MadalineNetwork.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 2.2. MadaLine"
      ],
      "metadata": {
        "id": "mjvsQeT9r4O2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2.B. Define and Plot Data"
      ],
      "metadata": {
        "id": "legc7v9FJTfM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.642596Z",
          "iopub.execute_input": "2022-11-01T00:14:18.642944Z",
          "iopub.status.idle": "2022-11-01T00:14:18.650969Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.642915Z",
          "shell.execute_reply": "2022-11-01T00:14:18.649982Z"
        },
        "trusted": true,
        "id": "wHt2BL-dfPrE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown 1NRwDhqrhXF8tRy1HKe9QofJZjPBnHV1r"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8t21627PrIGw",
        "outputId": "dd7824bf-7b63-41ff-861b-32093f1f790b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.9/dist-packages (4.4.0)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.6.4-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from gdown) (3.9.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from gdown) (4.9.3)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.9/dist-packages (from gdown) (2.25.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from gdown) (4.65.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->gdown) (2.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.26.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.9/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\n",
            "Successfully installed gdown-4.6.4\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1NRwDhqrhXF8tRy1HKe9QofJZjPBnHV1r\n",
            "To: /content/MadaLine.csv\n",
            "100% 8.69k/8.69k [00:00<00:00, 10.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/MadaLine.csv',names=['x', 'y','label'],header=None)\n",
        "df"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.705101Z",
          "iopub.execute_input": "2022-11-01T00:14:18.705481Z",
          "iopub.status.idle": "2022-11-01T00:14:18.724664Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.705454Z",
          "shell.execute_reply": "2022-11-01T00:14:18.723706Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tG2NnMzafPrL",
        "outputId": "30c3ed5b-5873-46f9-f85d-838e7570196f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            x         y  label\n",
              "0   -0.642823  0.720606    0.0\n",
              "1   -0.218126  0.677263    0.0\n",
              "2   -0.582930 -0.347496    0.0\n",
              "3    0.285127  0.091750    0.0\n",
              "4   -0.335577 -0.313893    0.0\n",
              "..        ...       ...    ...\n",
              "195  0.744066 -1.206548    1.0\n",
              "196 -0.457547  1.286227    1.0\n",
              "197 -1.020000 -0.783926    1.0\n",
              "198  1.363429 -0.800250    1.0\n",
              "199 -1.246702 -0.388615    1.0\n",
              "\n",
              "[200 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f37c42fb-40e8-45dd-a940-a164d061a444\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>x</th>\n",
              "      <th>y</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.642823</td>\n",
              "      <td>0.720606</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.218126</td>\n",
              "      <td>0.677263</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.582930</td>\n",
              "      <td>-0.347496</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.285127</td>\n",
              "      <td>0.091750</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.335577</td>\n",
              "      <td>-0.313893</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>0.744066</td>\n",
              "      <td>-1.206548</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>-0.457547</td>\n",
              "      <td>1.286227</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>-1.020000</td>\n",
              "      <td>-0.783926</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>1.363429</td>\n",
              "      <td>-0.800250</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>-1.246702</td>\n",
              "      <td>-0.388615</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f37c42fb-40e8-45dd-a940-a164d061a444')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f37c42fb-40e8-45dd-a940-a164d061a444 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-f37c42fb-40e8-45dd-a940-a164d061a444');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sample(frac = 1)\n",
        "df"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.743181Z",
          "iopub.execute_input": "2022-11-01T00:14:18.743643Z",
          "iopub.status.idle": "2022-11-01T00:14:18.757785Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.743608Z",
          "shell.execute_reply": "2022-11-01T00:14:18.756861Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5GdrI2rUfPrN",
        "outputId": "ac29740f-4a0b-432f-9ea5-c21072689262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            x         y  label\n",
              "14  -0.469356 -0.474269    0.0\n",
              "188 -0.677537  1.091894    1.0\n",
              "157  1.540464  0.062636    1.0\n",
              "179  0.369713 -1.186841    1.0\n",
              "62  -0.682580 -0.134757    0.0\n",
              "..        ...       ...    ...\n",
              "6   -0.639411 -0.171208    0.0\n",
              "192  0.568141  1.437551    1.0\n",
              "99  -0.744701 -0.432392    0.0\n",
              "96  -0.171735 -0.881529    0.0\n",
              "166  1.209089  0.294275    1.0\n",
              "\n",
              "[200 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41bcb912-e106-4212-8788-6eddbf5e41c9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>x</th>\n",
              "      <th>y</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-0.469356</td>\n",
              "      <td>-0.474269</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>-0.677537</td>\n",
              "      <td>1.091894</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>1.540464</td>\n",
              "      <td>0.062636</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0.369713</td>\n",
              "      <td>-1.186841</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>-0.682580</td>\n",
              "      <td>-0.134757</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.639411</td>\n",
              "      <td>-0.171208</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>0.568141</td>\n",
              "      <td>1.437551</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>-0.744701</td>\n",
              "      <td>-0.432392</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>-0.171735</td>\n",
              "      <td>-0.881529</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>1.209089</td>\n",
              "      <td>0.294275</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41bcb912-e106-4212-8788-6eddbf5e41c9')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41bcb912-e106-4212-8788-6eddbf5e41c9 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-41bcb912-e106-4212-8788-6eddbf5e41c9');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df[['x','y']]\n",
        "inputs = data.to_numpy()\n",
        "inputs.shape[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.759490Z",
          "iopub.execute_input": "2022-11-01T00:14:18.760182Z",
          "iopub.status.idle": "2022-11-01T00:14:18.772569Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.760139Z",
          "shell.execute_reply": "2022-11-01T00:14:18.771617Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qOcomnHfPrO",
        "outputId": "f3832836-9950-4e4e-fec9-4992879ac34a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to Bipolar\n",
        "label = df[['label']]\n",
        "target = label.to_numpy()\n",
        "target[np.isclose(target, 0)] = -1"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.851419Z",
          "iopub.execute_input": "2022-11-01T00:14:18.852408Z",
          "iopub.status.idle": "2022-11-01T00:14:18.858739Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.852370Z",
          "shell.execute_reply": "2022-11-01T00:14:18.857761Z"
        },
        "trusted": true,
        "id": "p9_BHGZ2fPrP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = df.loc[df['label']  == 0 ]\n",
        "df1 = df.loc[df['label']  == 1]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.923239Z",
          "iopub.execute_input": "2022-11-01T00:14:18.923729Z",
          "iopub.status.idle": "2022-11-01T00:14:18.928774Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.923701Z",
          "shell.execute_reply": "2022-11-01T00:14:18.928089Z"
        },
        "trusted": true,
        "id": "VlasqFo-fPrP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# scatter plot\n",
        "plt.scatter(df0['x'], df0['y'], c=\"red\", linewidths=2)\n",
        "plt.scatter(df1['x'], df1['y'], c=\"blue\", linewidths=2)\n",
        "\n",
        "# add axis labels and legend\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend([\"Class 1\", \"Class 2\"])\n",
        "\n",
        "# add grid\n",
        "plt.grid(True)\n",
        "\n",
        "# add title\n",
        "plt.title(\"Scatter Plot\")\n",
        "\n",
        "# adjust subplot spacing\n",
        "plt.tight_layout()\n",
        "\n",
        "# save the figure as PDF\n",
        "plt.savefig(\"scatter_plot.pdf\")\n",
        "\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:18.932687Z",
          "iopub.execute_input": "2022-11-01T00:14:18.933050Z",
          "iopub.status.idle": "2022-11-01T00:14:19.104305Z",
          "shell.execute_reply.started": "2022-11-01T00:14:18.933019Z",
          "shell.execute_reply": "2022-11-01T00:14:19.103250Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "UM_VUL_zfPrQ",
        "outputId": "12888e0a-6dd9-400d-f836-2e801e196243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sample(frac=1, random_state=5).reset_index()\n",
        "df"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-01T00:14:19.106306Z",
          "iopub.execute_input": "2022-11-01T00:14:19.107199Z",
          "iopub.status.idle": "2022-11-01T00:14:19.122874Z",
          "shell.execute_reply.started": "2022-11-01T00:14:19.107161Z",
          "shell.execute_reply": "2022-11-01T00:14:19.121731Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "SyWlWFiIfPrR",
        "outputId": "415c16c5-9f9b-4573-fcd1-69997b3c057a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     index         x         y  label\n",
              "0       92  0.155789  0.768451    0.0\n",
              "1       26 -0.032163  0.179866    0.0\n",
              "2      115 -1.093754  1.079181    1.0\n",
              "3       57  0.546561  0.438163    0.0\n",
              "4      187  0.326746  1.271150    1.0\n",
              "..     ...       ...       ...    ...\n",
              "195    118  0.796210  0.990588    1.0\n",
              "196     65  0.513081  0.298807    0.0\n",
              "197     88  0.484549  0.590856    0.0\n",
              "198    145  0.814315 -1.067399    1.0\n",
              "199      0 -0.642823  0.720606    0.0\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3adb683d-dfcd-4f77-96f9-c68bf54c6b42\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>index</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>92</td>\n",
              "      <td>0.155789</td>\n",
              "      <td>0.768451</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26</td>\n",
              "      <td>-0.032163</td>\n",
              "      <td>0.179866</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>115</td>\n",
              "      <td>-1.093754</td>\n",
              "      <td>1.079181</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>57</td>\n",
              "      <td>0.546561</td>\n",
              "      <td>0.438163</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>187</td>\n",
              "      <td>0.326746</td>\n",
              "      <td>1.271150</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>118</td>\n",
              "      <td>0.796210</td>\n",
              "      <td>0.990588</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>65</td>\n",
              "      <td>0.513081</td>\n",
              "      <td>0.298807</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>88</td>\n",
              "      <td>0.484549</td>\n",
              "      <td>0.590856</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>145</td>\n",
              "      <td>0.814315</td>\n",
              "      <td>-1.067399</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.642823</td>\n",
              "      <td>0.720606</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3adb683d-dfcd-4f77-96f9-c68bf54c6b42')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3adb683d-dfcd-4f77-96f9-c68bf54c6b42 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-3adb683d-dfcd-4f77-96f9-c68bf54c6b42');\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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2.C. MRI Implementation"
      ],
      "metadata": {
        "id": "5_KLtWRAJbft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_decision_boundary(start_x, end_x, weights, biases):\n",
        "    \"\"\"\n",
        "    Calculates the decision boundary given the start and end x values, weights, and biases.\n",
        "    \n",
        "    Args:\n",
        "    start_x (float): The start x value.\n",
        "    end_x (float): The end x value.\n",
        "    weights (numpy.ndarray): An array of weights.\n",
        "    biases (float): The bias value.\n",
        "    \n",
        "    Returns:\n",
        "    inputs (numpy.ndarray): An array of x values.\n",
        "    output (numpy.ndarray): An array of y values representing the decision boundary.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Create an array of x values\n",
        "    inputs = np.linspace(start_x, end_x)\n",
        "    \n",
        "    # Calculate the corresponding y values using the given weights and biases\n",
        "    output = -(weights[0] * inputs + biases)\n",
        "    output = output / weights[1]\n",
        "    \n",
        "    return inputs, output\n",
        "\n",
        "\n",
        "def initialize_weights(sm, num_neurons_layer1, num_neurons_layer2):\n",
        "    # Set a random seed for reproducibility\n",
        "    np.random.seed(10)\n",
        "    \n",
        "    # Generate random weights and biases for the first layer\n",
