(1+np.exp(-1*Hinit))\n",
        "    Hplus = np.linalg.inv(H.T @ H) @ H.T\n",
        "    beta = Hplus @ t\n",
        "    y = H @ beta\n",
        "    return W, beta, t, y\n",
        "\n",
        "  def fit(self,train,k=5,h=4,b1=-0.5,b2=0.5):\n",
        "    for i in range(k):\n",
        "      wf, bf, tf, yf = self.__fitELM(train,h,b1,b2)\n",
        "      self.W.append(wf)\n",
        "      self.beta.append(bf)\n",
        "      MAPE = sum(abs(yf-tf)/yf)*1/len(tf)\n",
        "      self.mape.append(MAPE)\n",
        "\n",
        "  def test(self,test):\n",
        "    t = test['Class']\n",
        "    t = np.stack(t.values)\n",
        "    y = []\n",
        "    yt = []\n",
        "    vote = []\n",
        "    Xt = test.drop(columns=['Class'])\n",
        "    Xt = Xt.values\n",
        "\n",
        "    for idx,e in enumerate(self.W):\n",
        "      Hinit = Xt @ e.T\n",
        "      H = 1/(1+np.exp(-1*Hinit))\n",
        "      yt = H @ self.beta[idx]\n",
        "      yt = np.around(yt)\n",
        "      y.append(yt)\n",
        "\n",
        "    yt = list(zip(*y))\n",
        "\n",
        "    pred = []\n",
        "    for x in yt:\n",
        "      mode = int(s.mode(x)[0])\n",
        "      pred.append(mode)\n",
        "    return pred"
      ],
      "metadata": {
        "id": "Sf3zYAUxwubu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "model = voteELM()\n",
        "model1 = voteELM()\n",
        "\n",
        "st1 = time.time()\n",
        "model.fit(trNom80,5,500)\n",
        "et1 = time.time()\n",
        "\n",
        "st2 = time.time()\n",
        "model1.fit(trNom70,5,500)\n",
        "et2 = time.time()\n",
        "\n",
        "pred = model.test(teNom20)\n",
        "pred1 = model1.test(teNom30)"
      ],
      "metadata": {
        "id": "oB0jZpnK5Dp_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c95e8c01-8a0b-4be8-e375-8a05194e7780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-02a16a6c810a>:49: 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",
            "  mode = int(s.mode(x)[0])\n",
            "<ipython-input-14-02a16a6c810a>:49: 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",
            "  mode = int(s.mode(x)[0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_test = teNom20['Class'].tolist()\n",
        "y_test1 = teNom30['Class'].tolist()\n",
        "\n",
        "print(\"Akurasi V-ELM dengan rasio pelatihan 80:20 : \",accuracy_score(y_test,pred))\n",
        "print(\"Akurasi V-ELM dengan rasio pelatihan 70:30 : \",accuracy_score(y_test1,pred1))\n",
        "print(\"Waktu Pelatihan V-ELM dengan rasio pelatihan 80:20 : \",et1-st1)\n",
        "print(\"Waktu Pelatihan V-ELM dengan rasio pelatihan 70:30 : \",et2-st2)\n",
        "print(\"Nilai MAPE pada Pelatihan 80:20 : \", model.mape[:5])\n",
        "print(\"Nilai MAPE pada Pelatihan 70:30 : \", model1.mape[5:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvEQJRbHXn0b",
        "outputId": "25b13f2d-b014-494c-cdaa-1121d95f9ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi V-ELM dengan rasio pelatihan 80:20 :  0.615660413598822\n",
            "Akurasi V-ELM dengan rasio pelatihan 70:30 :  0.6119766101839601\n",
            "Waktu Pelatihan V-ELM dengan rasio pelatihan 80:20 :  15.844164609909058\n",
            "Waktu Pelatihan V-ELM dengan rasio pelatihan 70:30 :  14.762497425079346\n",
            "Nilai MAPE pada Pelatihan 80:20 :  [0.24149344539444917, 0.23489746367239553, 0.2319417122857419, 0.21667234169943092, 0.2141022776585014]\n",
            "Nilai MAPE pada Pelatihan 70:30 :  [0.28229316252088354, 0.2413669358604659, 0.23360497552094775, 0.2513853299747013, 0.22891524507410801]\n"
          ]
        }
      ]
    }
  ]
}