        "    weights = np.random.rand(num_neurons_layer1, num_neurons_layer2) * sm\n",
        "    biases = np.zeros((num_neurons_layer1, 1))\n",
        "    \n",
        "    # Initialize weights and biases for the second layer\n",
        "    # The weights for the second layer are initialized to 1\n",
        "    weights_layer2 = np.array([[1]*num_neurons_layer1])\n",
        "    biases_layer2 = num_neurons_layer1 - 1\n",
        "    \n",
        "    # Return all the initialized weights and biases\n",
        "    return weights, biases, weights_layer2, biases_layer2\n",
        "\n",
        "def apply_activation_function(net):\n",
        "    # np.where(condition, x, y) returns an array with the same shape as condition, \n",
        "    # where the elements are taken from x where condition is True, \n",
        "    # and from y elsewhere. In this case, the condition is whether each element of \n",
        "    # the input net is greater than or equal to 0. If it is, the corresponding element \n",
        "    # in the output h is set to 1; otherwise, it is set to -1.\n",
        "    h = np.where(net >= 0, 1, -1)\n",
        "    return h\n",
        "\n",
        "def forward_propagation(weights, inputs, biases, should_reshape):\n",
        "    # Check if inputs should be reshaped\n",
        "    if should_reshape:\n",
        "        inputs = inputs.reshape((2, 1))\n",
        "    # Calculate the net input\n",
        "    net_input = np.dot(weights, inputs) + biases\n",
        "    # Apply activation function to net input to obtain outputs\n",
        "    outputs = apply_activation_function(net_input)\n",
        "    # Return both net input and outputs\n",
        "    return net_input, outputs\n",
        "\n",
        "def update_weights(weights, biases, inputs, target, net_input, output, learning_rate, num_neurons_layer1):\n",
        "    # Reshape inputs and net_input\n",
        "    inputs = inputs.reshape((1, 2))\n",
        "    net_input = net_input.reshape((num_neurons_layer1, 1))\n",
        "\n",
        "    # If target is equal to output, no weight or bias update is necessary\n",
        "    if target == output:\n",
        "        return weights, biases\n",
        "    # If target is 1 and output is -1, update weights and biases for the neuron with the highest net input\n",
        "    elif target == 1 and target != output:    #output=-1 but target=1\n",
        "        argmax_neuron = np.argmax(net_input)\n",
        "        diff_bias = learning_rate * (1 - net_input[argmax_neuron])\n",
        "        diff_weight = learning_rate * np.dot((1 - net_input[argmax_neuron]), inputs)\n",
        "        biases[argmax_neuron] = biases[argmax_neuron] + diff_bias\n",
        "        weights[argmax_neuron] = weights[argmax_neuron] + diff_weight\n",
        "    # If target is -1 and output is 1, update weights and biases for all neurons with positive net input\n",
        "    elif target == -1 and target != output:   # output=1 but target=-1\n",
        "        positive_indices = np.argwhere(net_input > 0)\n",
        "        diff_bias = learning_rate * (-1 - net_input)\n",
        "        diff_weight = learning_rate * np.dot((-1 - net_input), inputs)\n",
        "        new_biases = biases + diff_bias\n",
        "        new_weights = weights + diff_weight\n",
        "        for i in positive_indices[:, 0]:\n",
        "            weights[i] = new_weights[i]\n",
        "            biases[i] = new_biases[i]\n",
        "    # Return updated weights and biases\n",
        "    return weights, biases\n",
        "\n",
        "# Define a function named calculate_error\n",
        "def calculate_error(target, output):\n",
        "    # Calculate the error using the mean squared error formula\n",
        "    error = 0.5 * np.power((target - output), 2)\n",
        "    # Return the calculated error\n",
        "    return error\n",
        "\n",
        "def predict(inputs, target, weights, biases, num_neurons_layer1):\n",
        "    # initialize an empty list to store predicted outputs\n",
        "    predicted_output = []\n",
        "    \n",
        "    # initialize biases_layer2 as a numpy array of zeros\n",
        "    biases_layer2 = np.zeros((num_neurons_layer1, 1))\n",
        "    \n",
        "    # initialize weights_layer2 as a numpy array of shape (1, num_neurons_layer1) with all elements as 1\n",
        "    weights_layer2 = np.array([[1]*num_neurons_layer1])\n",
        "    \n",
        "    # update biases_layer2 to have a value of num_neurons_layer1 - 1\n",
        "    biases_layer2 = num_neurons_layer1 - 1\n",
        "    \n",
        "    # loop through each input and calculate the predicted output using forward propagation\n",
        "    for i in range(inputs.shape[0]):\n",
        "        # call the forward_propagation function with inputs, weights, biases, and should_reshape=True\n",
        "        net_input, outputs = forward_propagation(weights, inputs[i], biases, should_reshape=True)\n",
        "        \n",
        "        # call the forward_propagation function with weights_layer2, outputs, biases_layer2, and should_reshape=False\n",
        "        net_input2, output = forward_propagation(weights_layer2, outputs, biases_layer2, should_reshape=False)\n",
        "        \n",
        "        # append the predicted output to the predicted_output list\n",
        "        predicted_output.append(output[0])\n",
        "    \n",
        "    # return the list of predicted outputs\n",
        "    return predicted_output"
      ],
      "metadata": {
        "id": "XLZMW9ChDLcK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def MRI(df0, df1, inputs, target, num_neurons_layer1=3, num_neurons_layer2=2, learning_rate=0.0001, max_iter=200, samples=None, plot=True):\n",
        "    # If samples is not provided, set it to the number of rows in the input data.\n",
        "    if samples is None:\n",
        "        samples = inputs.shape[0]\n",
        "    # Print the number of samples.\n",
        "    print('sample:', samples)\n",
        "    # Initialize variables\n",
        "    sm = 0.001\n",
        "    error_list = []\n",
        "    errors = []\n",
        "    mean_error = 10**3\n",
        "    weights, biases, weights_layer2, biases_layer2 = initialize_weights(sm, num_neurons_layer1, num_neurons_layer2)  # Step 0\n",
        "    # Iterate through the training process\n",
        "    for i in range(max_iter):\n",
        "        # Perform forward propagation\n",
        "        net_input, outputs = forward_propagation(weights, inputs[i % samples], biases, should_reshape=True)  # Step 4 and 5\n",
        "        net_input2, output = forward_propagation(weights_layer2, outputs, biases_layer2, should_reshape=False)  # Step 6\n",
        "        # Calculate the error of the output\n",
        "        error = calculate_error(target[i % samples], output)\n",
        "        errors.append(error)\n",
        "        # If an epoch has ended, calculate the mean error of the epoch and append it to the error list\n",
        "        if i % samples == 0 and i != 0:\n",
        "            mean_error = np.mean(errors)\n",
        "            error_list.append(mean_error)\n",
        "            errors = []\n",
        "            # Print the epoch number and the mean error of the epoch\n",
        "            print('Epoch %d / %d' % (len(error_list), int(max_iter / samples)))\n",
        "            print('loss:', mean_error)\n",
        "            for j in range(len(weights)):\n",
        "                # Print the weights and biases for each layer\n",
        "                print('W%d:'%(j+1),weights[j])\n",
        "                print('b%d:'%(j+1),biases[j])\n",
        "        # If the mean error is 0 or the difference between the mean error of the last two epochs is 0, an early stop occurred\n",
        "        if mean_error == 0 or (i > 50 and len(error_list) >= 2 and error_list[-1] - error_list[-2] == 0):\n",
        "            print('An early stop occurred!')\n",
        "            # If plot is True, plot the error, decision boundary, confusion matrix, and classification report\n",
        "            if plot:\n",
        "                plt.plot(error_list)\n",
        "                plt.xlabel('Epochs')\n",
        "                plt.ylabel('Mean Squared Error')\n",
        "                plt.title('Error Plot')\n",
        "                plt.grid(True)\n",
        "                plt.savefig('error_plot.pdf')\n",
        "                plt.show()\n",
        "                df0 = df0\n",
        "                df1 = df1\n",
        "                plt.scatter(df0['x'], df0['y'], c =\"red\", linewidths = 0.1)\n",
        "                plt.scatter(df1['x'], df1['y'], c =\"blue\", linewidths = .1)\n",
        "                # Plot the decision boundary\n",
        "                for i in range(num_neurons_layer1):\n",
        "                    px1, px2 = find_decision_boundary(-2, 2, weights[i], biases[i])\n",
        "                    plt.plot(px1, px2)\n",
        "\n",
        "                plt.xlabel(\"x\")\n",
        "                plt.ylabel(\"y\")\n",
        "                plt.legend([\"Class 1\" , \"Class 2\"])\n",
        "                plt.xlim([-2, 2])\n",
        "                plt.ylim([-2, 2])\n",
        "                plt.savefig('error_plot1.pdf')\n",
        "                plt.show()\n",
        "                # Generate predictions\n",
        "                predicted_output = predict(inputs, target, weights, biases, num_neurons_layer1)\n",
        "                # Create confusion matrix\n",
        "                cm = confusion_matrix(target, predicted_output)\n",
        "                # Plot heatmap of confusion matrix\n",
        "                plt.figure(figsize=(6, 4))\n",
        "                sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
        "                plt.xlabel(\"Predicted labels\")\n",
        "                plt.ylabel(\"True labels\")\n",
        "                plt.title(\"Confusion Matrix\")\n",
        "                plt.tight_layout()\n",
        "                # Save plot as PDF\n",
        "                plt.savefig(\"confusion_matrix.pdf\")\n",
        "                plt.show()\n",
        "                predicted_output = predict(inputs, target,weights,biases,num_neurons_layer1)\n",
        "                print(classification_report(target, predicted_output))    \n",
        "            return weights, biases, error_list\n",
        "        weights, b = update_weights(weights, biases, inputs[i % samples], target[i % samples], net_input, output, learning_rate, num_neurons_layer1)  # Step 7\n",
        "    \n",
        "    if plot:\n",
        "        plt.plot(error_list)\n",
        "        plt.xlabel('Epochs')\n",
        "        plt.ylabel('Mean Squared Error')\n",
        "        plt.title('Error Plot')\n",
        "        plt.grid(True)\n",
        "        plt.savefig('error_plot.pdf')\n",
        "        plt.show()\n",
        "        df0 = df0\n",
        "        df1 = df1\n",
        "        plt.scatter(df0['x'], df0['y'], c =\"red\", linewidths = 0.1)\n",
        "        plt.scatter(df1['x'], df1['y'], c =\"blue\", linewidths = .1)\n",
        "\n",
        "        for i in range(num_neurons_layer1):\n",
        "            px1, px2 = find_decision_boundary(-2, 2, weights[i], biases[i])\n",
        "            plt.plot(px1, px2)\n",
        "\n",
        "        plt.xlabel(\"x\")\n",
        "        plt.ylabel(\"y\")\n",
        "        plt.legend([\"Class 1\" , \"Class 2\"])\n",
        "        plt.xlim([-2, 2])\n",
        "        plt.ylim([-2, 2])\n",
        "        plt.savefig('error_plot1.pdf')\n",
        "        plt.show()\n",
        "        # Generate predictions\n",
        "        predicted_output = predict(inputs, target, weights, biases, num_neurons_layer1)\n",
        "        # Create confusion matrix\n",
        "        cm = confusion_matrix(target, predicted_output)\n",
        "        # Plot heatmap of confusion matrix\n",
        "        plt.figure(figsize=(6, 4))\n",
        "        sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
        "        plt.xlabel(\"Predicted labels\")\n",
        "        plt.ylabel(\"True labels\")\n",
        "        plt.title(\"Confusion Matrix\")\n",
        "        plt.tight_layout()\n",
        "        # Save plot as PDF\n",
        "        plt.savefig(\"confusion_matrix.pdf\")\n",
        "        plt.show()\n",
        "        predicted_output = predict(inputs, target,weights,biases,num_neurons_layer1)\n",
        "        print(classification_report(target, predicted_output))  \n",
        "\n",
        "    return weights, biases, error_list"
      ],
      "metadata": {
        "id": "kD3pkKGBJoB0"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def find_decision_boundary(start_x, end_x, weights, biases):\n",
        "    inputs = np.linspace(start_x, end_x)\n",
        "    output = -(weights[0] * inputs + biases)\n",
        "    output = output / weights[1]\n",
        "    return inputs, output\n",
        "\n",
        "def initialize_weights(sm, num_neurons_layer1, num_neurons_layer2):\n",
        "    np.random.seed(10)\n",
        "    weights = np.random.rand(num_neurons_layer1, num_neurons_layer2) * sm\n",
        "    biases = np.zeros((num_neurons_layer1, 1))\n",
        "    weights_layer2 = np.array([[1]*num_neurons_layer1])\n",
        "    biases_layer2 = num_neurons_layer1 - 1\n",
        "    return weights, biases, weights_layer2, biases_layer2\n",
        "\n",
        "def apply_activation_function(net):\n",
        "    h = np.where(net >= 0, 1, -1)\n",
        "    return h\n",
        "\n",
        "def forward_propagation(weights, inputs, biases, should_reshape):\n",
        "    if should_reshape:\n",
        "        inputs = inputs.reshape((2, 1))\n",
        "    net_input = np.dot(weights, inputs) + biases\n",
        "    outputs = apply_activation_function(net_input)\n",
        "    return net_input, outputs\n",
        "\n",
        "def update_weights(weights, biases, inputs, target, net_input, output, learning_rate, num_neurons_layer1):\n",
        "    inputs = inputs.reshape((1, 2))\n",
        "    net_input = net_input.reshape((num_neurons_layer1, 1))\n",
        "    if target == output:\n",
        "        return weights, biases\n",
        "    elif target == 1 and target != output:    #output=-1 but target=1\n",
        "        argmax_neuron = np.argmax(net_input)\n",
        "        diff_bias = learning_rate * (1 - net_input[argmax_neuron])\n",
        "        diff_weight = learning_rate * np.dot((1 - net_input[argmax_neuron]), inputs)\n",
        "        biases[argmax_neuron] = biases[argmax_neuron] + diff_bias\n",
        "        weights[argmax_neuron] = weights[argmax_neuron] + diff_weight\n",
        "    elif target == -1 and target != output:   # output=1 but target=-1\n",
        "        positive_indices = np.argwhere(net_input > 0)\n",
        "        diff_bias = learning_rate * (-1 - net_input)\n",
        "        diff_weight = learning_rate * np.dot((-1 - net_input), inputs)\n",
        "        new_biases = biases + diff_bias\n",
        "        new_weights = weights + diff_weight\n",
        "        for i in positive_indices[:, 0]:\n",
        "            weights[i] = new_weights[i]\n",
        "            biases[i] = new_biases[i]\n",
        "    return weights, biases\n",
        "\n",
        "def calculate_error(target, output):\n",
        "    error = 0.5 * np.power((target - output), 2)\n",
        "    return error\n",
        "\n",
        "def predict(inputs, target, weights, biases, num_neurons_layer1):\n",
        "    predicted_output = []\n",
        "    biases_layer2 = np.zeros((num_neurons_layer1, 1))\n",
        "    weights_layer2 = np.array([[1]*num_neurons_layer1])\n",
        "    biases_layer2 = num_neurons_layer1 - 1\n",
        "    for i in range(inputs.shape[0]):\n",
        "        net_input, outputs = forward_propagation(weights, inputs[i], biases, should_reshape=True)\n",
        "        net_input2, output = forward_propagation(weights_layer2, outputs, biases_layer2, should_reshape=False)\n",
        "        predicted_output.append(output[0])\n",
        "    return predicted_output\n",
        "    \n",
        "\n",
        "def MRI(df0, df1, inputs, target, num_neurons_layer1=3, num_neurons_layer2=2, learning_rate=0.0001, max_iter=200, samples=None, plot=True):\n",
        "    if samples is None:\n",
        "        samples = inputs.shape[0]\n",
        "    print('sample:', samples)\n",
        "    sm = 0.001\n",
        "    error_list = []\n",
        "    errors = []\n",
        "    mean_error = 10**3\n",
        "    weights, biases, weights_layer2, biases_layer2 = initialize_weights(sm, num_neurons_layer1, num_neurons_layer2)  # Step 0\n",
        "    for i in range(max_iter):\n",
        "        net_input, outputs = forward_propagation(weights, inputs[i % samples], biases, should_reshape=True)  # Step 4 and 5\n",
        "        net_input2, output = forward_propagation(weights_layer2, outputs, biases_layer2, should_reshape=False)  # Step 6\n",
        "        error = calculate_error(target[i % samples], output)\n",
        "        errors.append(error)\n",
        "        if i % samples == 0 and i != 0:\n",
        "            mean_error = np.mean(errors)\n",
        "            error_list.append(mean_error)\n",
        "            errors = []\n",
        "            print('Epoch %d / %d' % (len(error_list), int(max_iter / samples)))\n",
        "            print('loss:', mean_error)\n",
        "            for j in range(len(weights)):\n",
        "                print('W%d:'%(j+1),weights[j])\n",
        "                print('b%d:'%(j+1),biases[j])\n",
        "        if mean_error == 0 or (i > 50 and len(error_list) >= 2 and error_list[-1] - error_list[-2] == 0):\n",
        "            print('An early stop occurred!')\n",
        "            if plot:\n",
        "                plt.plot(error_list)\n",
        "                plt.xlabel('Epochs')\n",
        "                plt.ylabel('Mean Squared Error')\n",
        "                plt.title('Error Plot')\n",
        "                plt.grid(True)\n",
        "                plt.savefig('error_plot.pdf')\n",
        "                plt.show()\n",
        "                df0 = df0\n",
        "                df1 = df1\n",
        "                plt.scatter(df0['x'], df0['y'], c =\"red\", linewidths = 0.1)\n",
        "                plt.scatter(df1['x'], df1['y'], c =\"blue\", linewidths = .1)\n",
        "\n",
        "                for i in range(num_neurons_layer1):\n",
        "                    px1, px2 = find_decision_boundary(-2, 2, weights[i], biases[i])\n",
        "                    plt.plot(px1, px2)\n",
        "\n",
        "                plt.xlabel(\"x\")\n",
        "                plt.ylabel(\"y\")\n",
        "                plt.legend([\"Class 1\" , \"Class 2\"])\n",
        "                plt.xlim([-2, 2])\n",
        "                plt.ylim([-2, 2])\n",
        "                plt.savefig('error_plot1.pdf')\n",
        "                plt.show()\n",
        "                # Generate predictions\n",
        "                predicted_output = predict(inputs, target, weights, biases, num_neurons_layer1)\n",
        "                # Create confusion matrix\n",
        "                cm = confusion_matrix(target, predicted_output)\n",
        "                # Plot heatmap of confusion matrix\n",
        "                plt.figure(figsize=(6, 4))\n",
        "                sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
        "                plt.xlabel(\"Predicted labels\")\n",
        "                plt.ylabel(\"True labels\")\n",
        "                plt.title(\"Confusion Matrix\")\n",
        "                plt.tight_layout()\n",
        "                # Save plot as PDF\n",
        "                plt.savefig(\"confusion_matrix.pdf\")\n",
        "                plt.show()\n",
        "                predicted_output = predict(inputs, target,weights,biases,num_neurons_layer1)\n",
        "                print(classification_report(target, predicted_output))    \n",
        "            return weights, biases, error_list\n",
        "        weights, b = update_weights(weights, biases, inputs[i % samples], target[i % samples], net_input, output, learning_rate, num_neurons_layer1)  # Step 7\n",
        "    \n",
        "    if plot:\n",
        "        plt.plot(error_list)\n",
        "        plt.xlabel('Epochs')\n",
        "        plt.ylabel('Mean Squared Error')\n",
        "        plt.title('Error Plot')\n",
        "        plt.grid(True)\n",
        "        plt.savefig('error_plot.pdf')\n",
        "        plt.show()\n",
        "        df0 = df0\n",
        "        df1 = df1\n",
        "        plt.scatter(df0['x'], df0['y'], c =\"red\", linewidths = 0.1)\n",
        "        plt.scatter(df1['x'], df1['y'], c =\"blue\", linewidths = .1)\n",
        "\n",
        "        for i in range(num_neurons_layer1):\n",
        "            px1, px2 = find_decision_boundary(-2, 2, weights[i], biases[i])\n",
        "            plt.plot(px1, px2)\n",
        "\n",
        "        plt.xlabel(\"x\")\n",
        "        plt.ylabel(\"y\")\n",
        "        plt.legend([\"Class 1\" , \"Class 2\"])\n",
        "        plt.xlim([-2, 2])\n",
        "        plt.ylim([-2, 2])\n",
        "        plt.savefig('error_plot1.pdf')\n",
        "        plt.show()\n",
        "        # Generate predictions\n",
        "        predicted_output = predict(inputs, target, weights, biases, num_neurons_layer1)\n",
        "        # Create confusion matrix\n",
        "        cm = confusion_matrix(target, predicted_output)\n",
        "        # Plot heatmap of confusion matrix\n",
        "        plt.figure(figsize=(6, 4))\n",
        "        sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
        "        plt.xlabel(\"Predicted labels\")\n",
        "        plt.ylabel(\"True labels\")\n",
        "        plt.title(\"Confusion Matrix\")\n",
        "        plt.tight_layout()\n",
        "        # Save plot as PDF\n",
        "        plt.savefig(\"confusion_matrix.pdf\")\n",
        "        plt.show()\n",
        "        predicted_output = predict(inputs, target,weights,biases,num_neurons_layer1)\n",
        "        print(classification_report(target, predicted_output))  \n",
        "\n",
        "    return weights, biases, error_list"
      ],
      "metadata": {
        "id": "4y9lLVQo-RYa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Madaline Results (3 neurons)"
      ],
      "metadata": {
        "id": "X_zzMKjBKu6j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 3 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.0001,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e1def07a-d03b-4151-aad7-4eccd2b31eb3",
        "id": "8v26YzNCfZH_"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.44776119402985076\n",
            "W1: [ 0.0004571  -0.00013667]\n",
            "b1: [-0.00050011]\n",
            "W2: [-0.00014157  0.00074863]\n",
            "b2: [-0.00040013]\n",
            "W3: [-1.33668384e-05 -2.72382517e-04]\n",
            "b3: [-0.00019994]\n",
            "Epoch 2 / 200\n",
            "loss: 0.46\n",
            "W1: [4.98696739e-04 9.13273390e-05]\n",
            "b1: [-0.0005001]\n",
            "W2: [-0.00035982  0.00067303]\n",
            "b2: [-0.00050014]\n",
            "W3: [-0.00019636 -0.00040592]\n",
            "b3: [-0.00029995]\n",
            "Epoch 3 / 200\n",
            "loss: 0.34\n",
            "W1: [5.66390129e-04 6.99312453e-05]\n",
            "b1: [-0.00050009]\n",
            "W2: [-0.00041581  0.00071741]\n",
            "b2: [-0.00050015]\n",
            "W3: [-0.00012121 -0.00051607]\n",
            "b3: [-0.00029994]\n",
            "Epoch 4 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00060739 0.00011654]\n",
            "b1: [-0.00050009]\n",
            "W2: [-0.00047609  0.00065136]\n",
            "b2: [-0.00060016]\n",
            "W3: [-0.00025323 -0.0004869 ]\n",
            "b3: [-0.00039999]\n",
            "Epoch 5 / 200\n",
            "loss: 0.29\n",
            "W1: [0.00053614 0.00033523]\n",
            "b1: [-0.00050008]\n",
            "W2: [-0.00060882  0.00046378]\n",
            "b2: [-0.00070017]\n",
            "W3: [-0.00031302 -0.00056529]\n",
            "b3: [-0.00040003]\n",
            "Epoch 6 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00051135 0.00044049]\n",
            "b1: [-0.00050011]\n",
            "W2: [-0.00062427  0.00045441]\n",
            "b2: [-0.00070019]\n",
            "W3: [-0.00023268 -0.00065078]\n",
            "b3: [-0.00040005]\n",
            "Epoch 7 / 200\n",
            "loss: 0.25\n",
            "W1: [0.00055587 0.00037107]\n",
            "b1: [-0.00050013]\n",
            "W2: [-0.00070011  0.00039736]\n",
            "b2: [-0.0007002]\n",
            "W3: [-2.96904318e-05 -6.66444023e-04]\n",
            "b3: [-0.00050009]\n",
            "Epoch 8 / 200\n",
            "loss: 0.23\n",
            "W1: [0.00052836 0.00036001]\n",
            "b1: [-0.00060015]\n",
            "W2: [-0.0006462   0.00054324]\n",
            "b2: [-0.00070019]\n",
            "W3: [-0.00038678 -0.00063612]\n",
            "b3: [-0.00050013]\n",
            "Epoch 9 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00060012 0.00044364]\n",
            "b1: [-0.00050016]\n",
            "W2: [-0.00062636  0.00046605]\n",
            "b2: [-0.0008002]\n",
            "W3: [-0.00018144 -0.00065833]\n",
            "b3: [-0.00050016]\n",
            "Epoch 10 / 200\n",
            "loss: 0.22\n",
            "W1: [0.00065063 0.00024207]\n",
            "b1: [-0.00060019]\n",
            "W2: [-0.000628    0.00059371]\n",
            "b2: [-0.00070018]\n",
            "W3: [-0.0001533  -0.00065034]\n",
            "b3: [-0.00050018]\n",
            "Epoch 11 / 200\n",
            "loss: 0.28\n",
            "W1: [0.00065239 0.00044599]\n",
            "b1: [-0.00050023]\n",
            "W2: [-0.0006345   0.00053141]\n",
            "b2: [-0.00080018]\n",
            "W3: [-0.00023062 -0.00061684]\n",
            "b3: [-0.00050018]\n",
            "Epoch 12 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00070287 0.00024442]\n",
            "b1: [-0.00060029]\n",
            "W2: [-0.00072914  0.00053651]\n",
            "b2: [-0.00070015]\n",
            "W3: [-0.00014065 -0.00073482]\n",
            "b3: [-0.00050019]\n",
            "Epoch 13 / 200\n",
            "loss: 0.31\n",
            "W1: [0.00063648 0.00038979]\n",
            "b1: [-0.0006003]\n",
            "W2: [-0.00066745  0.00053103]\n",
            "b2: [-0.00080017]\n",
            "W3: [-0.00034913 -0.00072312]\n",
            "b3: [-0.00050022]\n",
            "Epoch 14 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00065759 0.00050331]\n",
            "b1: [-0.00060032]\n",
            "W2: [-0.00065782  0.0005662 ]\n",
            "b2: [-0.00080018]\n",
            "W3: [-0.00034157 -0.00075311]\n",
            "b3: [-0.00050029]\n",
            "Epoch 15 / 200\n",
            "loss: 0.19\n",
            "W1: [0.00065145 0.0005113 ]\n",
            "b1: [-0.00060035]\n",
            "W2: [-0.00064812  0.00059948]\n",
            "b2: [-0.00080018]\n",
            "W3: [-0.0002464  -0.00068268]\n",
            "b3: [-0.00060035]\n",
            "Epoch 16 / 200\n",
            "loss: 0.18\n",
            "W1: [0.0007211  0.00026149]\n",
            "b1: [-0.00070037]\n",
            "W2: [-0.00058628  0.00064991]\n",
            "b2: [-0.00080016]\n",
            "W3: [-0.00024196 -0.00072909]\n",
            "b3: [-0.00050036]\n",
            "Epoch 17 / 200\n",
            "loss: 0.22\n",
            "W1: [0.00072474 0.00027563]\n",
            "b1: [-0.00070038]\n",
            "W2: [-0.00054566  0.00069531]\n",
            "b2: [-0.00080014]\n",
            "W3: [-0.00037958 -0.00070528]\n",
            "b3: [-0.00050038]\n",
            "Epoch 18 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00071914 0.0003126 ]\n",
            "b1: [-0.00070039]\n",
            "W2: [-0.00055785  0.00072324]\n",
            "b2: [-0.00080013]\n",
            "W3: [-0.00029219 -0.00077689]\n",
            "b3: [-0.00050043]\n",
            "Epoch 19 / 200\n",
            "loss: 0.25\n",
            "W1: [0.00072309 0.00048216]\n",
            "b1: [-0.00060046]\n",
            "W2: [-0.00062423  0.00056107]\n",
            "b2: [-0.00090011]\n",
            "W3: [-0.000292   -0.00069599]\n",
            "b3: [-0.0006005]\n",
            "Epoch 20 / 200\n",
            "loss: 0.22\n",
            "W1: [0.00069967 0.00034861]\n",
            "b1: [-0.00070046]\n",
            "W2: [-0.0006877   0.00066076]\n",
            "b2: [-0.00080008]\n",
            "W3: [-0.00025125 -0.00067848]\n",
            "b3: [-0.00060052]\n",
            "Epoch 21 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00071066 0.0003456 ]\n",
            "b1: [-0.00070049]\n",
            "W2: [-0.00078084  0.00060582]\n",
            "b2: [-0.00080007]\n",
            "W3: [-0.00017846 -0.00068798]\n",
            "b3: [-0.00060054]\n",
            "Epoch 22 / 200\n",
            "loss: 0.21\n",
            "W1: [0.00073977 0.00028201]\n",
            "b1: [-0.00070052]\n",
            "W2: [-0.00070211  0.00070799]\n",
            "b2: [-0.00080007]\n",
            "W3: [-0.00026269 -0.00077155]\n",
            "b3: [-0.00050055]\n",
            "Epoch 23 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00074154 0.00048594]\n",
            "b1: [-0.00060054]\n",
            "W2: [-0.00076107  0.00057339]\n",
            "b2: [-0.0009001]\n",
            "W3: [-0.00034996 -0.00077945]\n",
            "b3: [-0.00050061]\n",
            "Epoch 24 / 200\n",
            "loss: 0.25\n",
            "W1: [0.00079203 0.00028438]\n",
            "b1: [-0.00070058]\n",
            "W2: [-0.00083296  0.0006023 ]\n",
            "b2: [-0.00080007]\n",
            "W3: [-0.00018154 -0.00079081]\n",
            "b3: [-0.00060065]\n",
            "Epoch 25 / 200\n",
            "loss: 0.23\n",
            "W1: [0.00080803 0.00029659]\n",
            "b1: [-0.00070061]\n",
            "W2: [-0.00062031  0.00073878]\n",
            "b2: [-0.00090008]\n",
            "W3: [-0.00032249 -0.00068459]\n",
            "b3: [-0.00060067]\n",
            "Epoch 26 / 200\n",
            "loss: 0.34\n",
            "W1: [0.00078367 0.00035683]\n",
            "b1: [-0.00070066]\n",
            "W2: [-0.00051154  0.00089454]\n",
            "b2: [-0.00090009]\n",
            "W3: [-0.00030786 -0.00070118]\n",
            "b3: [-0.00060072]\n",
            "Epoch 27 / 200\n",
            "loss: 0.2\n",
            "W1: [0.00073026 0.00041247]\n",
            "b1: [-0.00070065]\n",
            "W2: [-0.00069514  0.00080641]\n",
            "b2: [-0.00090006]\n",
            "W3: [-0.0002671  -0.00068367]\n",
            "b3: [-0.00060075]\n",
            "Epoch 28 / 200\n",
            "loss: 0.28\n",
            "W1: [0.00073385 0.00041981]\n",
            "b1: [-0.00070066]\n",
            "W2: [-0.00087446  0.00066647]\n",
            "b2: [-0.00090005]\n",
            "W3: [-0.0001943  -0.00069316]\n",
            "b3: [-0.00060078]\n",
            "Epoch 29 / 200\n",
            "loss: 0.27\n",
            "W1: [0.00074494 0.0004389 ]\n",
            "b1: [-0.00070067]\n",
            "W2: [-0.00083962  0.00062631]\n",
            "b2: [-0.00100007]\n",
            "W3: [-0.00031658 -0.00067737]\n",
            "b3: [-0.00060079]\n",
            "Epoch 30 / 200\n",
            "loss: 0.28\n",
            "W1: [0.00068739 0.00050396]\n",
            "b1: [-0.00070066]\n",
            "W2: [-0.00082816  0.00071326]\n",
            "b2: [-0.00100007]\n",
            "W3: [-0.00024229 -0.0006868 ]\n",
            "b3: [-0.00060082]\n",
            "Epoch 31 / 200\n",
            "loss: 0.2\n",
            "W1: [0.00067351 0.0005678 ]\n",
            "b1: [-0.00070067]\n",
            "W2: [-0.00071372  0.00070759]\n",
            "b2: [-0.00110008]\n",
            "W3: [-0.00023786 -0.0007332 ]\n",
            "b3: [-0.00050083]\n",
            "Epoch 32 / 200\n",
            "loss: 0.24\n",
            "W1: [0.00072336 0.00055275]\n",
            "b1: [-0.0007007]\n",
            "W2: [-0.00078356  0.00062333]\n",
            "b2: [-0.00110007]\n",
            "W3: [-0.00037548 -0.00070939]\n",
            "b3: [-0.00050085]\n",
            "Epoch 33 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00077612 0.00048333]\n",
            "b1: [-0.00070072]\n",
            "W2: [-0.00076376  0.00066328]\n",
            "b2: [-0.00110008]\n",
            "W3: [-0.00028809 -0.000781  ]\n",
            "b3: [-0.00050091]\n",
            "Epoch 34 / 200\n",
            "loss: 0.22\n",
            "W1: [0.00075908 0.00046529]\n",
            "b1: [-0.00070068]\n",
            "W2: [-0.0009571   0.00059362]\n",
            "b2: [-0.00100005]\n",
            "W3: [-9.42161729e-05 -7.84725244e-04]\n",
            "b3: [-0.00060096]\n",
            "Epoch 35 / 200\n",
            "loss: 0.25\n",
            "W1: [0.00071572 0.0005587 ]\n",
            "b1: [-0.00070067]\n",
            "W2: [-0.00099471  0.00059954]\n",
            "b2: [-0.00100007]\n",
            "W3: [-0.00017442 -0.00071193]\n",
            "b3: [-0.00070092]\n",
            "Epoch 36 / 200\n",
            "loss: 0.21\n",
            "W1: [0.00076848 0.00048928]\n",
            "b1: [-0.00070068]\n",
            "W2: [-0.00095494  0.00055689]\n",
            "b2: [-0.00110007]\n",
            "W3: [-0.00017663 -0.00071973]\n",
            "b3: [-0.00070088]\n",
            "Epoch 37 / 200\n",
            "loss: 0.23\n",
            "W1: [0.00087364 0.00031058]\n",
            "b1: [-0.00070068]\n",
            "W2: [-0.00096928  0.00073053]\n",
            "b2: [-0.00100006]\n",
            "W3: [-0.00017885 -0.00072753]\n",
            "b3: [-0.00070084]\n",
            "Epoch 38 / 200\n",
            "loss: 0.25\n",
            "W1: [0.00082707 0.0003511 ]\n",
            "b1: [-0.00070074]\n",
            "W2: [-0.00088154  0.00080797]\n",
            "b2: [-0.00100009]\n",
            "W3: [-0.00030935 -0.00075088]\n",
            "b3: [-0.00060084]\n",
            "Epoch 39 / 200\n",
            "loss: 0.29\n",
            "W1: [0.00074358 0.00046537]\n",
            "b1: [-0.00070076]\n",
            "W2: [-0.00085528  0.00074493]\n",
            "b2: [-0.00110008]\n",
            "W3: [-0.00031953 -0.00069018]\n",
            "b3: [-0.00060087]\n",
            "Epoch 40 / 200\n",
            "loss: 0.27\n",
            "W1: [0.00077541 0.00040334]\n",
            "b1: [-0.00070074]\n",
            "W2: [-0.00091545  0.00074021]\n",
            "b2: [-0.00110008]\n",
            "W3: [-0.0003839  -0.00072019]\n",
            "b3: [-0.00050089]\n",
            "Epoch 41 / 200\n",
            "loss: 0.24\n",
            "W1: [0.00074524 0.00042371]\n",
            "b1: [-0.00070075]\n",
            "W2: [-0.00090574  0.00077349]\n",
            "b2: [-0.00110008]\n",
            "W3: [-0.00036979 -0.00070496]\n",
            "b3: [-0.00050092]\n",
            "Epoch 42 / 200\n",
            "loss: 0.25\n",
            "W1: [0.0006964 0.0005455]\n",
            "b1: [-0.00070078]\n",
            "W2: [-0.00084226  0.00069626]\n",
            "b2: [-0.00120009]\n",
            "W3: [-0.0002824  -0.00077657]\n",
            "b3: [-0.00050098]\n",
            "Epoch 43 / 200\n",
            "loss: 0.24\n",
            "W1: [0.00083155 0.00034292]\n",
            "b1: [-0.00070081]\n",
            "W2: [-0.00082411  0.00086387]\n",
            "b2: [-0.00110007]\n",
            "W3: [-0.00028945 -0.00072166]\n",
            "b3: [-0.00060104]\n",
            "Epoch 44 / 200\n",
            "loss: 0.27\n",
            "W1: [0.000785   0.00038341]\n",
            "b1: [-0.00070086]\n",
            "W2: [-0.00080523  0.00089119]\n",
            "b2: [-0.00110009]\n",
            "W3: [-0.0003201 -0.000801 ]\n",
            "b3: [-0.00050104]\n",
            "Epoch 45 / 200\n",
            "loss: 0.2\n",
            "W1: [0.00067242 0.00058406]\n",
            "b1: [-0.00070088]\n",
            "W2: [-0.00081166  0.00074496]\n",
            "b2: [-0.0012001]\n",
            "W3: [-0.00024612 -0.00077112]\n",
            "b3: [-0.00060103]\n",
            "Epoch 46 / 200\n",
            "loss: 0.3\n",
            "W1: [0.00071403 0.000569  ]\n",
            "b1: [-0.00070091]\n",
            "W2: [-0.0008815  0.0006607]\n",
            "b2: [-0.00120009]\n",
            "W3: [-0.00026725 -0.00080424]\n",
            "b3: [-0.00060103]\n",
            "Epoch 47 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00077431 0.00045255]\n",
            "b1: [-0.00070094]\n",
            "W2: [-0.00088682  0.00065611]\n",
            "b2: [-0.00120008]\n",
            "W3: [-0.00029423 -0.00082617]\n",
            "b3: [-0.00060106]\n",
            "Epoch 48 / 200\n",
            "loss: 0.26\n",
            "W1: [0.00072349 0.00048708]\n",
            "b1: [-0.00070092]\n",
            "W2: [-0.00086702  0.00069606]\n",
            "b2: [-0.00120008]\n",
            "W3: [-0.00027097 -0.00084013]\n",
            "b3: [-0.0006011]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABXd0lEQVR4nO2dd3hUVdrAf2eSSUKoIaGHhF5S6FXFBWGlWFDRUKRLwLWsrt8Ktl3siu6qq+4qVRKQEguKSBEQxUYJNQmd0EEkoUN6zvfHnYSUmUy705Lze577JHPnzrnvvXPnvOe87QgpJQqFQqFQOIrB0wIoFAqFwrdRikShUCgUTqEUiUKhUCicQikShUKhUDiFUiQKhUKhcAqlSBQKhULhFB5TJEKIpkKIDUKIPUKINCHEE2aOEUKI94UQh4QQu4UQXTwhq0KhUCgs4+/Bc+cD/yel3C6EqAlsE0KslVLuKXHMYKC1aesJfGT6q1AoFAovwWMzEinlGSnldtP/V4C9QJMyhw0FEqXGJqCOEKKRm0VVKBQKRQV4ckZSjBCiGdAZ2FzmrSbAiRKvT5r2nTHTxmRgMkD16tW7tmvXziWyKnyX0xezyLyWS93qATSpU83T4jhGYT6cPwK5V6FGA6jV2NMSKSoJ27Zty5BS1nPksx5XJEKIGsAXwJNSysuOtiOlnAXMAujWrZtMTk7WSUJFZUFKyZur9zHzx3T6dg1nxrAO+BmEp8WynVPbYcmDkB0MQz+BmPs8LZGiEiGEOOboZz2qSIQQRjQl8qmU8kszh5wCmpZ4HW7ap1DYjRCCZwa1I8jfj/+sP0hufiH/juuI0c8Hghd3LYHlf9VmIRPXQKMOnpZIoSjGY4pECCGAucBeKeU7Fg5bDjwmhFiC5mS/JKUsZ9ZSKGxFCMHf/tyGQKOBt1bvJye/gA9GdiHA30uVSUE+rJsOv30IzfrAAwlQPdTTUikUpfDkjORmYAyQIoTYadr3HBABIKX8GFgJDAEOAdeBCe4XU1EZeaRvK4L8/Xh5xR6mLEjmo9FdCTL6eVqs0lw/D59PhPQN0GMKDHwN/IyelkqhKIfHFImU8megQgO11GrcP+oeiRRVjYm3NCfI6MfzX6XwUMJWZo/tRnCAx92GGmf3wJKRcPk0DP0vdB7taYl8jry8PE6ePEl2dranRfEqgoKCCA8Px2jUb1DiJb8ahcIzjOoZQaC/gac/38X4eVuZO74bNYM8POrfsxyWPQyBNWH8Smja3bPy+CgnT56kZs2aNGvWDM2SrpBSkpmZycmTJ2nevLlu7XqpYVihcB/DuobznxGd2Xb8AqPnbuHS9TzPCFJYCN+/BkljoH57mPyDUiJOkJ2dTWhoqFIiJRBCEBoaqvssTSkShQK4q2NjPnqwC3tPX2bk7E2cv5brXgGyL8PSB2HjW5oZa8JKqKVyb51FKZHyuOKeKEWiUJi4Pbohs8Z25fC5q4yY9Rt/XHGTbT3jEMwZAAfWwOC34e4PwT/QPedWKHRAKRKFogR929bnk/HdOXE+ixEzN3HmUpZrT3hwHcy+Da5nwNivoedkUKPoSsHvv//OiBEjaNmyJV27dmXIkCEcOHCAo0ePEhMT45Jzbty4kS5duuDv78/nn3/uknOYQykShaIMN7UKY8FDPfjjSg5xM3/jxPnr+p9ESvj5Xfj0fqgTAfEboHkf/c+j8AhSSu6991769u3L4cOH2bZtG2+88QZnz5516XkjIiKYP38+o0aNcul5yqIUiUJhhm7N6vLppJ5czsonbuZvHMm4pl/judfhi4dg3YsQfS88tAZCIvVrX+EY2dmQmAhTp2p/nXBIb9iwAaPRyMMPP1y8r2PHjvTpU3qwcPToUfr06UOXLl3o0qULv/76KwBnzpzh1ltvpVOnTsTExPDTTz9RUFDA+PHjiYmJITY2lnfffbfceZs1a0aHDh0wGNzbtavwX4XCAh2b1mFxfC9Gz91M3MzfWDSpJ60b1HSu0YvHYcko+D0V+k+HW/6mTFneQHY23HUXrFt3Y9+CBfDNNxAUZHdzqampdO3a1epx9evXZ+3atQQFBXHw4EFGjhxJcnIyixYtYuDAgTz//PMUFBRw/fp1du7cyalTp0hNTQXg4sWLdsvlKtSMRKGogKjGtVg6uRcAw2dtIu30JccbO/ozzOoLF47DqCTo85RSIt5CUlJpJQLa66Qkl542Ly+P+Ph4YmNjeeCBB9izR1uOqXv37nzyySe8+OKLpKSkULNmTVq0aEF6ejqPP/44q1evplatWi6VzR6UIlEorNC6QU2SpvQmyN/AyFmb2HXion0NSAlbZkPiUAgOhfjvoc3tLpFV4SCmUX450tIcai46Oppt27ZZPe7dd9+lQYMG7Nq1i+TkZHJztbDzW2+9lY0bN9KkSRPGjx9PYmIiISEh7Nq1i759+/Lxxx8zadIkh2RzBUqRKBQ20DysOkun9KZ2sJEH52xm69Hztn0wPweWPw4r/w6t/gyT1kFYK9cKq7AfS1FU0dEONXfbbbeRk5PDrFmzivft3r2bn376qdRxly5dolGjRhgMBhYsWEBBQQEAx44do0GDBsTHxzNp0iS2b99ORkYGhYWFDBs2jFdffZXt27c7JJsrUIpEobCRpnWDSZrSm/o1Axk7dwu/Hsqo+ANXfof5d8KOBXDr0zBiEQTVdo+wCvuIi4MBA0rvGzBA2+8AQgiWLVvGunXraNmyJdHR0Tz77LM0bNiw1HGPPPIICQkJdOzYkX379lG9enUAfvjhBzp27Ejnzp1ZunQpTzzxBKdOnaJv37506tSJ0aNH88Ybb5Q779atWwkPD+ezzz5jypQpRDuoCO2+Xq0uYuVCLWylcCV/XMlm9JzNHMu8zswxXenbtn75g04mw9LRkH0J7vkIou9xu5xVnb1799K+fXvbP5CdrflE0tK0mUhcnEOOdl/A3L0RQmyTUnZzpD0VtaVQ2En9mkEsmdybMXM3E5+YzIejujAwusRIc8ensOJJqNkIHloLDV2TfKbQmaAgGDvW01L4JMq0pVA4QN3qASyK70V049o8+ul2vtl1GgryYNU0+PoRiOilFV1USkRRBVCKRKFwkNrVjCyc1JMukSG8uORH/vjfENj8MfR6BEYvg+C6nhZRoXALSpEoFE5QI9CfxDuqsyp4OrUzdvBr7Csw6A3wU1ZjRdVBKRKFwhnSlhGUMJB61Qy81fhdRm1tybyfj3haqnLoWP1DoSiHR4dNQoh5wJ3AH1LKcsZkIURf4Gug6Jf5pZTyZbcJqPBpioJwUlO1NAFdg3AKC2HDq/DTvyG8B2L4AqZVq8/pJTt4ecUesvMLeKSvd+SL6Fz9Q6Eoh6dnJPOBQVaO+UlK2cm0KSWisImiznPcOHj7be3vXXfpNBLPvgSLR2hKpMtYGL8CajYkwN/AByM7M7RTY95avZ931h7AVeH19swwPFT9o8rjiTLy77zzDlFRUXTo0IH+/ftz7Ngxl5ynLB6dkUgpNwohmnlSBkXlpKLO06kIz4yDsHgkXDgCQ/4F3SeVqpfl72fgnbhOBPn78f76g+TkFfDM4Ha6rkpn7wxD5+ofChsoKiM/btw4lixZAsCuXbs4e/YsTZs2ddl5O3fuTHJyMsHBwXz00UdMnTqVpUuXuux8RXh6RmILvYUQu4QQq4QQ7knTVPg8Luk8D6zRFqHKuqAtQtUj3mzRRT+D4I37YhnbO5KZG9N5cXkahYX6zUzsnWHoXP2j0qKnH8lTZeT79etHcHAwAL169eLkyZOOX4QdeHtoyXYgUkp5VQgxBPgKaG3uQCHEZGAyaIu7KKo2unaeUsLP78D6V6BhrFbqpE7Fo0qDQfDS3dEEGf2YtTGdnPxCXrs3lrxcYdFvY6tPx14lGRenzVhKKh97q3+41N/kBejtR/KGMvJz585l8ODB9gvvAF6tSKSUl0v8v1II8T8hRJiUslyRIynlLGAWaCVS3CimwgvRo/MEIPcafP0opC2DmGHaeuoBwTZ9VAjBs4PbEeRv4P3vD3E9p5A9n3Rg/bobhoCizgps78jsVZJBQVo7tlT/MKcw7JHNV3GZKdQKeXl5PPbYY+zcuRM/Pz8OHDgAaGXkJ06cSF5eHvfccw+dOnUqVUb+jjvu4PbbLVeQXrhwIcnJyfz444+uE74kUkqPbkAzINXCew25UQ+sB3C86HVFW9euXaVCkZUlZUKClFOnan+zsuxs4PxRKf93s5TTa0v583tSFhY6LMuH3x+UkdNWyLChyRJDgdSmOdqWkKBtJfeVfM/cdfXvX/q4/v0duD4z7fbrV7rdfv2knD3bdtm8iT179th87NNPm7/GqVMdO/e6detknz59zL535MgRGR0dLaWUcvr06fL//u//ZEFBgczLy5N+fn7Fx506dUrOmjVLduzYUSaYbvaVK1fk559/LocOHSonTJhgtv21a9fKdu3aybNnz1qUz9y9AZKlg/24p8N/FwN9gTAhxElgOmAEkFJ+DNwP/EUIkQ9kASNMF6xQWMWp0klHNkLSOJAF8ODn0HqA9c9UwKP9WvHdKgO72u1F+G3j3NddoMAP0GYJlp5qS+aqwsKKXzvCwoWwYUPpfRs2gNFon2y+iN5+pNtuu43nnnuOWbNmMXnyZEArI3/p0qVSzvZLly4RHh6OwWAgISGhVBn58PBw4uPjycnJYfv27QwZMoSAgACGDRtG27ZtGT16dLnz7tixgylTprB69Wrq1zdTTNRFeDpqa6SV9z8EPnSTOAo34PW2dilh80xY8xyEtoKRiyG0pS5ND4tpwffv+hE6MJX6w5I592U3ZL5fhZ2VufeSksx3+M6aYb780vz+33+3XTZfRTdTqImiMvJPPvkkM2bMICgoiGbNmvHee++VOu6RRx5h2LBhJCYmMmjQoFJl5N9++22MRiM1atQgMTGRU6dOMWHCBApNowZzZeSffvpprl69ygMPPABo/uLly5c7dhF2oMrIK9yGOYfmgAG22drdooDysuHbp2Dnp9B2CNw7E4L0W8606Pp/O3uC0MG7yTlRl+jM7nz7tTaes/XeTJ2q5caUZcgQrXN39P4MGQKrVpXfP3AgFBSYlw28d2Bgbxn5KlRFXpWRV/gujjo03ZKZffmMtn7IqWT40zT40zNg0Dc6/obTuykr0wxsidhFzZs2kyN7ULua0WaHuCUzzMqV2gaO3Z/77jOvSO6/H0aPLi8blP9eEhNhxAg4cMD7FIs1VBV5J3DUueLNm3K2eyeOOjTtcUQ7xPHNUr7dWspXG0mZ9rVOjVpnVcpp2eq5b+Ud72+U56/m2Py5rCwpBwwwf0+cuT/2OvEtfS8ltwEDnA8CcBR7nO1VDb2d7b6QkKioJDjq0LQnb8KepLLsbPj1wwXkz7mDK9nVyBm7DqLurlgYHRkU04hZY7px4OxVRs7exLkrOTZ9rmhmk5CgXaelVAF7neFBQbBixY12ExK015ZmFJa+l5J4uhSLrISme2dxyT1xVAN586ZmJO6nKNT26acth9qaG0nbMmK1dUZiT/tZV3Plsvi/Szm9llwzeqgMCcp02ejZ2r35+eA52e6FVfK2f22QZy7aL4Cl+zN4sINhz06eV68QWmdJT0+X586dk4VOhG1XNgoLC+W5c+dkenp6ufdwYkainO0Kp7HHie6IQ9PW9hMTteKMZUlIKGP7vpbB7x+Mp2H2T/zr18d4Zt1LFEh/88c6ia2ybzlynonzt5pWXuxJeIhtSY+WzlESWwMa7MXaeYvQ+57aSl5eHidPniRb1cwvRVBQEOHh4RjLxHU742z3+OzBFZuakbgXl/swpG3JhTb5YE7vkvKdGJn7z3rywdglLh8923Nvth87L2Onr5Y3vbFeHjl31a7zFN2fwYNd/12YO+/UqVriYtmERk/6SBT2ga8mJCoqB+6oLmtLRI1VH0zqF/DVoxBcl+/CV/NpShfLx+qEPfemc0QIi+J7MWbuZuJm/sai+J60ql/TpvMU3Z/UVPORV65KHiz7vZiL7vKVqC2F4yhnu8JpvKW6bFycZsYpyYABEHd/AaydDp9PhEYdYfIP9B/TxfyxDiagWcLeexPTpDZLp/SmUMLwmZvYe+ay+QN1Op/eFCmWGTO0v0qJVA2Uj0ThNM4kGrpCloULYdkyzbgy4p6LPBg0Cb/0tdB1Agx+C/wDio919ejZ0XuTfu4qD87ZTFZeAYkTe9AhvI5Lz6dQOOMjUYpEUSG2ZpR7S1ZwyY60Xdh+vh4xkhYhx5CD38bYe6L7BcLxe3Pi/HVGzt7Epet5zJ/Yna6RdV16PkXVRimSMihFog++OLotity6q81KFt43may8IIYlLWDyq719Mmv59MUsHpyzmbOXs5k7rju9W4Z6WiSX4PU12KoAzigS5SNRWMQX1/pOSy3khVvfYvnIkRzIbEm32T/wy4nePluptnGdaiyd3Ismdaox/pMt/HjgnKdF0p2iAcu4cVoNsXHjtNcqatd3UIpEYRFLEUdLlnj2R24xez3nKo+EjeWVfq+xcHccfT5ZzcnL4YBvV6qtXyuIJZN70bJeDeITklm756ynRdIVXxywKEqjFEkVxZZSIpYigFat0nfEaG9ZE3Oj15zfj8DcPxOR9S0zj7zKmGWzyM6vBrgmGsvdhNYIZHF8L9o3rsVfFm7j291nPC2SbrgjfFzhWlQeSRXE1mq65tZoKEKvZUjtrexrbvQqD2+AWeMhAMToLxjX5Daq9a98zubawUYWPtSDifO38vji7eTkd+S+LuGeFssitvo9XB2yrPwvbsDRTEZv3lRme8XYu6yrpWxpe7LALdWbsjcrvnT2eqF8steHMv8fdeSZf/SUMvOw/TfDB7mWkydHzvpNNntmhVy0+ZinxTGLXXXPHKzBprccVR1U9V+FPdhjSggK0taXMIetI8aKnKn2mjWKRq9B/lnMH/oX3h34HF/vH8L3LdZC3Ra2CeTjBAf4M298d/7Uph7PfpnC/F+OeFqkctjj9yhbzTghQb/IQOV/cQ/KtFUFsWRKaN3a/H5nlyGt6Mdsr1kjLg5WLT3FU40fpHuTHUz/4Vl+M05l+YiqNSYKMvoxc0xXHl+0gxe/2UN2fiEP/0mfJYH1wN4BgqsWlVL+F/fg0V+fEGKeEOIPIYTZr1tovC+EOCSE2C2EKF8cSWE3cXHQv3/5/YsWmXd0OztirOjHbLGsiQUlFfTHJhb17Uun8IMk5Cyi5cRnWP6NoUravAP9/fjvg124q2Nj3ly1j/fWHUB6SV5YRQMEe4IrXCmHQkcctYnpsQG3Al2AVAvvDwFWAQLoBWy2pV3lI7HO7Nn2+SacwZofxJbKvlJKKbfOk/KlUCn/00nKs5Vw9TtbFnUxQ35BoXxq6U4ZOW2FfGPlXq9Yf8OSb+LChfL7o6Kk/NvfXLN2ivKR2A6+Wv1XSrlRCNGsgkOGAommi9wkhKgjhGgkpaw8sY9uwFzUyoED5o91xZTfmmnMqlkjPxdWT4PkedCyP9w/F6qF6C+oJ3FiYXo/g+Dt+zsQZDTw8Y+Hyc4rYPpdUQghXCy0ZW6sT186es6cmXPPHm0Dx9aad0SOqjiDdSXe7iNpApwo8fqkaV85RSKEmAxMBoiIiHCLcL6Apf5p+HDzxzsy5bcWXunUj/nqH5A0Do7/Cjf9FQa8CAY/+4X0dipyJNngPDAYBK/eE0Ogvx/zfjlCTn4hr90Tg8HgWWVSVnRry/PqFVZuTQ6Fvni7IrEZKeUsYBZotbY8LI7XYKl/Gj5cmxU46kAvwtaBtEM/5tM7YMlouJ4B982BDg/Y2YAP4YhXuIwGF3Fx/OPO9gQZDfzvh8Pk5Bfw1rAO+Pt5TyCCJZ9FSZQj3PfwdkVyCmha4nW4aZ/CRiz1TwcP6jPld3IgbZndn8HyxyA4DCaugcadnGjMB7DXK2xBg4tvvmHqoHZUM/rx77UHyMkv5L3hnTB6iTKpKMm1COUI9z28XZEsBx4TQiwBegKXlH/EPirqn/SY8useXllYAOumw68fQMRNEJcINeo5LJ/PpDVbcySVvY7c3Ao1+OP9WxNoNPD6yn3k5hfy4ajOBPp73iRY0sy5axesWVP6WakM5WyqIh5VJEKIxUBfIEwIcRKYDhgBpJQfAyvRIrcOAdeBCZ6R1HdxNgfEGrqGV2Zd0FYxPPw9dJ8EA98oXoTKIZxwYLudihxJ5q4jMtJ8OyV65cm3tiTI6Mc/v05jcuI2Zo7pSpDRpEw8qGBLDmBee005wisFjoZ7efOmwn9LY3N4rYNt6xJeeXaPlO911MJ7kz/RRzh76694K5auw8ZrW7LlmGz2zAo5YuZv8mp2noqJVZgFXw3/VbgHV0at6BJeuXcFLJsCxmAYvwIieukjXGVJa7Z0HZGRcOzYjdcWpprDu0cQ6O/H/322i3HztjAv6DC1XOLYUlRVlCJROI3DiqqwEH6cAT++CY27wPCFULuJc8KUNNlcvGj+GF/z5lqyH77wAgQE2KTB7+nchEB/A48v3sHoAn8Sg2pQJ/tq6YN8TcEqvAalSBSeIecKLHsY9q2AjiPhzvfA6KRx3JwvISQELly48VpPB5G7/AyWHF2jR9t1vsGxjfjYz8AjC7YycsTrLFz6AqFZl28c4GsKVuE1qDXbFe4n8zAseRAyDsDtr0Kvv0DJLGxHO+iiBdvLEh+vKRQ9vbnuWNC+5H1o00bbd/Cg09fxU9op4hO2En7+DIuWPE/9axf0l13hczizZruakSjcy6H18PkEEAYY8yW06Fv6fWcirSz5EkJCYMYMp8Quh8sSaEy4UFH1iW7C/IkGHkrcRtyjs/i0TTZNHrzfJ5SIr0RzVzW8I0tJUfmREn55Hz69H2qFQ/yG8koEnFtAwp2lXvVy5FsqhevihTR6tWtE4pSbyaxWk7hzjTl+rVCXdl1JRevaKDyLUiQ+jjtLcjtMXhZ8ORnW/gPa3wUPfQd1m5s/dudO8/tt6aDtrUlvC5ZusB5KS88Vvxyga2QIi+N7cS03nwdm/srhc1etf8iD6KFbfeL34os4GjfszVtVySPxiXSAC8el/LiPlNNrS/njW1JWVOI8K0vK6Gjncj/0TJqp6AbrcfMrynNxYw7M3jOXZNdXvpNdX1kr9525rHv7elF6meUbm61LPvvE78WD4EQeicc7fVdsVUWReH2+3dFfpJzRQsrXmki5b5X14y1dUFSUfgt427Peh24LqVigop7Rzb3ewbNXZI/X1spOL62RKScvuuQczuLs8+71vxcP44wiUc52H8ar8+22zoVVUyGkGYxYDPXaWP+MpQsaNMh5j+rFi3DLLaVvjjUnviUz265d2l9nMz2tFUJz40IarerXIGlKb0bN3szI2ZtImNiDLhHeteaLs+V+vPr34uMoH4kP45XLiObnwjdPwLdPQcvbYNJ625QIWL6gjh2dkyk7G26+uXyPYc3AftWCz+DKFefkKcKcT6dfP60g49SpmmxxcVrE2dixLg9PigytTtLDvalbPYAxczazOT3TpeezF2eXfPbK30slQc1IfBhbRmhuDZe8chaSxsKJTXDLU3DbC/YtQmXugqKj4e67nZMrKenGEnxlWbrU8s2pUcP8Z2rWdE6eIsrOOlq3hiVLtLyXItxcZLJJnWqmmckmxn2yhTlju3NL6zC3nNsW7J0Elk3F6d8f1q+/8b6qNqwTjtrEvHmrKj4SKSs207vVzH5ym5T/aiflKw2kTPnc8XYuXNB8InoIXXRzunc3bxwvu5U9j7uN6l5kxD93JVsOfPdH2fr5lXL93t/dfn49MPf89+sn5ezZrilg6uugnO1VV5FUhNv6pZ2LpXy5npTvxEh5epdzbekltLlexN7que4O83E2LMlZygQjXMi8LO/64CfZ6rlv5aqU0+6RQUe8SC/7BM4oEmXaqsS43LlYkA9r/wmb/gvN+sAD86G6k2YQvYQ2l3RQktBQyDTjAyh5Hjc7vD1qxDeTSV9nwAIWfraMCYt38+iiHbwTV8jQTk4W1XQjyrnuPpQiqcS4tF+6fl4rdZL+A/SYAgNfAz+j8+3qJbSlXqRHD3j0Uc2hXdIXYek8rqzBXxZzPqL+/W843835cfRyglnI9qu1/EsSJ47ioYStPLl0Jzl5hcR1b2q+DS9DOdfdiKNTGW/elGlLw2WWmd9TpXw3VsqXw6TcvkAXWaWUmmCzZ0sZEeG80JbsGrNn3zhX2ZsTFSXl3/7mOuO5LXksJZ1e//uflO3aWb4Xen7BVsxq13Py5Zi5m2XktBUy4dcjjt8DN6ISEO0D5SNRisQSuq+OmPa1lK82kvLtNlIe36KLjFJK87/6yEit43fU0d6vX/mOsX//0h1xQoKUTz1VPqNe7x7H3l7Nlix/PZ0ANrSVnZcvH5q/VUZOWyFn/XjYgZvgfly5Omhlw2cVCTAI2I+2JvszZt4fD5wDdpq2Sba0qxSJCygokHL9q1JOryXlrNukvKSz89UVntHZs21r0x1eWXvPUdHyukXOdz2d8zYqutz8AvnIwm0yctoK+f66A/afR+G1OKNIPOYjEUL4Af8F/gycBLYKIZZLKcsG/C+VUj7mdgEVN8i+rBVdPLAKOo2GO/7t/CJUZXGFZ/TAAdvadIdX1t5zWDoebhj59XQC2BhYYPQz8J8RnQg0Gvj32gNk5xfw99vbIkquJ6OocnjS2d4DOCSlTAcQQiwBhgIWMscUHiHjECwZBZmHYPDb0CO+9CJUeuEKz6itbbrDK2vvOSwdHxV1I4PO2ZohZbExsMDfz8C/7u9IoL+B/244THZeIS/c0V4pk6qMo1MZZzfgfmBOiddjgA/LHDMeOAPsBj4HmlbQ3mQgGUiOiIjQa7ZXtTnwnZSvN5XyzWZSpv/o2nO5wjNqa5vu8Mo64iMpe3x0tJawWfY4VzoBKggQKCwslNO/TpWR01bI577cLQsKKqjsrPB6cMK05bGldoUQ9wODpJSTTK/HAD1lCTOWECIUuCqlzBFCTAGGSylvs9Z2ZVlq15HITl2iQaWEX96DdS9BgxgY8SmERDp6GbZTJLyeORu2tumKczt7Dj1kcuaBsGGVRiklM1bv5+MfD3N/13BmDOuAn0HNTHwRZ5ba9eSMpDewpsTrZ4FnKzjeD7hkS9u+7mwvioKNjLRvkKzLwDrnmpRJ4zWnetI4KXOuOnk1Co/h7ANhS4BAVpYsnJ8g3336Qxk5bYV8bMFWmZtf4JrrUbgUfNHZDmwFWgshmgOngBHAqJIHCCEaSSnPmF7eDex1r4jux9wgsAhrS4I7vYz4xeOaP+T3VOg/HW75m2v8Id5EZV4E3NkHwlqAgOlhFevW8SQQ2HMYM5hA7uvLeP/KFgJjbJxFVebvoIrgMUUipcwXQjwGrEGbbcyTUqYJIV5G04zLgb8KIe4G8oHzaD6TSo21yh4VBRI5FXx05Cf4bJxW9mRUErS53YYP+TjmtLabq+26FGej0awFCJR5WP+y+QuC8nN5acAUHj5SjY/ejSfI2v2sZN9BldWJjk5lvHnzZdOWpdSAom3wYMs+VYfSIQoLpdw0U8oXQ6T8oJuU56pQbkBlr+rn7PVZM41ZeFg/7ThQNpu6XI4c/pq8Zgys+HyV6Dvw9Ux6nDBtqYWt3Eh2NiQmamWTEhO112WxNAgsYtUqGDdOG8SV/by5dZIqjAbNz4Hlj8Gqp6H1n2HSOghrbfP1+DyVvaqfvQ9E2QcUKl5JysLDOmrXGv717Xtsiohh3AMvcyVtn2UZK9F3UJElsbKjija6CVtn8OZSA8wVqjVn6rarWO2V32HpaDi5Ffr8Hfo9D4YqNq6oLFX9LNlT7HkgKnpALflTzD2sJoalfU9gQS5P3vl3RleTJF7Po3awmaKeleU7oFLpRPtxdCrjzZs3mrbsmcGXTQ3429/Mf/app6zXADTLia1araxXG0qZukzPy/QtfN0WIaV+12DpAY2Ptx4qWPSwzp5drr7ZmmGTZevnvpWD39soM65ku05+B7ClhqY9+LqVDl+tteWqzRsViTNlkSw9oA7VGdy+QKva+26MlGdSdLk2n8bXq/pZ671s7S0rcs7Z07GbuZ8b9p2VbZ5fKQf8+wd59pKVisdu+g48mf/qrShF4sWKpOg3Mnhwxb93a22Yq3huV3v5uVKunKrlh8y/U8prmbpep8JDVDRCsadnq6hIpA7D6l8OnpPt/7FK9n17gzx14bpTbemBq2YPvjwucUaRKB+JC6koJwRsL4tkztS9cyfsMVOVzKw99lqmFtp79Cfo9Qj8+RXwU199paAiH4M9eSQV+DsAWLLEqVjWm1qFkTixB+M/2UrczN9YHN+LpnWDHWpLD/TwZ1hyTblrHTSvwlEN5M2bt8xILI16hgxxfrRis0n7zG7NjPVyPSl3fOr0NfkEehu/vZmKZh322lOzsrQHSA8TlwV2Hr8gO7y4RvZ6fZ08/McVp9pyBldHRvsiKNOWdyoSPZeLKIu5B7ncA53yheZQ/1dbKU8kO39SX6Ay/sKtYcme4khvWdGDpZPnOO3UJdn55e9kt1fXyv2/X3a6PUdwR/UYX8MZRVLF4j3di96RjSXD/JOS4LPPzC87/v36Ag5+/JK2pnqDGJj8A4R3deykrsCWhBpH8aZgfldeZ0mK7CkzZmh/i8xPdicWccOOOniw+ffN2X7svM6oxrVYOrkXAhgxaxNppy9VeLwrKLpMSyky1qjSob7mcFQDefPmLTMSPQfHltoqGxpcK/Ci/GbkA5pT/evHpMwzE3LpSVw9Y3DlNNAevGVm5Kj319YhtxPXmX7uquz9+joZO3213HH8gv3X5kHUjKT05vFO3xWbtygSKfWL4qjIJ1L0f9vQ/XLfo11k7gt15ab3Z2nlT/TGWf+Dq3+B3vIL9xY5HMVWBeHkdR7PvCZvmbFeRv9ztdxyxHciCb1lnKAnzigSFbrjYvSK4rA0la5ZU7NWBBxbw6L7JpFTEMC0Pct5/fmbQe/CvdnZMGQIbNhwY9/8+bBype02gZ07ze/ftctZ6TT0XjXQEtaq8/m67cPWrHgnr7Np3WA+m3ITo2ZvYuzcLcwZ142bW4U5KbzrsauKRBVAKRIfwZK/pWMHyZt3/Bv/ja9yujCWTc0W8forTV3zQC9cWFqJgPZ64UKYNMm2Nq5eNb//yhXnZCvC3rIgjpRqtaXeTWUo/WHLKEiH62xYO4ilU3ozes5mJszfyswxXenXtr4dgnqGKhvqaw5HpzLevHmTaUsvzE2l77z9qsxfPFbzh3w2UVuUypVYyqocPNj2Niqq9+JOnLFN2Ljgk1ttH54KedbxOjOv5sgh/9koWz33rVydesYFwioqAmXa8j70Xpeg7EC7Z5uj3JP9IIb9e7QEw5se99wiVBWdt+yNiIoyf1zHjq6RzRLOLPpkiznHnbYPT67poeN11q0ewKL4Xoybt4VHPt3Oe8M7cVfHxi4QWqE3SpG4AFf9roun0uk/wmfjQRbAg59BqwHWPuoYZZXAXXdpdezLcu+9lj9f9kb06wf9+8P69Tf2ucKHYQ1LysAWX42t5hx32T6cXhrTSXS8ztrVjCyc1JOJ87fyxJId5OQXcn/XcF3a9jSVetErR6cy3rx52rTlsoCdwkIpf/tIW4Tqwx5SZhzSRV6zmDNZ9OsnZd++pff172/ZjGHpRsye7fmCRBVVwrQmj7eF7HhLyLOOXM/Jl6PnbJKR01bIhZuOelocp/G2R8YcKNOWd+GSgJ28bPj2Kdj5KbS9A+6bCYE1nWjQCuZGuRs2wOzZMGGCbWYMSzfi4EEtea4k7h6uxcVpMpQtWJaWZn0k720hO5XBsV+GagF+zB7bjUc/3c7zy1LJyStk4i3NPS2WzZR9nHNzPTtpdDUeVSRCiEHAf9DWbJ8jpXyzzPuBQCLQFcgEhkspj7pbTnvR63dd9DAeTzvDlDoPUi9nG/zpGfjTNNcvQmWPErCErTfCEzb+oCAYONCOypdmPu/pHqDoAdm5U/M9lbwWT5gLdSbI6MdHo7vyxJIdvLxiD9n5BTzSt5WnxbKKucc5MtL8sb4SDW4Nj5VIEUL4Af8FBgNRwEghRFlP7EPABSllK+BdwMYezLM4UpmiLEUP40fPb+Gh/D9R7co+Xtq7gOzez7pnJUM9tKG5GxEZqQ3PSpbR8FRZk06dzO83d43uKndiK0UPyLhx8O67mhKJjoannrK/3ocXE+Bv4IORnbmnU2PeWr2fd9YeQLPCeC/mHudjx8wf68OTxtJYs30BjwMhjtrOKmi3N7CmxOtngWfLHLMG6G363x/IAIS1tj3tI5HS+Yz2hAQpJ3ZOkNnPh8lDj3eQ0fXS3JsYrZdRNytL84lERlpuy1M2fluv0RsN3L6eOW8n+QWFcupnu2TktBXy9W/3yEJXVG3QCUuPc0U/AW8AF/tIGgBbhRDbgXmmzl+PIUET4ESJ1yeBnpaOkVLmCyEuAaFoCqUUQojJwGSAiIgIHcRzDqcsHwV5tNz/LGPvns13h/sx4vN5XMiuC7hxKqyXHyAoCAICyg/JShqIPWXjt/UaPR0VZQ5fz5y3Ez+D4I37Ygk0Gpi5MZ3svAKm3xWNweChkPcKsPQ4v/CC9lPwBrea3lhVJFLKF4QQ/wBuByYAHwohkoC5UsrDrhbQVqSUs4BZAN26dfPuuW9FXMuApHHcbPyZf/36GM+se4kCeeNrSknRLCtueQhdXd+lqNNzV1kTc9hyjZ7utM0FIviig93JgAqDQfDS3dEEGf2YtTGdnPxCXrs3Fj8vUyaWHufRoyuP4iiLTc52KaUUQvwO/A7kAyHA50KItVLKqQ6e+xTQtMTrcNM+c8ecFEL4A7XRnO6VkzO7YMmDcO0cuXfOYs0vwykooxJXrdI2d+WbWaRkp9CmjbbvwAHzHYS1Ts/boqDKYkn+Cxe0++BKOS0FInz2mdY7eUL5OoJOARVCCJ4d3I4gfwPvf3+InPxC3r6/A/5+3rMihrc/zi7Bmu0LeALYhuaveAAwmvYbgMOO2tTQlFg60BwIAHYB0WWOeRT42PT/CCDJlrbrt6kv56XMk1vObJFXc6/qZEF0Mbs/k/KVBlL+u72UJ7dJKW/4WYYM8TJzuLXFj8oaf93lY3BVmRCbVhFzERX5QrxtgfCK7r8LfDoffn9QRk5bIf+yMFnm5BXocAFVG1zsI6kL3CelLGXkllIWCiHudEKB5QshHjMpKD9gnpQyTQjxsumClgNzgQVCiEPAeZMysUp2fjbvbHsHAIGgZZ2WRIdGExsWS0y9GNrUaYPRz+io6PpSWADrX4Jf/gMRvSEuEWpoBeuKrC6pqVqB3bJ4zBxuzmdQkrL+A2tDND1ySFwZQlwk/1//quXRlMScr0TPnJiKzGreEIJchLX77wLz4KP9WhFk9OOVFXvIzd/Gh6O6EGT0c7g9hePY4iOZXsF7e505uZRyJbCyzL5/lvg/G20WZBetQ1qzdvhaUjNSSc1IJSUjhZ9O/cTXh78GIMAQQLu67YgOMymXsBgia0ViEG6eHmddgC8mwaF10G0iDJoB/gHlDvM6c7ilTqEkZTsIS52eXgrA1Q7xoCCoU8f8eyWvVW+F5qkv315laO3+u+g6HrqlOYH+Bl74KpX4xGRmjelGtQClTNxNpc1sDwkKoU94H/qE9wE0E97pa6dLKZevDn3F4n2LAahhrEF0WDQxoTHFyqVB9QauE/CPfbBkFFw8Dne+qykSC3jSF20WS51CSWztICx1QPfdByNG2D6ad4dD3JbOUG+F5okv3xFl6MGAitG9Ign0NzDti92M/2QLc8d3p0ag93VtlbnWlvfdbRchhKBJjSY0qdGEgc0GAlBQWED6pfRixZKakUpCWgL5Mh+A+tXql5q1RIdFUyuglvPC7PsWvpwCxiAY9w1E9q7wcK9z3pnrFEpiTwdhqQOyN6rAHSN3WzpDvRWaJ758R5ShhwMqHujWlECjH39bupOxczfzyYQe1K7mJeZrPFug2S046lzx5s2ZhMTs/Gy584+dcuGehfKZjc/IO768Q8bMjyne7vzyTjlt4zS5cM9CuePsDpmdb8ea6AUFUv4wQ1s/5ONbpbx4wmE5PY6lRMOoKCkvXLC9HUtOWHsdsu526FtycFeGREFHEkS9JGlzVcoZ2eq5b+Ud72+U56/muPXcFeELjwWqaKN+BPoF0rFeRzrW61i871LOJdIy00jLSCMlI4UtZ7bwbfq3APgLf1qHtC6etcSExdCidgv8DGXstDlX4auHYe830GE43PUfMFZz56Xpi6VEwz17YPly28041mY3YHvtK3eM3K05uL3ODukAjszuvGTaPCimIbPGdGPKwm2MnL2JBQ/1pF7NQLfKYA5PpyK5GqVIbKB2YG1uanwTNzW+qXjf2WtnNX9LpuZzWXVkFUkHtNpQ1fyrERUadUO5+Nei8fK/Ic7th9tfg96P2r0IlVfaV639OmwRuqgDWrgQpk2D8+fLt2erecobopi8pEN1CkeVoTfcf6Bfu/p8Mr47kxKSGT7rNxZN6kXD2p69/14XMKMzQpvRVC66desmk5OT3XrOQlnIscvHip35qRmp7Du/j9zCXADqFhQSHRZDbERfzakfFkPdoLo2tW3OvjpggBfYVxMTtaKBZUlI0Dode4S21FZ0NCQne/ZCvVKLu5iia/ZVZQhsOXKeifO3mlZe7El4SLDLzmXtEfHa33AJhBDbpJTdHPqsUiQuQkryfn2fAxtfI61uU1Ja9yH1yjEOXzyMRLvnTWo0ISZMixKLDo0mKjSKYGP5h72i/tqjA8CKfh1JSfYJPXUqvP12+f0DB8JXX3nu1+aKHqAqKiYPsfPERcbO3UyNQH8WxfeiWVh13c9h6yPi7brZGUWiTFuuIC8LvnkS4+4lRLe7k+h7PybOtAjVtbxr7MnccyMM+VwKa46uAcAgDLSo3aKUv6V1SGtSU81Hn3jcvlqRGcdeo7Cluf+aNdC2LfzjH54pVqR3OG+lD9/xLjo1rcOi+F6MmbuZuJm/8emknrRu4PyCcCXHAhcv2vaIeInlzyUoRaI3l07C0tFwegf0fQ5ufbrU+iHVjdXp3rA73Rt2L96XmZVJWqbmyE/JSGHDiQ0sO7QM0Jz/odHtaDgqlqwj0WSlx5J7NgIQ3mFftfTrsNcoXJHT/fhxiI+HpUv163BtnRXo7SX1xkrCrsCLZl0xTWqzdEpvRs3ezIhZmgM+qrHjYfzmxgLm8PhAz40oRaInx36DpDHajGTEImh3h00fC60Wyq3ht3Jr+K2AFpJ98urJ4iix3X+kEnbb5+C/EICCazWpdimGzPYxfH9cM43VC67nsstyCHsdtkWzm/vu0/JHzKFXh2vPrEBvL2llD98B8/f3rbfg9tu1xcQ8oFTaNKhJ0pRePDhnsymaqwdtwuo4pOusVQgqwisGem5C+Uj0IvkTWPk01GkKIxZD/Xa6Nn/1ej4ff3aY7adTEY1Tya6TyuFLBymQBQDUD65fyiQWHRpNzQAXruluC44YhS05hIoYMgS++MK5jsgep5PePhKvdXjpiLXv0INe5hPnrzNy9iYuXs+jzq7u/PL1jYAXW8Wy5M4ribc50m1BOdvL4FZFkp8Lq6dB8jxo2R/unwvVQqx+TI+Zf1Z+FvvP7y/Oyk/NSOX4lePF7zev3bzYkR8bFkvbum0J8Ctfy8ursMVu4Oyv1FJPMHWq+fXo9fSS+kL4jrPY0tOWVJxuNoOduZTFnf/ezLmr2fzxeXdyToSaFcsSlvRkfDyEhHinI90WlLPdU1z9A5LGwvHf4OYnoP90KJuIaAZbLCu2/Laq+VejU/1OdKrfqfj47fsuEdIujXqxqey9mMKvp39l+eHlAPgb/Gkb0rZ41hIbFkuzWs3KJ096kpJ5Ja++an6xa2dNXPaaq/T0klaGPBNr2FKLrWSukZuDDxrVrsZN2b344vJm6j+whXPLupF9pF4psSrCktX2/fcr19doD2pG4iind2iLUF3PhKH/hdj7bf6oNeuGvYPWio4PDJScvX62uJ5YWkYaqZmpXMu7BkCwf3Bxscoi5dKwekOEnQmTLiE727LPxNLswdZ2K/uswJ2UHfXcfTc88EDFs8r4eK2a8sWL5Uvzg8tNfYmJMOHhHBoM34Ix9Crnvu5C1qEGNp+25CS1dWttn6V13XwFNSNxN7uTYPnjEBwGE9dA4052fdyav9XewJ6Kjxc0rN6QhtUbMiByAKAlTx69dLTYJJaWmcbCvQvJK8wDoG5QXc0kVlSwMjSGOkF17LpG3QgPN7/fGU9mVZgVuAtLSvmzz7RSObt2werVWumcIkJCzCuPkrg4+ECbVQTy/eJe1I/bQr17ttHoaGfi4hrZ9PmiSaqK5tZQisQeCgtg3XT49QOIvBkeSIAa9kdLWbOs2BvYY+/xBmGgRZ0WtKjTgqGthgKQW5DLgQsHSlVC3nhyY3HyZHiN8FLO/Pah7anm70CtMFvt4RX5SvSoXWWrucqLwli9EkujmJL11l577YbSvnDBuhIBl4c83RhLGNmZ1oPk4K2cbLWdlXs6cl8XC4MXM1SVaG5rKEViK1kX4POJcPh76B4Pg94AB1dZtBYZa68JX48I1QC/gGIlMcK0EOXV3KvsydyjmcQy09hxbgerjmpmJj/hR6s6rYo/ExMWQ6s6rfA3VPBI2TN8sxRjGR/vPmO0K4ebvqCgbJHRllFMSaU9dar187qpyGWRWGMxcj23B5MSkvm/z3aRnVfIqJ4RNrVRFaK5bUEpElv4Yy8sHqklG971PnStILTRBqxZVuxNwXBVwdkaATXo0agHPRr1KN6XkZVByrmU4mKVa4+t5YuDX2jX5RdE+9D2xVFisWGxhNcMv+FvsWf4ZukXGhJiucPVu3N21XDTF+whtsqo16jHwyFPwQH+zBvfnb8s3MZzy1LIyS9gws3NrX6ushdjtBlH6887s6GtA78WOGj6G2LhuAJgp2lbbmv7zqxHUo69K6R8rbGUb7eW8vhm/dq1grVlL5w9Xi8KCwvlsUvH5LeHv5Vvbn5Tjv52tOy6oGvx+i03L75ZTvluivxg+wdyw0tj5blafratc2HvAg6uWA/DkXU5bMEXFqewVUZ777uXrFtiiey8fDk5cauMnLZCfvTDIavHZ2VJ2b9/6cvp399rLscu8MH1SJ4B1ksp3xRCPGN6Pc3McVlSyk5ulayIwkLY+Bb88AY07gIjPoVajd12ensjTj1Vx0cIQUStCCJqRTCkxRAA8grzOHzxcKlKyHNS5lAQWQDvt6dhZi6x6VnEpGcRcySLqKhW1CjbsL3TLFfMHlw13PQFe4itMtobuODlgQ6B/n58OKoLTyXt4s1V+8jOK+CJ/q0rjGIsLKz4dVXAU4pkKNDX9H8C8APmFYlnyLkCyx6GfSug4yhtTXWjdzzojuBuc7zRYKRd3Xa0q9uO+9toYdHX866z//cUUt76G6n5J0hpEcza7rUBEHxA869WFIcfx4TF0CakDQHmOhzQYjfLXowrOmdX2QxdbQ/R4wu3R0ZfGfXYiNHPwHvDOxHob+C9dQfJzitk2qC2ZpVJUhJs2FB634YNytnuLhpIKc+Y/v8daGDhuCAhRDKQD7wppfzKUoNCiMnAZICICNscZWbJPKzlh2QcgEFvQs+H7V6EypvwFnN8sDGYzk170vnt74uVw4X6zUm7tQ0pl/eTlpHGz6d+Lk6eNBqMWvJkmxhib76ZmNAYmslCDHcPNX8xruicXTV6duUqinp94ZVhpUcn8DMI3hrWgUB/Ax//eJjsvAKm3xVVTpn4wuTSHbgsIVEIsQ5oaOat54EEKWWdEsdekFKWqysihGgipTwlhGgBfA/0l1IetnZuhxMSD62HzyeAMGihvS3+ZH8bXoYvlXaSUvL7td9vlHzJTCUtI43r+dcBqEEA0XvOE30ky2Qau06DC/kIRxbS8jSuWpzCmS/cXGLh8uVeaYJyF1JKXlmxl3m/HGFkjwheuycGg+GGMvGl35c1vDIhUUo5wNJ7QoizQohGUsozQohGwB8W2jhl+psuhPgB6AxYVSQOCKvlhqybDvWjNH9ISDPdT+MOyvYFO3eaP84bR0xCCBrVaESjGo24vdntABQUFnD0sil58pvZpAZdJHFgKPn+Wmn+sIt5xFxOIPZANjGznyf6x/upvSfd+zs+V5l3HB0iq2x/swgh+Med7akWYOC/Gw6Tk1fAW/d3wN9Pe/6q+MStGE+ZtpYD44A3TX+/LnuAECIEuC6lzBFChAE3A2/pLkleFiz/K6QkQdQ9cM//IED/VdTcgbm+wJJlx1fCE/0MfrSs05KWdVpyT+hleGocOUbB/qZBpLSoRlrzaqTcnM8POz4o/kxEzwhiwiAm/TNiw2JpV7cdQf5e2hnq7cBy1MSnMussIoTg6YHtCPL3499rD5CTX8h7Izph9DN4e+yA2/CUInkTSBJCPAQcA+IAhBDdgIellJOA9sBMIUQhYEDzkeyx1KBDXDwBSx+EM7vhthegz9992h9iri9IS4OoqNIVKnx2xGQa/gWuW0eH9Cw6pGeZquV9w2VDbqmVJ5PPJrPyyEpAS55sE9LmRsmXsBha1m7p+WKVjvgzrCkeS0Pku+82H6RQhDL2W+Xx/q0JNBp4feU+cgsK+XBUZwL9/bw9dsAtVN2ijcd+haVjID8Hhs2BtoPcI5wLsVS9+6mnoGPHSjJissO38Mf1P0qFIKdmpnIl9wqgVU5uX7d9caRYdFg04TXC3Vus0l4Du6OLg5srolj2c5XJ2O9iEn87yj+/TuPWNvWYOborotDP64sU2IJX+ki8mq1zYdVUzQ8yYjHUa+NpiXTBklXjyhXffbjLYcfwr35wfW6LuI3bIm4DNMfp8SvHi6sgp2SksGTfEhILEwGoE1inVIn96NBoQquFVnQK57B3FrBwoWOLgycmWv+cMvbbzNjezQj0N/DMlymMm7eFzGXd+f67G12ptxUpcAdVS5Hk58Kqp2HbfGh9O9w3G6rV8bRUumFp2fPZs+HIkar3cJdFCEFkrUgia0VyZ4s7AS158uCFg6WKVf56+lcKpZZV1phaRId3J7ZBx+KVJ4ONwfoIZI8/IztbW5/FHNbMT7bWw1LGfpsZ3j2CQH8/nlq6i+v1tiACuiNztdp7VdG1VHUUyZWz2nrqJzbDLU9pPhFP28h1pqgv+OtfyxdYrYoPty0YDUaiQqOICo0irm0cZGdz/d472JO+ibQW1UhpfonUK9dYe3I9YKqcXLtFKZNYmzptMDpSwNOeWUBSkvlFvsC6I91WhaWM/XZxT+cmJC028EujHTQYsZk/knpQmK2tQFrVXEtVQ5Gc2gZLRmsVfO+fBzHDPC2RywgK0tYLMkdVe7gdIimJ4NXf0w3oduA6kAmc4Pz8j0jtH1VsEvvxxI98degrAAIMAbQLbVe8OFhMWAyRtSIxCEPF57JnFmBpVhEZad38pMxWLmNQTCO+etlAvXu202DkJs4u7Unh9UCfiYrUi8qvSHYuhm+egBoN4KHvoFEHT0vkclRFUiew0GHX3XOEW8c9zK3htwKav+XU1VOkZqZq1ZAzUll2aBmL9i0CoKaxJlFhUTfWcAmNoUF1MwUcbJ0FWPpSX3jBuvlJma3swp6IbE1HN+DnL7pR775kGozcRNvTPYmLq1r3tvJGbW3eBGv/CZv+C836wAPzoXqYp0VzCyq3zAmciF7KL8wn/VJ68awlNSOVgxcOki/zAahfrX6p9Vuiw6KpFVDLNrnsjdiyN4TIF9ZHcQOO/HaKbt2GtEx+8dtKozqBLJrciyZ1HFj4zYM4E7VVORVJl04y+YkIOPIj9JgCA19zeBEqX8VVFTgqPTpr4ez8bPZf2F/szE/LSOPo5aPF7zer1ayUcmlXtx2BfoGWZavoS3VUdjXyKMbZKOjtxy8wbt4WagUZWRTfk8hQ30luVoqkDN0igmVyfHWtam/n0Z4Wx+fw2cGpXoK7WAtfyrlUKnkyJSOFc1nnAPAX/rQOaV1qWeMWtVvYljzpaC+ockiKsZSLNXUqzJhhWxuppy4xZu5mAvwNLIrvRct65RZJ8EpUHklZZCGMXwlNu3taEq/DWl/rLdWC7UZPwV0cvVRbBtJ73UF6px6EmK4QN4OzBZduhCBnprLyyEqSDiQBWvJkVGhUcZRYbFgsjas3Lp886Wh2uspqL8aaf9GWsUpMk9osmdybB+dsYvjM31g4qSftGtpowvRVHF0Ry5u3rp06WF0NzNcpWhHx6adtXxHRlsXpfGHxPrP4iuA2rhBYUFgg0y+my+WHlsvXNr0mR60YJTsndi5eefLWJbfKR9Y9Iv+3439y44mN8nzWecfvga/cOzdQ0ddj7+KOB89ekT1eWys7vrRGppy86N4LcQB8cIVE11LJ/SGODr5tqcvns4NTXxHcxuKIBmGgee3mNK/dnLta3gVAXkEeBy4cKC73kpqRyk8nf0KimaebVG9M7IvdiNl0hJj0LNofzSL41v4qPNgOKgpws6VAQEla1a9B0pTejJq9mZGzN5EwsQddIsqtllEpqJyKpJLjaKFWW/panw0d9hXBnVB4Rj8j0WHRRIdFM5zhAFzLu1bsb0nJSGFXVAqrmzUCwICgZe0axG5/g+hQzSTWKqQVRkOZgZYKDy6FJcumI19dZGh1kh7uzYOzNzFmzmbmje9OzxYuLLvjIZQi8UEc7Yts6Wt9dnDqLsGddejrrPCqG6vTvWF3uje84Q/MyMogLSNNy3HJSGH98fV8efBLAAL9AouLVRZl5zet2RShstqt4uhX16RONZZO6c2o2ZsY98kWZo/tRp/W9fQX0INUzqgtR1dI9BEcDbJxtHiszwxOXS24HmGyHgi1lVJy8srJYnNYakYqe8/vJSs/C4BaAbWK64gVRYvVC65cHZ0eOPvVZVzNYfSczaSfu8ZHo7vQv72lFcY9gwr/LUNlVyTOPNA+qyS8Ab3CZL3gS8gvzOfwxcOkZaax+9xu0jLTOHjhIAWyAIAGwQ1KRYlFhUZRM6CmW2X0xjB0Z7+6i9dzGTtvC3tOX+aDkZ0ZHNvIdcLaiVIkZajsigS8oi+qHNjTW+mRZODFZOVnsf/8flIyUoqTJ49fOV78fvPazUvVE2tbt63l5Eknqcw5kpez85jwyVZ2nrjIvx/oyD2dm3haJEDlkVRJlElbB+wNf/MVh76DVPOvRqf6nehUv1PxvovZF0nLTCs2if16+le+Sf8GAH+DP21D2pbytzSr1UyXlScr88q/tYKMJE7swUMJW/lb0k5y8gsY3j3C02I5hVIkiqqLvb2Vz0YiOE6doDrc3ORmbm5yM6D5W85eP0tqRiq7M3aTlpHGivQVLN2/FNCc/1GhUcWKJSY0hobVG1pdebLsxHDnTvPHeVs0t6NUD/Rn/oQeTF6wjWlfpJCTX8jY3s08LZbDeESRCCEeAF5EW5e9h5TSrB1KCDEI+A/gB8yRUr7pNiEVlR97w99UmCxCCBpWb0jD6g0ZEDkAgEJZyNFLR4tNYqkZqSzYs4D8Qq1YZWhQaKl6YjGhMdQJqlPcprmJoaVJXiWZ/AEQZPRj9tiuPPrpDv75dRrZeQVMvrWlp8VyCE/NSFKB+4CZlg4QQvgB/wX+DJwEtgohlksp97hHREWlxxFTlbIplsMgDLSo04IWdVowtNVQAHILctl/fn+pSLGNJzcWJ082rdm0WKmcTI5l/cZ2wI1quWlpEBUFe0r82ivj5C/Q34+PRnfhySU7eX3lPrLzCnn8tlZWZ3DehkcUiZRyL2DtZvUADkkp003HLgGGAkqRKPShCpqq3EWAXwCx9WKJrRdbvO9q7tVif8uuP1L5+cgOVh1ZBUDUR35kn2pFVnoMWUdiyEqP5fZBLZk2zb/ST/6Mfgb+M6ITgf4G3ll7gOy8Ap4e2NanlIk3+0iaACdKvD4J9LR0sBBiMjAZICLCtx1XCjehTFVupUZADXo26knHkJ4seQp+Wwf+tTOo1jyVai1SqNY8lVrd1lK37xcA/EAQmfXaE3N/DPXCYjmXG0N4YLhPdbC24u9n4F8PdCTQaOB/PxwmO6+Qf9zZ3meu1WWKRAixDmho5q3npZRf630+KeUsYBZo4b96t6/QB6/LDVCmKrdTMsYh/1IYV3b25crOvqZ3JQH1T9D97lSGTExh74VUkvYnsWDPAgBqB9YuDkEuynMJq1Y5FqwzGASv3xtLoL8f8345Qk5+Aa8MjcFg8H5l4jJFIqUc4GQTp4CmJV6Hm/YpfBSfLVGv0BVLEVm33w6dOgmioyOIi4sgKGgIAHmFeRy+eLjU+i2zU2ZTKAsBaFS9UakQ5KjQKKobq3vfoMUGhBBMvyuKIKMfH/94mJz8QmYM64CflysTbzZtbQVaCyGaoymQEcAoz4qkcIbKnBugsJ2rV83vj4w0n9dpNBhpV7cd7eq24/429wNwPe86+87vK06c3J2xm7XH1gIgEDSr1YLfd8Rw+JcYrqfHkvNOGxYsMPrEoEUIwbRBbQkyGnhv3UFy8gt5J64jRj+Dp0WziKfCf+8FPgDqAd8KIXZKKQcKIRqjhfkOkVLmCyEeA9aghf/Ok1JWkihy30DvEZ2vVHqvqrhrBF/DwoKBNe2owBJsDKZLgy50adCleN+F7AvFJfZX70zlSr2faDxWs6IX5hlJP96OyUujuf8WrZ5Ys1rNMAjv7JyFEDw5oA2B/n7MWL2P3PwC3h/ZmUB/55M9XYGnoraWAcvM7D8NDCnxeiWw0o2iKUy4wgxVyRPDfRpz3/f8+TBqFBw4oK9i6dTJ/P6OHZ1rNyQohD7hfegT3ocjn8LytyXGsNOaM795GsEtUthdsJwdPy8BoIaxBtGh0aX8LQ2CG3iVg/svfVsSZDTw0jd7mLJgGx+P7kqQ0fuUiaq1pTCLK5bxrsz1k3wdS993SfT6rtzxHFi6nk/mF3DL3Uc0k1hmGikZKRy4cKA4ebJetXqlkiejQ6OpHVhbH6GcYNHm4zz/VQq9W4QyZ1w3ggP0nwOoWlsK3XGFGUpF23ovlr7vkujlz3LHc2ApRWjEcD+CglrRKqQV97a+F4CcghwtebKEM3/DiQ3Fn4usFVmcPBkTFkO7uu0I8nfvQzuqZwSB/gae/nwX4+ZtYd747tQM8p6VYJUiUZjFVWYoFW3rfmzxfVj6vsuilz/L1c+BPcoq0C+QDvU60KFeh+J9l3MvF688mZqRytbft/Jt+rcA+At/Woe0Li6xHxMWQ4vaLfA3uLY7HdY1nECjgSeX7GT03C0kTuhB7WDvUCbKtKUwiznzQ2QkvPACjB6tZhG+gj2LmZU9zhyOLr3iSyG4ljh77SypmamkZaQVR4tdybsCaJWTi1aeLFIuTWo0cYm/5bu033ls0Q5a1q/Bwod6EFpDn1L+aj2SMihFog/Z2bBwIbz6Khw7dmO/8mv4Dvb4ukqucdO6NSxZAuvX33jfBxaDdCuFspDjl48X1xNLyUhhX+Y+cgtzAQgJDCk1a4kJi6FuUF1dzv3jgXNMTkwmom4wn07qSf1azt9Q5SNRuISgIAgIKK1EQOV+eBPWRvz2+LrKmptGj3bOj1HZ84YMwkCz2s1oVrsZd7a4E9CSJw9eOFjK3/LLqV+Ki1U2rt64nDM/2Bhs97n/1KYen0zozqSEZIbP2sSnk3rSuE416x90EUqRKCpE5X5YxtNmG1tCtJ3xdTnrx6iKz47RYCQqNIqo0Cji2mrFP6/nXSctM+2GSSwzje+OfQeYKifXblHKJNY6pDVGg3Xfx00tw0ic2IMJn2wlbuZvLI7vRdO69islPVCKRFEhKvfDPN5Q7sWWEf/dd2vfVcnO210FjtWzoxFsDKZ7w+50b9i9eF9mVmZxJeSUjBR+PPEjXx36CoAAQwDtQttpuS2hmmksolaE2eTJbs3q8ml8T8bM3cIDH//GovietKhnIePThSgfiaJCKrud21FckWdjL9aWkDf33UVFwS+/QJ06rpdPPTu2I6Xk5NWTpGXcUC57z+8lKz8LgJrGmsX+lqK/9YPrF39+z+nLjJm7GSEEi+J70qaBHWUCTCgficJl+GLuh6MmJ3s+5w1mG0sj/tattb/mZix79sDy5e5Rdr747HgKIQRNazalac2mDGo+CID8wnzSL6WX8rfMS51HgSwAoH61+ppJrJ42c5k3MZr4+XsYMWsTCx7qQXRj9yVSqhmJolLh6CjY3s95w4wkOxuGDIENG0rv798fVqyAf/7T/Izlqae0ciSVISS3qpGdn82+8/uKa4qlZqRy7PKNaJgm1SM4e64++VnhTL99EHe170agn23hwSr8twxKkVRdHO3g7f2ct5ht5syB+Pjy+xMStL/mrsmcz+Sbb7T/K0vOR1XiUs6lYn9LakYqO//YzYWcTAD8hB9t67YttYZL89rN8TOUr9elTFsKhQlHTU72fs5bzDYHDpjfn5YGL71UvkxIVFT5a1q3TssXWrpUrRXji9QOrM1NjW/ipsY3AZq/JfXscR7+7CsuFhymoNZFVh5ZSdKBJACC/YOJCo0q5W9pVL2RUzIoRaKoVDgaKeTI57yh3EtFcptTdjt3an6SsixbVrlzPqoSQghiG0by1fiHGTN3M7uTr/HRg51p3vh68awlNSOVhXsXkleYB+B8oqSUstJtXbt2lYqqSVaWlAMGSAk3tgEDtP2u+JynsVfuhITSxxZtgweb3z91qnuvR6Ev56/myDve3yhbPfetXJVyptR7ufm5MvVcqlyyd4l8/qfnJZAsHexzlY9EUekoWerDHpOTo5/zNPbIbcm3M3y4ZV+LmpH4Npey8hj/yRZ2n7zEu8M7cXfHxmaPU872MihFUjXxdKa5r2BO8YB3BA8oXMPVnHwmzt9K8tHzzBjWgQe6NS13jFIkZVCKpOpRURQVKAVjC746I1PYRlZuAZMXJPPTwQxevSeG0b0iS73vc4pECPEA8CLQHughpTTb6wshjgJXgAIg39aLVIqk6mEpfHf27PLRSJ4eaauZk8JTZOcV8Oin21m/7w/+cWcUD93SvPg9Xwz/TQXuA2bacGw/KWWGi+VR+DiWwnfffx9SUkrvsxSN5I4O3htqdCmqLkFGPz4a3ZUnluzglRV7NMXSr5XT7XpEkUgp9wIuWfRFUTWxFAZbVokUUTaXwl0dfGUvra7wfgL8DXwwsjN//2wXb6/ZT3ZeAU/9uY1TbZYvJ+ldSOA7IcQ2IcRkTwuj8F7i4jSTla2UzQ+pqIPXE2+o0aVQ+PsZ+HdcJ4Z3a8oH3x/i9ZV7nWtPJ7nKIYRYBzQ089bzUsqvbWzmFinlKSFEfWCtEGKflHKjhfNNBiYDREREOCSzwncpSr677z5YtariY82VUXdXB69Kqyu8BT+D4I37Ygk0Gpj90xGn2nKZIpFS2jE+tNjGKdPfP4QQy4AegFlFIqWcBcwCzdnu7LkVvkdQEIwYYV6RxMdDSIjlaCR3dfBxceXLlrhrfRCFoiwGg+Clu6MJMvrx/AzH2/Fo+K8Q4gfg7+aitoQQ1QGDlPKK6f+1wMtSytXW2lVRW1UXd1X/dVZGFWar8CaklBgMBp8L/70X+ACoB1wEdkopBwohGgNzpJRDhBAtgGWmj/gDi6SUr9nSvlIkVZuqltmuUOiBz+WRuBqlSBQKhcI+nFEk3h61pVAoFAovRykShUKhUDiFUiQKhUKhcAqlSBQKhULhFEqRKBQKhcIplCJRKBQKhVMoRaJQKBQKp1CKRKFQKBROoRSJQqFQKJxCKRKFQqFQOIVSJAqFQqFwCqVIFAqFQuEUSpEoFAqFwimUIlEoFAqFUyhFolAoFAqnUIpEoVAoFE6hFIlCoVAonEIpEoVCoVA4hVIkCoVCoXAKjygSIcTbQoh9QojdQohlQog6Fo4bJITYL4Q4JIR4xs1iKhQKhcIGPDUjWQvESCk7AAeAZ8seIITwA/4LDAaigJFCiCi3SqlQKBQKq3hEkUgpv5NS5ptebgLCzRzWAzgkpUyXUuYCS4Ch7pJRoVAoFLbh72kBgInAUjP7mwAnSrw+CfS01IgQYjIw2fQyRwiRqpuEriEMyPC0EDag5NQXJae+KDn1o62jH3SZIhFCrAMamnnreSnl16ZjngfygU+dPZ+UchYwy9RuspSym7NtuhJfkBGUnHqj5NQXJad+CCGSHf2syxSJlHJARe8LIcYDdwL9pZTSzCGngKYlXoeb9ikUCoXCi/BU1NYgYCpwt5TyuoXDtgKthRDNhRABwAhgubtkVCgUCoVteCpq60OgJrBWCLFTCPExgBCisRBiJYDJGf8YsAbYCyRJKdNsbH+WC2TWG1+QEZSceqPk1Bclp344LKMwb1VSKBQKhcI2VGa7QqFQKJxCKRKFQqFQOEWlUCS+UHJFCPGAECJNCFEohLAYBiiEOCqESDH5jhwOx3MUO+T0aPkaIURdIcRaIcRB098QC8cVmO7lTiGE24I1rN0fIUSgEGKp6f3NQohm7pKtjBzW5BwvhDhX4h5O8oCM84QQf1jKDRMa75uuYbcQoou7ZTTJYU3OvkKISyXu5T89IGNTIcQGIcQe0+/8CTPH2H8/pZQ+vwG3A/6m/2cAM8wc4wccBloAAcAuIMqNMrZHS/j5AehWwXFHgTAP3kurcnr6XppkeAt4xvT/M+a+c9N7Vz1wD63eH+AR4GPT/yOApV4q53jgQ3fLVkaGW4EuQKqF94cAqwAB9AI2e6mcfYEVHr6XjYAupv9ropWoKvud230/K8WMRPpAyRUp5V4p5X53nc9RbJTTG8rXDAUSTP8nAPe4+fwVYcv9KSn/50B/IYRwo4zgHd+jVaSUG4HzFRwyFEiUGpuAOkKIRu6R7gY2yOlxpJRnpJTbTf9fQYuIbVLmMLvvZ6VQJGWYiKZNy2Ku5ErZG+gNSOA7IcQ2U9kXb8Qb7mUDKeUZ0/+/Aw0sHBckhEgWQmwSQtzjHtFsuj/Fx5gGQZeAULdIZ0YGE5a+x2EmE8fnQoimZt73NN7wPNpKbyHELiHEKiFEtCcFMZlTOwOby7xl9/30hlpbNuHukiuOYIuMNnCLlPKUEKI+Wp7NPtNIRzd0ktPlVCRnyRdSSimEsBTHHmm6ny2A74UQKVLKw3rLWon5BlgspcwRQkxBm0Xd5mGZfJXtaM/jVSHEEOAroLUnBBFC1AC+AJ6UUl52tj2fUSTSB0quWJPRxjZOmf7+IYRYhmZ+0FWR6CCnW8rXVCSnEOKsEKKRlPKMadr9h4U2iu5nuhDiB7QRmKsViS33p+iYk0IIf6A2kOliucpiVU4pZUmZ5qD5prwNnyinVLLDllKuFEL8TwgRJqV0azFHIYQRTYl8KqX80swhdt/PSmHaEpWk5IoQoroQombR/2hBBN5Yxdgb7uVyYJzp/3FAuZmUECJECBFo+j8MuBnY4wbZbLk/JeW/H/jewgDIlViVs4xt/G40m7q3sRwYa4o26gVcKmH29BqEEA2L/GBCiB5o/a9bBw+m888F9kop37FwmP3305MRBDpGIhxCs+ntNG1F0TCNgZVlohEOoI1In3ezjPei2RpzgLPAmrIyokXP7DJtae6W0VY5PX0vTecPBdYDB4F1QF3T/m7AHNP/NwEppvuZAjzkRvnK3R/gZbTBDkAQ8Jnp2d0CtHD3PbRRzjdMz+IuYAPQzgMyLgbOAHmmZ/Mh4GHgYdP7Am0RvMOm79liVKSH5XysxL3cBNzkARlvQfPD7i7RXw5x9n6qEikKhUKhcIpKYdpSKBQKhedQikShUCgUTqEUiUKhUCicQikShUKhUDiFUiQKhUKhcAqlSBQKhULhFEqRKBQKhcIplCJRKFyMEKK7qehhkKl6QZoQIsbTcikUeqESEhUKNyCEeBUtm70acFJK+YaHRVIodEMpEoXCDZhqWW0FstFKYxR4WCSFQjeUaUuhcA+hQA20VemCPCyLQqErakaiULgBoa0XvwRoDjSSUj7mYZEUCt3wmfVIFApfRQgxFsiTUi4SQvgBvwohbpNSfu9p2RQKPVAzEoVCoVA4hfKRKBQKhcIplCJRKBQKhVMoRaJQKBQKp1CKRKFQKBROoRSJQqFQKJxCKRKFQqFQOIVSJAqFQqFwiv8HcS67vqiGFEgAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       0.90      0.93      0.92       100\n",
            "         1.0       0.93      0.90      0.91       100\n",
            "\n",
            "    accuracy                           0.92       200\n",
            "   macro avg       0.92      0.92      0.91       200\n",
            "weighted avg       0.92      0.92      0.91       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 3 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.01,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "l-oz7fJe0UXc",
        "outputId": "5e7c700d-2ce2-49db-e10e-ce444f5c834b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.5373134328358209\n",
            "W1: [ 0.01999928 -0.01868992]\n",
            "b1: [-0.02033481]\n",
            "W2: [0.0090555  0.03025936]\n",
            "b2: [-0.01009131]\n",
            "W3: [-0.03285424 -0.00251668]\n",
            "b3: [-0.02987213]\n",
            "Epoch 2 / 200\n",
            "loss: 0.53\n",
            "W1: [ 0.03196411 -0.01718711]\n",
            "b1: [-0.03077899]\n",
            "W2: [-0.00500262  0.0483135 ]\n",
            "b2: [-0.02046311]\n",
            "W3: [-0.01817077 -0.03760123]\n",
            "b3: [-0.03979307]\n",
            "Epoch 3 / 200\n",
            "loss: 0.35\n",
            "W1: [ 0.03951594 -0.01703764]\n",
            "b1: [-0.04070401]\n",
            "W2: [-0.02462661  0.04625301]\n",
            "b2: [-0.03062091]\n",
            "W3: [-0.02640123 -0.0513103 ]\n",
            "b3: [-0.03014534]\n",
            "Epoch 4 / 200\n",
            "loss: 0.35\n",
            "W1: [0.05345152 0.01232587]\n",
            "b1: [-0.03084243]\n",
            "W2: [-0.02450477  0.05230597]\n",
            "b2: [-0.04075056]\n",
            "W3: [-0.01851008 -0.05566127]\n",
            "b3: [-0.03996725]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       0.96      0.64      0.77       100\n",
            "         1.0       0.73      0.97      0.83       100\n",
            "\n",
            "    accuracy                           0.81       200\n",
            "   macro avg       0.84      0.80      0.80       200\n",
            "weighted avg       0.84      0.81      0.80       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Madaline Results (4 neurons)"
      ],
      "metadata": {
        "id": "Yp2y4wsmKs5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 4 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.0001,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pTlgo7pNh0Vi",
        "outputId": "8c874900-adb1-449d-89a3-55c1376de7a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.3781094527363184\n",
            "W1: [-0.00012744 -0.00039955]\n",
            "b1: [-0.0002999]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [ 3.74797206e-04 -6.59544221e-05]\n",
            "b3: [-0.00030007]\n",
            "W4: [-0.00045818  0.00025898]\n",
            "b4: [-0.00050013]\n",
            "Epoch 2 / 200\n",
            "loss: 0.18\n",
            "W1: [-0.00013174 -0.0004157 ]\n",
            "b1: [-0.00039991]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [ 0.00035165 -0.00012933]\n",
            "b3: [-0.00040008]\n",
            "W4: [-0.00058566  0.00018459]\n",
            "b4: [-0.00050014]\n",
            "Epoch 3 / 200\n",
            "loss: 0.17\n",
            "W1: [-0.00023854 -0.00048009]\n",
            "b1: [-0.0003999]\n",
            "W2: [0.00038604 0.00048675]\n",
            "b2: [-0.00060017]\n",
            "W3: [ 0.00031594 -0.0002285 ]\n",
            "b3: [-0.00040009]\n",
            "W4: [-0.0004656   0.00036107]\n",
            "b4: [-0.00060016]\n",
            "Epoch 4 / 200\n",
            "loss: 0.07\n",
            "W1: [-0.00029079 -0.00036586]\n",
            "b1: [-0.00049991]\n",
            "W2: [0.00038604 0.00048675]\n",
            "b2: [-0.00060017]\n",
            "W3: [ 0.00032348 -0.00029435]\n",
            "b3: [-0.00040009]\n",
            "W4: [-0.00049929  0.00034837]\n",
            "b4: [-0.00060017]\n",
            "Epoch 5 / 200\n",
            "loss: 0.02\n",
            "W1: [-0.00029079 -0.00036586]\n",
            "b1: [-0.00049991]\n",
            "W2: [0.00038604 0.00048675]\n",
            "b2: [-0.00060017]\n",
            "W3: [ 0.00032348 -0.00029435]\n",
            "b3: [-0.00040009]\n",
            "W4: [-0.00054568  0.00033304]\n",
            "b4: [-0.00060017]\n",
            "Epoch 6 / 200\n",
            "loss: 0.04\n",
            "W1: [-0.00029079 -0.00036586]\n",
            "b1: [-0.00049991]\n",
            "W2: [0.00038604 0.00048675]\n",
            "b2: [-0.00060017]\n",
            "W3: [ 0.00032348 -0.00029435]\n",
            "b3: [-0.00040009]\n",
            "W4: [-0.00055896  0.00038771]\n",
            "b4: [-0.00060015]\n",
            "Epoch 7 / 200\n",
            "loss: 0.04\n",
            "W1: [-0.00033718 -0.00038119]\n",
            "b1: [-0.0004999]\n",
            "W2: [0.00038604 0.00048675]\n",
            "b2: [-0.00060017]\n",
            "W3: [ 0.00032348 -0.00029435]\n",
            "b3: [-0.00040009]\n",
            "W4: [-0.00054926  0.00042099]\n",
            "b4: [-0.00060015]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       1.00      0.97      0.98       100\n",
            "         1.0       0.97      1.00      0.99       100\n",
            "\n",
            "    accuracy                           0.98       200\n",
            "   macro avg       0.99      0.98      0.98       200\n",
            "weighted avg       0.99      0.98      0.98       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 4 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.0005,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mVFwVjR94vTs",
        "outputId": "be52ce7e-c6b8-4302-a3ec-3fec70f7cca5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.4975124378109453\n",
            "W1: [-0.00118432 -0.00106362]\n",
            "b1: [-0.00100089]\n",
            "W2: [0.00031242 0.00077556]\n",
            "b2: [-0.00100031]\n",
            "W3: [ 0.00129383 -0.00142994]\n",
            "b3: [-0.00100103]\n",
            "W4: [-0.00129865  0.00117701]\n",
            "b4: [-0.00100105]\n",
            "Epoch 2 / 200\n",
            "loss: 0.23\n",
            "W1: [-0.00140555 -0.00075758]\n",
            "b1: [-0.00200074]\n",
            "W2: [0.0010105  0.00127987]\n",
            "b2: [-0.00099997]\n",
            "W3: [ 0.00108752 -0.00132146]\n",
            "b3: [-0.00150173]\n",
            "W4: [-0.00141668  0.0016316 ]\n",
            "b4: [-0.00100115]\n",
            "Epoch 3 / 200\n",
            "loss: 0.27\n",
            "W1: [-0.00139882 -0.00172706]\n",
            "b1: [-0.00200096]\n",
            "W2: [0.00115085 0.00159799]\n",
            "b2: [-0.00150046]\n",
            "W3: [ 0.00143307 -0.00158861]\n",
            "b3: [-0.00150165]\n",
            "W4: [-0.00136983  0.00128044]\n",
            "b4: [-0.00200206]\n",
            "Epoch 4 / 200\n",
            "loss: 0.15\n",
            "W1: [-0.00135278 -0.00152585]\n",
            "b1: [-0.00250093]\n",
            "W2: [0.00136777 0.00125143]\n",
            "b2: [-0.00200087]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00135979  0.00151128]\n",
            "b4: [-0.00200218]\n",
            "Epoch 5 / 200\n",
            "loss: 0.12\n",
            "W1: [-0.00168038 -0.00159274]\n",
            "b1: [-0.00250088]\n",
            "W2: [0.00103465 0.00168218]\n",
            "b2: [-0.00200074]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.0019637   0.00131993]\n",
            "b4: [-0.002002]\n",
            "Epoch 6 / 200\n",
            "loss: 0.13\n",
            "W1: [-0.00174348 -0.00191435]\n",
            "b1: [-0.00250106]\n",
            "W2: [0.00121609 0.00163693]\n",
            "b2: [-0.00200091]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00193853  0.00135095]\n",
            "b4: [-0.00250235]\n",
            "Epoch 7 / 200\n",
            "loss: 0.12\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00251517  0.00144905]\n",
            "b4: [-0.00250256]\n",
            "Epoch 8 / 200\n",
            "loss: 0.05\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00212396  0.00169438]\n",
            "b4: [-0.00300294]\n",
            "Epoch 9 / 200\n",
            "loss: 0.04\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.0024464   0.00163888]\n",
            "b4: [-0.00300327]\n",
            "Epoch 10 / 200\n",
            "loss: 0.06\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00260213  0.0020685 ]\n",
            "b4: [-0.00300319]\n",
            "Epoch 11 / 200\n",
            "loss: 0.02\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00292228  0.00183354]\n",
            "b4: [-0.00300327]\n",
            "Epoch 12 / 200\n",
            "loss: 0.03\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00238391  0.00201052]\n",
            "b4: [-0.00350318]\n",
            "Epoch 13 / 200\n",
            "loss: 0.04\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00248091  0.0023034 ]\n",
            "b4: [-0.00350329]\n",
            "Epoch 14 / 200\n",
            "loss: 0.0\n",
            "W1: [-0.00202817 -0.0017949 ]\n",
            "b1: [-0.0025012]\n",
            "W2: [0.00139735 0.00159163]\n",
            "b2: [-0.00200124]\n",
            "W3: [ 0.00136332 -0.00137619]\n",
            "b3: [-0.00200181]\n",
            "W4: [-0.00248091  0.0023034 ]\n",
            "b4: [-0.00350329]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       1.00      1.00      1.00       100\n",
            "         1.0       1.00      1.00      1.00       100\n",
            "\n",
            "    accuracy                           1.00       200\n",
            "   macro avg       1.00      1.00      1.00       200\n",
            "weighted avg       1.00      1.00      1.00       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Madaline Results (10 neurons)"
      ],
      "metadata": {
        "id": "Qt_2s8HBKkd1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 10 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.01,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1mQx9PbGhyXC",
        "outputId": "7ab58e08-3735-40b6-cd9c-2a1419929e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.5074626865671642\n",
            "W1: [-0.00449166  0.00139753]\n",
            "b1: [-0.01000403]\n",
            "W2: [-0.00815933  0.01006695]\n",
            "b2: [-0.01998125]\n",
            "W3: [0.00131494 0.02063177]\n",
            "b3: [-0.02015728]\n",
            "W4: [ 0.02474997 -0.00124232]\n",
            "b4: [-0.03051395]\n",
            "W5: [-0.02141443 -0.00056342]\n",
            "b5: [-0.02004181]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.0096258 -0.0353953]\n",
            "b7: [-0.02031051]\n",
            "W8: [-0.00445004  0.00198922]\n",
            "b8: [-0.01000343]\n",
            "W9: [-0.00454089  0.00166856]\n",
            "b9: [-0.0100034]\n",
            "W10: [-0.00434511  0.00209132]\n",
            "b10: [-0.01000384]\n",
            "Epoch 2 / 200\n",
            "loss: 0.22\n",
            "W1: [-0.00449166  0.00139753]\n",
            "b1: [-0.01000403]\n",
            "W2: [-0.00815933  0.01006695]\n",
            "b2: [-0.01998125]\n",
            "W3: [0.00447757 0.02505918]\n",
            "b3: [-0.02040738]\n",
            "W4: [0.03302932 0.00115858]\n",
            "b4: [-0.03063383]\n",
            "W5: [-0.01454661 -0.00432588]\n",
            "b5: [-0.03035814]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.01216591 -0.02855176]\n",
            "b7: [-0.03032487]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.00908851 -0.00319185]\n",
            "b9: [-0.02021021]\n",
            "W10: [-0.00434511  0.00209132]\n",
            "b10: [-0.01000384]\n",
            "Epoch 3 / 200\n",
            "loss: 0.39\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.03540243  0.0091708 ]\n",
            "b2: [-0.03033706]\n",
            "W3: [0.00409418 0.02971421]\n",
            "b3: [-0.0303584]\n",
            "W4: [0.03718264 0.00294279]\n",
            "b4: [-0.04065932]\n",
            "W5: [-0.01454661 -0.00432588]\n",
            "b5: [-0.03035814]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.01216591 -0.02855176]\n",
            "b7: [-0.03032487]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 4 / 200\n",
            "loss: 0.25\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.04745052  0.01684365]\n",
            "b2: [-0.03065045]\n",
            "W3: [0.0094419  0.03497125]\n",
            "b3: [-0.04033449]\n",
            "W4: [0.0384576  0.00670745]\n",
            "b4: [-0.04077294]\n",
            "W5: [-0.01454661 -0.00432588]\n",
            "b5: [-0.03035814]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.01216591 -0.02855176]\n",
            "b7: [-0.03032487]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 5 / 200\n",
            "loss: 0.15\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.0397226   0.01694881]\n",
            "b2: [-0.04070683]\n",
            "W3: [0.00390432 0.0519732 ]\n",
            "b3: [-0.03043476]\n",
            "W4: [0.0384576  0.00670745]\n",
            "b4: [-0.04077294]\n",
            "W5: [-0.01454661 -0.00432588]\n",
            "b5: [-0.03035814]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [-0.01109072 -0.04513223]\n",
            "b7: [-0.02058509]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 6 / 200\n",
            "loss: 0.25\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.04782568  0.02341758]\n",
            "b2: [-0.04095024]\n",
            "W3: [-0.00094023  0.05036889]\n",
            "b3: [-0.04053699]\n",
            "W4: [0.03990901 0.0124687 ]\n",
            "b4: [-0.04080141]\n",
            "W5: [-0.02285295 -0.01248154]\n",
            "b5: [-0.0302612]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.00789219 -0.03816919]\n",
            "b7: [-0.04081945]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 7 / 200\n",
            "loss: 0.05\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.03778788  0.02284579]\n",
            "b2: [-0.05103028]\n",
            "W3: [-0.01063893  0.05266318]\n",
            "b3: [-0.04055036]\n",
            "W4: [0.03990901 0.0124687 ]\n",
            "b4: [-0.04080141]\n",
            "W5: [-0.02134185 -0.02129143]\n",
            "b5: [-0.03036327]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.00789219 -0.03816919]\n",
            "b7: [-0.04081945]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 8 / 200\n",
            "loss: 0.08\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.03778788  0.02284579]\n",
            "b2: [-0.05103028]\n",
            "W3: [-0.01494194  0.04390702]\n",
            "b3: [-0.05055981]\n",
            "W4: [0.03990901 0.0124687 ]\n",
            "b4: [-0.04080141]\n",
            "W5: [-0.02946043 -0.02043609]\n",
            "b5: [-0.04044762]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.00789219 -0.03816919]\n",
            "b7: [-0.04081945]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "Epoch 9 / 200\n",
            "loss: 0.0\n",
            "W1: [-0.00782353  0.00173441]\n",
            "b1: [-0.02009454]\n",
            "W2: [-0.03778788  0.02284579]\n",
            "b2: [-0.05103028]\n",
            "W3: [-0.01494194  0.04390702]\n",
            "b3: [-0.05055981]\n",
            "W4: [0.03990901 0.0124687 ]\n",
            "b4: [-0.04080141]\n",
            "W5: [-0.02946043 -0.02043609]\n",
            "b5: [-0.04044762]\n",
            "W6: [-0.00270789  0.00673301]\n",
            "b6: [-0.02004848]\n",
            "W7: [ 0.00789219 -0.03816919]\n",
            "b7: [-0.04081945]\n",
            "W8: [-0.00649812 -0.00355493]\n",
            "b8: [-0.02011386]\n",
            "W9: [-0.01351397 -0.00180954]\n",
            "b9: [-0.03031243]\n",
            "W10: [-0.0069874  -0.00241771]\n",
            "b10: [-0.01998597]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       1.00      1.00      1.00       100\n",
            "         1.0       1.00      1.00      1.00       100\n",
            "\n",
            "    accuracy                           1.00       200\n",
            "   macro avg       1.00      1.00      1.00       200\n",
            "weighted avg       1.00      1.00      1.00       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases, error_list = MRI(df0, df1, inputs,target,num_neurons_layer1 = 10 ,num_neurons_layer2 = 2,max_iter = 200,learning_rate = 0.0001,samples = inputs.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IfkmX9v2pt-d",
        "outputId": "7de8a6dc-464d-4312-f1e4-be3c25e73c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample: 200\n",
            "Epoch 1 / 200\n",
            "loss: 0.4975124378109453\n",
            "W1: [ 0.00039507 -0.00025718]\n",
            "b1: [-0.00050009]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [2.49371696e-04 7.42828079e-05]\n",
            "b3: [-0.00040007]\n",
            "W4: [0.0001475  0.00046002]\n",
            "b4: [-0.00050011]\n",
            "W5: [-0.00013064 -0.00041798]\n",
            "b5: [-0.00019998]\n",
            "W6: [0.00038478 0.00064533]\n",
            "b6: [-0.00070022]\n",
            "W7: [-0.0002618   0.00038029]\n",
            "b7: [-0.00030006]\n",
            "W8: [0.0005287  0.00035952]\n",
            "b8: [-0.00060018]\n",
            "W9: [0.00040676 0.00012274]\n",
            "b9: [-0.00050013]\n",
            "W10: [0.00054922 0.00042307]\n",
            "b10: [-0.00070025]\n",
            "Epoch 2 / 200\n",
            "loss: 0.11\n",
            "W1: [ 0.00035963 -0.00033373]\n",
            "b1: [-0.0005001]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [2.49371696e-04 7.42828079e-05]\n",
            "b3: [-0.00040007]\n",
            "W4: [0.0001475  0.00046002]\n",
            "b4: [-0.00050011]\n",
            "W5: [-0.00035888 -0.00021633]\n",
            "b5: [-0.00030002]\n",
            "W6: [0.00034179 0.00055785]\n",
            "b6: [-0.00080022]\n",
            "W7: [-0.00021964  0.00031996]\n",
            "b7: [-0.00040006]\n",
            "W8: [0.0005287  0.00035952]\n",
            "b8: [-0.00060018]\n",
            "W9: [0.00040676 0.00012274]\n",
            "b9: [-0.00050013]\n",
            "W10: [0.00054922 0.00042307]\n",
            "b10: [-0.00070025]\n",
            "Epoch 3 / 200\n",
            "loss: 0.13\n",
            "W1: [ 0.00041915 -0.00041833]\n",
            "b1: [-0.00040009]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [2.49371696e-04 7.42828079e-05]\n",
            "b3: [-0.00040007]\n",
            "W4: [0.0001475  0.00046002]\n",
            "b4: [-0.00050011]\n",
            "W5: [-0.00035978 -0.00030433]\n",
            "b5: [-0.00040006]\n",
            "W6: [0.00034179 0.00055785]\n",
            "b6: [-0.00080022]\n",
            "W7: [-0.00034715  0.00035021]\n",
            "b7: [-0.00030006]\n",
            "W8: [0.0005287  0.00035952]\n",
            "b8: [-0.00060018]\n",
            "W9: [0.00040676 0.00012274]\n",
            "b9: [-0.00050013]\n",
            "W10: [0.00054922 0.00042307]\n",
            "b10: [-0.00070025]\n",
            "Epoch 4 / 200\n",
            "loss: 0.13\n",
            "W1: [ 0.0003387  -0.00037832]\n",
            "b1: [-0.0005001]\n",
            "W2: [0.00034974 0.00049579]\n",
            "b2: [-0.00060016]\n",
            "W3: [2.49371696e-04 7.42828079e-05]\n",
            "b3: [-0.00040007]\n",
            "W4: [0.0001475  0.00046002]\n",
            "b4: [-0.00050011]\n",
            "W5: [-0.00035941 -0.00029839]\n",
            "b5: [-0.00050005]\n",
            "W6: [0.00034179 0.00055785]\n",
            "b6: [-0.00080022]\n",
            "W7: [-0.00043638  0.00027404]\n",
            "b7: [-0.00040006]\n",
            "W8: [0.0005287  0.00035952]\n",
            "b8: [-0.00060018]\n",
            "W9: [0.00040676 0.00012274]\n",
            "b9: [-0.00050013]\n",
            "W10: [0.00054922 0.00042307]\n",
            "b10: [-0.00070025]\n",
            "An early stop occurred!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuUUlEQVR4nO3deXxV9Z3/8dcnIQtL2CGsEpZExV0iWNe4gDit6K/WcWkd7NihWmlxmc7Yacd27HSm06lYFa3SVscuSq1dhjqoIBB3KaC4oAXCJiCyyhKWQMLn98c94PV6k9wk9+Tc3Lyfj8d5cLbvvZ+Ptz2ffM/2NXdHREQkUU7UAYiISGZSgRARkaRUIEREJCkVCBERSUoFQkREklKBEBGRpFQgRDKUmf2Pmf171HFI+6UCIe2Wma0xs31mVh03TWvlGCrNbH/w3VvN7A9m1r8Zn+NmNiKMGKX9UoGQ9u4Sd+8SN01OtpOZdUiyLrcpX9TA/pPdvQtQBnQH7m7K54qERQVCJAkzu87MXjazu81sG/C94JTPT81slpntAc4zs2ODXsAOM1tqZhPiPuNT+zf0ne6+Hfg9cHw9Mf2DmVWZ2XYzm2lmA4L1LwS7vBn0RK5Mx38DERUIkfqNAVYBxcAPgnXXBPNFwALgz8BsoC/wdeA3ZnZ03GfE7/9SQ19mZr2By4E3kmw7H/hP4G+B/sBaYAaAu58T7HZS0Av6bVMTFUlGBULauz8Ff/0fnv4hbtsH7n6fu9e6+75g3f+6+8vufgg4GegC/NDdD7j7POAp4Oq4zziyv7vvryeGe81sB/AmsBG4Nck+XwQedvfX3b0G+BbwGTMraWbeIo361HlVkXbmMnd/rp5t6xpZNwBYFxSLw9YCAxv5jETfcPefN7LPAOD1wwvuXh2c+hoIrEnhO0SaTD0Ikfole9Vx/LoPgMFmFv//o6OADY18RnN8AAw5vGBmnYFeCd8lklYqECLNtwDYC/yTmeWZWQVwCcG1gTR7HPiymZ1sZgXAfwAL3H1NsH0TMCyE75V2TAVC2rs/JzwH8cdUG7r7AWIF4WJgK/AA8Hfu/td0BxmcBvtXYnc5bQSGA1fF7fI94NHgOsrfpvv7pX0yDRgkIiLJqAchIiJJqUCIiEhSKhAiIpKUCoSIiCSVNQ/K9e7d20tKSprdfs+ePXTu3Dl9AUUkW/IA5ZKpsiWXbMkDWpbL4sWLt7p7n2TbsqZAlJSUsGjRoma3r6yspKKiIn0BRSRb8gDlkqmyJZdsyQNalouZra1vm04xiYhIUioQIiKSlAqEiIgkpQIhIiJJhVogzGy8mS0LRsG6Pcn268xsi5ktCaavxG2baGYrgmlimHGKiMinhXYXUzD+7v3AWGA9sNDMZrr7uwm7/jZxHGAz6wl8Fygn9rrkxUHbj8KKV0REPinMHsRooMrdVwVvvZwBXJpi24uAOe6+PSgKc4DxIcUpIiJJhPkcxEA+OZrWemJj/Ca63MzOAZYDt7j7unraDkxsaGaTgEkAxcXFVFZWNjnIvQedp1cf5JQeB6AZ7TNNdXV1s/47ZCLlkpmyJZdsyQPCyyXqB+X+DDzu7jVm9lXgUeD8VBu7+3RgOkB5ebk350GRLbtruO2F+Wzd14HHr2h6+0yjh38yk3LJPNmSB4SXS5inmDYAg+OWB5EwPKK7bwsGYAf4OTAq1bbp0qeogIlnlPDaxjpWbNodxleIiLRJYRaIhUCpmQ01s3xio1/NjN/BzPrHLU4A3gvmnwXGmVkPM+sBjAvWheKr5wyjIBfufm55WF8hItLmhHaKyd1rzWwysQN7LvCwuy81szuBRe4+E/iGmU0AaoHtwHVB2+1m9n1iRQbgTnffHlasPTrnc1FJHv/79ocs/WAnxw3oFtZXiYi0GaFeg3D3WcCshHV3xM1/C/hWPW0fBh4OM75440rymL/BuXvOcn4+8bTW+loRkYylJ6kDnfOMr547nOfe28ySdTuiDkdEJHIqEHGuO6OEnp3zmTpH1yJERFQg4nQu6MAN5w7jheVbWLgmtEseIiJtggpEgmtPL6FPUQE/fnYZ7h51OCIikVGBSNAxP5ebKoazYPV2Xlm5LepwREQiowKRxNVjjmJAt0Lumq1ehIi0XyoQSRR0yGXy+aW8/v4OKpdviTocEZFIqEDU44ryQQzu2ZGps5erFyEi7ZIKRD3ycnOYckEZb2/Yyex3N0UdjohIq1OBaMBlJw9gWO/OTJ29nEOH1IsQkfZFBaIBHXJzuHlsGcs27eb/3t4YdTgiIq1KBaIRnzuhP0cXF3H3c8uprTsUdTgiIq1GBaIROTnGLWNLWbVlD/+75IOowxERaTUqECm46Lh+HDegK/fMXcFB9SJEpJ1QgUiBmXHbuDLe376XJxevjzocEZFWoQKRovOO7svJg7tz39wV1NTWRR2OiEjoQi0QZjbezJaZWZWZ3d7AfpebmZtZebBcYmb7zGxJMD0YZpypMDP+cdzRfLBzP79duC7qcEREQhdagTCzXOB+4GJgJHC1mY1Msl8RMAVYkLBppbufHEw3hBVnU5w5ohejh/Zk2rwq9h9UL0JEsluYPYjRQJW7r3L3A8AM4NIk+30f+C9gf4ixpIWZcdvYMjbvruHXr62NOhwRkVCFOSb1QCD+XMx6YEz8DmZ2KjDY3f/PzL6Z0H6omb0B7AK+4+4vJn6BmU0CJgEUFxdTWVnZ7GCrq6tTbn9crxzumfMeg2rWUtjBmv2dYWhKHplOuWSmbMklW/KAEHNx91Am4AvAz+OWrwWmxS3nAJVASbBcCZQH8wVAr2B+FLFC07Wh7xs1apS3xPz581Pe9/W1233IPz/l0+ataNF3hqEpeWQ65ZKZsiWXbMnDvWW5AIu8nuNqmKeYNgCD45YHBesOKwKOByrNbA1wOjDTzMrdvcbdtwG4+2JgJVAWYqxNcspRPbjgmL5Mf2EVu/YfjDocEZFQhFkgFgKlZjbUzPKBq4CZhze6+0537+3uJe5eArwGTHD3RWbWJ7jIjZkNA0qBVSHG2mS3jC1j576D/OLF1VGHIiISitAKhLvXApOBZ4H3gCfcfamZ3WlmExppfg7wlpktAZ4EbnD37WHF2hzHD+zGxcf34+GXVrNj74GowxERSbswL1Lj7rOAWQnr7qhn34q4+d8Dvw8ztnS4ZWwZzyz9kOkvrOKfxh8TdTgiImmlJ6lboKy4iEtOHMAjL69ha3VN1OGIiKSVCkQL3XxhKTW1dTxYuTLqUERE0koFooWG9enC508dxK9eW8umXRn/rJ+ISMpUINJgygWl1B1y7p9fFXUoIiJpowKRBoN7duJvTxvM4395n/Uf7Y06HBGRtFCBSJPJ543AMKbNUy9CRLKDCkSaDOjekWvGHMXvFq9nzdY9UYcjItJiKhBp9LXzhpOXa9w7d0XUoYiItJgKRBr1LSpk4mdK+NOSDVRtro46HBGRFlGBSLOvnjucjnm5/OS55VGHIiLSIioQadazcz5fPnMoT721kfc27oo6HBGRZlOBCME/nD2MosIO3D1HvQgRabtUIELQrVMe/3D2MGa/u4m31u+IOhwRkWZRgQjJl88soXunPKaqFyEibZQKREiKCvO44dzhVC7bwuK1GTWUhYhISlQgQvR3nxlC7y753DVbvQgRaXtUIELUKb8DX6sYwSsrt/Hqym1RhyMi0iShFggzG29my8ysysxub2C/y83Mzaw8bt23gnbLzOyiMOMM0zVjjqJf10KmzlmGu0cdjohIykIrEGaWC9wPXAyMBK42s5FJ9isCpgAL4taNBK4CjgPGAw8En9fmFOblctP5I1i45iNeWLE16nBERFIWZg9iNFDl7qvc/QAwA7g0yX7fB/4LiB9t51JghrvXuPtqoCr4vDbpyvLBDOzekamz1YsQkbajQ4ifPRBYF7e8HhgTv4OZnQoMdvf/M7NvJrR9LaHtwMQvMLNJwCSA4uJiKisrmx1sdXV1i9o35qJBdTz8zk5+8ru5nNI3vP/sYefRmpRLZsqWXLIlDwgvlwaPVGaWA5zu7q+k+4uDz54KXNfcz3D36cB0gPLycq+oqGh2PJWVlbSkfWPOqjvEvKnPM3tjB6Z84SxyciyU7wk7j9akXDJTtuSSLXlAeLk0eIrJ3Q8Ru47QHBuAwXHLg4J1hxUBxwOVZrYGOB2YGVyobqxtm9MhN4ebLyzjvY27eGbph1GHIyLSqFSuQcwN7jJq6p+8C4FSMxtqZvnELjrPPLzR3Xe6e293L3H3EmKnlCa4+6Jgv6vMrMDMhgKlwF+a+P0Z55KTBlDatwtT5yyn7pCuRYhIZkulQHwV+B1wwMx2mdluM2v0NaXuXgtMBp4F3gOecPelZnanmU1opO1S4AngXeAZ4CZ3r0sh1oyWm2PcMraMqs3V/PnND6IOR0SkQY1eLXX3ouZ+uLvPAmYlrLujnn0rEpZ/APygud+dqcYf149j+3flJ88t53Mn9qdDrp5VFJHMlNLRycwmmNmPg+lzYQeVzXJyjNvGlrFm217+8HqbvqwiIlmu0QJhZj8k9iDbu8E0xcz+M+zAstkFx/blpMHduWfuCg7UHoo6HBGRpFLpQfwNMNbdH3b3h4k92fzZcMPKbmbGrWPL2LBjH79dtK7xBiIiEUj1BHj3uPluIcTR7pxT2pvTSnowbd4K9h9s89ffRSQLpVIg/gN4w8z+x8weBRaThRePW1usF3E0m3bV8JsF70cdjojIpzRYIIKnnQ8Re4jtD8Dvgc+4+29bIbas95nhvThjeC9+WlnF3gO1UYcjIvIJqTxJ/U/uvtHdZwaTHgNOo9vGlbG1+gC/fHVt1KGIiHxCKqeYnjOzfzSzwWbW8/AUemTtxKghPak4ug8PPr+S3fsPRh2OiMgRqRSIK4GbgBeIXX9YDCwKM6j25raxR7Nj70EeeXlN1KGIiByRyjWI2919aMI0rJXiaxdOGNSNcSOL+dmLq9i5V70IEckMqVyD+GZD+0h63DK2jN37a/nZi6uiDkVEBNA1iIxxbP+ufO7E/jzy8mq2VddEHY6IiK5BZJKbLyxj38E6HnpBvQgRiV6jBSLJ9QddgwjJiL5duOyUgfzy1TVs3r2/8QYiIiGqt0CY2T/FzV+RsO0/wgyqPZtyQSkH65wH5q+MOhQRaeca6kFcFTf/rYRt40OIRYAhvTpzxahBPLbgfT7YsS/qcESkHWuoQFg988mWk3+A2XgzW2ZmVWZ2e5LtN5jZ22a2xMxeMrORwfoSM9sXrF9iZg+m8n3Z4usXlAIwbX5VxJGISHvWUIHweuaTLX+KmeUC9wMXAyOBqw8XgDiPufsJ7n4y8CNgaty2le5+cjDd0Nj3ZZOB3Tty1ejBPLFwHe9v2xt1OCLSTjVUIE46PAY1cGIwf3j5hBQ+ezRQ5e6r3P0AMAO4NH4Hd48f27ozKRSe9uKm80aQm2PcO29F1KGISDtl7uEck83sC8B4d/9KsHwtMMbdJyfsdxNwK5APnO/uK8ysBFgKLAd2Ad9x9xeTfMckYBJAcXHxqBkzZjQ73urqarp06dLs9mF4/K81zF5Ty3+e3ZF+nVMbuiMT82gu5ZKZsiWXbMkDWpbLeeedt9jdy5NudPdQJuALwM/jlq8FpjWw/zXAo8F8AdArmB8FrAO6NvR9o0aN8paYP39+i9qHYcvu/X7svz7tX3/s9ZTbZGIezaVcMlO25JItebi3LBdgkddzXE11RLnm2AAMjlseFKyrzwzgMgB3r3H3bcH8YmAlUBZOmJmrd5cCrjujhD+/9QHLPtwddTgi0s6EWSAWAqVmNtTM8ondNjszfgczK41b/CywIljfJ7jIjZkNA0qBdvl48aRzhtElvwN3z1kedSgi0s6EViDcvRaYDDwLvAc84e5LzexOM5sQ7DbZzJaa2RJi1yEmBuvPAd4K1j8J3ODu28OKNZN175TP9WcP5ZmlH/LOhp1RhyMi7UiH+jYEdyvVewXb3bs29uHuPguYlbDujrj5KfW0+z2x4U0F+PuzhvLIy2u4e85yfnHdaVGHIyLtRL09CHcvCorAPcDtwEBi1xH+GfhJq0QnAHQtzGPSOcOY+9fNvP7+R1GHIyLtRCqnmCa4+wPuvtvdd7n7T0l4nkHCd90ZJfTqnK9rESLSalIpEHvM7ItmlmtmOWb2RWBP2IHJJ3Uu6MCNFcN5ccVWFqzaFnU4ItIOpFIgrgH+FtgUTFcE66SVfen0IfQtKuCuOcsPPzsiIhKaVMaDWOPul7p7b3fv4+6XufuaVohNEhTm5TL5/BH8ZfV2Xq5SL0JEwtVogTCzMjOba2bvBMsnmtl3wg9NkrnytMEM6FbIj2cvUy9CREKVyimmnxEbD+IggLu/xSfHipBWVNAhl29cUMqSdTuYv2xz1OGISBZLpUB0cve/JKyrDSMYSc3lowZxVM9O3DVb1yJEJDypFIitZjac4KG54C2tG0ONShqUl5vDlAtKWfrBLp5d+mHU4YhIlkqlQNwEPAQcY2YbgJuBdjWATya67JSBDO/TmalzllN3SL0IEUm/BgtE8MK8r7n7hUAf4Bh3P8vd17ZKdFKv3Bzj5gvLWL6pmqfe+iDqcEQkCzVYINy9DjgrmN/j7nrndAb57An9OaZfEfc8t4LaukNRhyMiWSaVU0xvmNlMM7vWzD5/eAo9MmlUTo5xy9gyVm3dw5+WqBchIulV79tc4xQC24Dz49Y58IdQIpImGTeymBMGduOeucu59OQBUYcjIlmk0QLh7l9ujUCkecyMW8eV8eVHFvK7RetRiRCRdGm0QJhZIXA9cByx3gQA7v73IcYlTVBR1odTj+rOffNW8G+jwxwkUETak1SOJr8C+gEXAc8TGxNCF6sziJnxj+OOZuPO/Ty/Xs8wikh6pFIgRrj7vwJ73P1RYmNHj0nlw81svJktM7MqM7s9yfYbzOxtM1tiZi+Z2ci4bd8K2i0zs4tSTai9OmNEb04f1pOnVh1k34G6qMMRkSyQSoE4GPy7w8yOB7oBfRtrFDxDcT9wMTASuDq+AAQec/cT3P1k4EfA1KDtSGLvezoOGA88EHyeNOC2cUezs8b51Wtrog5FRLJAKgViupn1AP4VmAm8S+xg3pjRQJW7r3L3A8AMEkaic/ddcYud+XgM7EuBGe5e4+6rgarg86QBp5X05PjeuTz4/Cqqa3SqSURaxsJ62Vvwzqbx7v6VYPlaYIy7T07Y7ybgViAfON/dV5jZNOA1d/91sM8vgKfd/cmEtpOASQDFxcWjZsyY0ex4q6ur6dKlS7PbZ4p3Nlbz4zeNz5fmMWF4ftThtEi2/CagXDJRtuQBLcvlvPPOW+zu5cm2pXIX0x3J1rv7nc2K5tOfcz9wv5ldA3wHmNiEttOB6QDl5eVeUVHR7DgqKytpSfuMUVnJhQe68NzqbXz3mjPp1jEv6oiaLWt+E5RLJsqWPCC8XFIakzpuqiN2TaEkhXYbgMFxy4OCdfWZAVzWzLYS59axZezaX8svXloddSgi0oalMuToXXHTD4AKYFgKn70QKDWzoWaWT+yi88z4HcysNG7xs8CKYH4mcJWZFZjZUKAUSByTQuoxckBX/uaEfjz80mo+2nMg6nBEpI1qzlNVnYj9Rd8gd68FJgPPAu8BT7j7UjO708wmBLtNNrOlZraE2HWIiUHbpcATxC6IPwPcFLw4UFJ0y4Vl7DlQy0MvrIo6FBFpo1K5BvE2H99dlEvstd8pXX9w91nArIR1d8TNT2mg7Q+AH6TyPfJppcVFXHrSAB59ZQ3XnzWUPkUFUYckIm1MKj2IzwGXBNM4YIC7Tws1KkmLKReWcaDuED+tXBl1KCLSBqVSIHbHTfuArmbW8/AUanTSIkN7d+byUwfy6wVr+XDn/qjDEZE2JpUC8TqwBVhO7CLyFmBxMC0KLzRJh6+fX4q7M23+isZ3FhGJk0qBmANc4u693b0XsVNOs919qLuncjeTRGhwz05cedpgfrtwHes/2ht1OCLShqRSIE4PLjYD4O5PA2eEF5Kk2+TzSjEz7ptbFXUoItKGpFIgPjCz75hZSTB9G9D4lm1Iv26FfHHMUTz5+nrWbN0TdTgi0kakUiCuJnZr6x+DqW+wTtqQGyuGk5+bwz1zdS1CRFKTypPU2919irufQmxc6pvdfXv4oUk69S0q5O/OGMKflmxgxSaN9yQijau3QJjZHWZ2TDBfYGbziL12e5OZXdhaAUr63HDOcDrnd+Anz6kXISKNa6gHcSWwLJifGOzbFzgX+I+Q45IQ9Oicz9+fWcL/vb2Rdz/Y1XgDEWnXGioQB/zjwSIuAh539zp3f48UXtEhmen6s4fRtbADU+csjzoUEclwDRWIGjM73sz6AOcBs+O2dQo3LAlLt455TDpnGM+9t4k31+2IOhwRyWANFYgpwJPAX4G7g6E/MbO/Ad5ohdgkJNedOZQenfK4S70IEWlAvQXC3Re4+zHu3svdvx+3fpa76zbXNqxLQQdurBjOC8u3sGiNbkgTkeSaMx6EZIFrTy+hT1EBd81WL0JEklOBaKc65ufytYrhvLpqG69UbY06HBHJQKEWCDMbb2bLzKzKzG5Psv1WM3vXzN4ys7lmNiRuW52ZLQmmmYltpeWuHn0U/bsVctec5Xx8w5qISExKt6ua2RlASfz+7v7LRtrkAvcDY4H1wEIzm+nu78bt9gZQ7u57zexG4EfEnr8A2OfuJ6eYhzRDYV4uk88fwbf/+A6Vy7dw3tF9ow5JRDJIoz0IM/sV8GPgLOC0YCpP4bNHA1XuvsrdDwAzgEvjd3D3+e5++B3Ur5HCWNeSXleMGsygHh25W70IEUmQSg+iHBjpTT96DATWxS2vB8Y0sP/1wNNxy4VmtgioBX7o7n9q4vdLCvI75DDlglK++eRbzHl3E+OO6xd1SCKSIVIpEO8A/YCNYQVhZl8iVojOjVs9xN03mNkwYJ6Zve3uKxPaTQImARQXF1NZWdnsGKqrq1vUPlM0J4+eh5x+nYw7//g6HTZ3JMcsnOCaKFt+E1AumShb8oDwckmlQPQG3jWzvwA1h1e6+4RG2m0ABsctDwrWfULw4r9vA+e6e/znbwj+XWVmlcApwCcKhLtPB6YDlJeXe0VFRQrpJFdZWUlL2meK5uZR3XMDU2YsYW+vo/nciQPSH1gzZMtvAsolE2VLHhBeLqkUiO8187MXAqVmNpRYYbgKuCZ+BzM7BXgIGO/um+PW9wD2unuNmfUGziR2AVtCcsmJA7h/fhV3z1nOxcf3JzcnM3oRIhKdRguEuz/fnA9291ozmww8C+QCD7v7UjO7E1jk7jOB/wa6AL+z2GmN94OeybHAQ2Z2iNiF9B8m3P0kaZaTY9xyYRk3/uZ1/nfJBj5/qu4XEGnvGi0QZnY6cB+xg3Y+sYP9Hnfv2ljbYCzrWQnr7oibTzquhLu/ApzQ2OdLel10XD+OG9CVe+au4JKTBpCXq+coRdqzVI4A04gNMboC6Ah8hdjzDZJlcnKMW8eWsXbbXn6/eH3U4YhIxFL6E9Hdq4DcYDyIR4Dx4YYlUTn/mL6cPLg7982roqa2LupwRCRCqRSIvWaWDywxsx+Z2S0ptpM2yMy4bVwZG3bs44mF6xpvICJZK5UD/bXBfpOBPcRuXb08zKAkWmeN6M3okp7cN6+K/QfVixBprxotEO6+FjCgv7v/m7vfGpxykix1uBexeXcNv35tbdThiEhEUnkX0yXAEuCZYPlkvV01+40Z1ouzRvTmp5Ur2VNTG3U4IhKBVE4xfY/Yi/d2ALj7EmBoaBFJxrh1XBnb9hzg0VfXRB2KiEQglQJx0N13JqzTaz/bgVOP6sH5x/TloedXsWv/wajDEZFWlkqBWGpm1wC5ZlZqZvcBr4Qcl2SIW8eWsXPfQR5+aXXUoYhIK0ulQHwdOI7Yi/oeB3YBN4cYk2SQ4wd2Y/xx/fjFi6vZsfdA1OGISCtK5S6mve7+bXc/zd3Lg/n9rRGcZIZbxpZRfaCW6S+sijoUEWlF9b6LqbE7lVJ43bdkiaP7FXHJiQP4n1fWcP1ZQ+nVpSDqkESkFTT0sr7PEBsR7nFgAbFnIaSdmnJhKU+99QEPPr+Sb392ZNThiEgraOgUUz/gX4DjgXuAscBWd3++ua8Al7ZreJ8u/L9TBvHLV9eyaZfOMIq0B/UWiODFfM+4+0TgdKAKqAzGeJB2aMoFpdQdch6YrwfpRdqDBi9Sm1mBmX0e+DVwE3Av8MfWCEwyz1G9OnFF+WAe/8s6NuzYF3U4IhKyeguEmf0SeBU4Ffi34C6m7x8eK1rap6+fPwKAafNWRByJiIStoR7El4BSYArwipntCqbdZrYrlQ83s/FmtszMqszs9iTbbzWzd83sLTOba2ZD4rZNNLMVwTSxqYlJOAZ078g1Y47id4vWs3bbnqjDEZEQNXQNIsfdi4Kpa9xUlMpwo2aWS2zkuYuBkcDVZpZ4+8sbQLm7nwg8CfwoaNsT+C4whth7oL5rZj2ak6Ck39cqhpObY9wzV70IkWwW5sA/o4Eqd1/l7geAGcCl8Tu4+3x33xssvgYMCuYvAua4+3Z3/wiYg0axyxh9uxYy8YwS/vTGBqo2V0cdjoiEpKHnIFpqILHnKA5bT6xHUJ/rgacbaDswsYGZTQImARQXF1NZWdnsYKurq1vUPlO0Vh7Hd3DycuBfHnuJr51cGMp3ZMtvAsolE2VLHhBeLmEWiJSZ2ZeAcuDcprRz9+nAdIDy8nKvqKhodgyVlZW0pH2maM08lvlfuX/+Su485lSO6dfoWccmy5bfBJRLJsqWPCC8XMI8xbSB2PCkhw0K1n2CmV0IfBuY4O41TWkr0Zp09nCKCjtw95zlUYciIiEIs0AsBErNbKiZ5QNXAZ94v5OZnQI8RKw4bI7b9Cwwzsx6BBenxwXrJIN065THV84axrNLN/H2+sQhQ0SkrQutQLh7LTCZ2IH9PeAJd19qZnea2eEX/f030AX4nZktOfyCQHffDnyfWJFZCNwZrJMM8/dnldC9Ux5T5yyLOhQRSbNQr0G4+yxgVsK6O+LmL2yg7cPAw+FFJ+lQVJjHV88Zzn8981cWr/2IUUN0N7JItgjzFJO0ExPPGELvLvnqRYhkGRUIabFO+R24sWIEL1dt49WV26IOR0TSRAVC0uKLY46iuGsBU+csw92jDkdE0kAFQtKiMC+XyeeXsnDNR7y4YmvU4YhIGqhASNpcWT6Ygd07ctec5epFiGQBFQhJm/wOOXzjghG8uW4Hc9/b3HgDEcloKhCSVp8/dRAlvToxdc5yDh1SL0KkLVOBkLTKy81hyoWlvLtxF88s/TDqcESkBVQgJO0mnDSQEX27cPec5dSpFyHSZqlASNrl5hi3XFjGis3VPPXWB1GHIyLNpAIhobj4+H4c06+Inzy3gtq6Q1GHIyLNoAIhocjJMW4bdzSrt+7hD2/oTe0ibZEKhITmwmP7ctKgbtzz3AoO1KoXIdLWqEBIaMyMW8aWsWHHPp5YtK7xBiKSUVQgJFTnlvWhfEgPps2rYv/BuqjDEZEmUIGQUJkZt44r48Nd+3lswftRhyMiTRBqgTCz8Wa2zMyqzOz2JNvPMbPXzazWzL6QsK0uGGXuyEhz0jadMbw3ZwzvxQOVK9l7oDbqcEQkRaEVCDPLBe4HLgZGAleb2ciE3d4HrgMeS/IR+9z95GCakGS7tCG3jStja3UNv3x1bdShiEiKwuxBjAaq3H2Vux8AZgCXxu/g7mvc/S1At7hkuVFDenJuWR8een4l1TXqRYi0BWEWiIFA/K0r64N1qSo0s0Vm9pqZXZbWyCQSt40r46O9B3nkpdVRhyIiKegQdQANGOLuG8xsGDDPzN5295XxO5jZJGASQHFxMZWVlc3+surq6ha1zxSZnscpfXN5YP5yhh1aT+c8a3DfTM+lKZRL5smWPCC8XMIsEBuAwXHLg4J1KXH3DcG/q8ysEjgFWJmwz3RgOkB5eblXVFQ0O9jKykpa0j5TZHoexUfv4uJ7XuSvPoDbKo5ucN9Mz6UplEvmyZY8ILxcwjzFtBAoNbOhZpYPXAWkdDeSmfUws4JgvjdwJvBuaJFKqzm2f1c+e2J/Hn5pNdv3HIg6HBFpQGgFwt1rgcnAs8B7wBPuvtTM7jSzCQBmdpqZrQeuAB4ys6VB82OBRWb2JjAf+KG7q0BkiVsuLGXfwToeen5l4zuLSGRCvQbh7rOAWQnr7oibX0js1FNiu1eAE8KMTaIzom8Rl508kEdfXcP1Zw+lb1Fh1CGJSBJ6kloi8Y0LSjlY5zwwX70IkUylAiGRKOndmStGDeKxBe+zcee+qMMRkSRUICQyk88fgeNMm1cVdSgikkQmPwchWW5Qj05cddpRPP6X97nh3OEM7tkp6pCkEQdqD3GgzrPizbzZkgfEcgmDCoREavL5I3hi0TrunbuC/77ipKjDabf2H6xjy+4aNu3az+bg3027atj8ieX97NofvCZlzjPRBpwuWZLHsG45jLsg/Z+rAiGRKu5ayJdOH8IjL6/mxorhDOvTJeqQskpNbR2bd9WweXfsYL9p13427a4J1u0/Ugh27jv4qbZ5uUbfokL6di1geJ8unDG8F727FPD+2tUMGzY8gmzSa9WqlVmRB8D2DatC+VwVCIncjRXDeWzB+9wzdwX3XHVK1OG0CQdqD7GlOviLP+Gv/k279h/pDXy099MH/g45Rt+iAvp2LWRo786MGdqL4q6x5eKuhfQtKqC4ayHdO+aRk/Pp16FUVm6goqLtH1grWZcVeQBUVoYzYqMKhESud5cCrjuzhAefX8lN542grLgo6pAic7Du0CdO9WyOO+hvjluf7Cn03MMH/qICBvfsRHlJD4qDHkDfroUUFxVS3LWAHp3ykx74RRKpQEhGmHT2MH716lrunrOcn35pVNThpF3tIWfjzn1sDg72m3bXsOXwwX937N8tu/ezbc8BPOF6Y45Bn+Cv+kE9OnHqkB5HDvbFXQuPbOvZOZ9cHfgljVQgJCP06JzP9WcN5Z65K3hnw06OH9gt6pBSUlt3iG17Dnx8UXf3xxd3P/6rv4Zt1TX47HmfaJtjsd5T364FDOhWyMmDu8dO9cQd/Pt2LaBX5wId+CUSKhCSMa4/eyj/88oa7p6znF9cd1qksdQdcrZV13zioB874O+P9QKCdduqaziU8Be/GfTqXHDkIH/CwG7s2/4ho088+sgpn+KuhfTqnE+HXD2KJJlLBUIyRtfCPCadM4z/fnYZb7z/USjfceiQH/mL/+O/9mMH/M1xvYAtuz994Afo3SX/yJ09x/XvRnHXAvp0LaQ4OM1T3LWQXl3yyUs48FdWbqdizJBQchIJiwqEZJTrzijhFy+tZuqc5VzfhBtMDh1ytu8NDvwJf/UfPr+/aVcNW6prqEty5O/ZOf/I3TvH9i86cpon/s6e3l0KyO+gv/il/VCBkIzSuaADN547nB/Meo+zehRyziHno70HPr6DJ+GOnk3BnT5bdtdQm+TA36NT3pELuaXFRR+f2y/6+ODfRwd+kaRUICTjfOn0IfzsxVXctXg/P178NAeTvEagW8e8Iwf7EX16x87rFx2+sBsrAH2KCijMy40gA5HsoAIhGadjfi7/dfmJ/Hz2G5xQOuRTd/bowC/SOlQgJCOdd0xf7MMCKiqOiToUkXYr1BOvZjbezJaZWZWZ3Z5k+zlm9rqZ1ZrZFxK2TTSzFcE0Mcw4RUTk00IrEGaWC9wPXAyMBK42s5EJu70PXAc8ltC2J/BdYAwwGviumfUIK1YREfm0MHsQo4Eqd1/l7geAGcCl8Tu4+xp3fws4lND2ImCOu29394+AOcD4EGMVEZEEYV6DGAjEv2JwPbEeQXPbDkzcycwmAZMAiouLqaysbFagANXV1S1qnymyJQ9QLpkqW3LJljwgvFza9EVqd58OTAcoLy/3ioqKZn9WZWUlLWmfKbIlD1AumSpbcsmWPCC8XMI8xbQBGBy3PChYF3ZbERFJgzALxEKg1MyGmlk+cBUwM8W2zwLjzKxHcHF6XLBORERaSWgFwt1rgcnEDuzvAU+4+1Izu9PMJgCY2Wlmth64AnjIzJYGbbcD3ydWZBYCdwbrRESklZgnjk7SRpnZFmBtCz6iN7A1TeFEKVvyAOWSqbIll2zJA1qWyxB375NsQ9YUiJYys0XuXh51HC2VLXmAcslU2ZJLtuQB4eWiV1iKiEhSKhAiIpKUCsTHpkcdQJpkSx6gXDJVtuSSLXlASLnoGoSIiCSlHoSIiCSlAiEiIkm1qwKRwvgUBWb222D7AjMriSDMlKSQy3VmtsXMlgTTV6KIszFm9rCZbTazd+rZbmZ2b5DnW2Z2amvHmKoUcqkws51xv8kdrR1jKsxssJnNN7N3zWypmU1Jsk+b+F1SzKWt/C6FZvYXM3szyOXfkuyT3mOYu7eLCcgFVgLDgHzgTWBkwj5fAx4M5q8Cfht13C3I5TpgWtSxppDLOcCpwDv1bP8b4GnAgNOBBVHH3IJcKoCnoo4zhTz6A6cG80XA8iT/+2oTv0uKubSV38WALsF8HrAAOD1hn7Qew9pTD6LR8SmC5UeD+SeBC8zMWjHGVKWSS5vg7i8ADb1G5VLglx7zGtDdzPq3TnRNk0IubYK7b3T314P53cRelZP4uv028bukmEubEPy3rg4W84Ip8S6jtB7D2lOBSGWMiSP7eOxdUjuBXq0SXdOkNF4GcHnQ/X/SzAYn2d4WpJprW/GZ4BTB02Z2XNTBNCY4RXEKsb9W47W536WBXKCN/C5mlmtmS4DNxAZVq/d3SccxrD0ViPbmz0CJu59IbES+RxvZX8L3OrH33pwE3Af8KdpwGmZmXYDfAze7+66o42mJRnJpM7+Lu9e5+8nEhkAYbWbHh/l97alApDLGxJF9zKwD0A3Y1irRNU2jubj7NnevCRZ/DoxqpdjSLWvGBnH3XYdPEbj7LCDPzHpHHFZSZpZH7ID6G3f/Q5Jd2szv0lgubel3OczddwDz+fRQzGk9hrWnApHK+BQzgYnB/BeAeR5c7ckwjeaScD54ArFzr23RTODvgrtmTgd2uvvGqINqDjPrd/h8sJmNJvb/v4z7AySI8RfAe+4+tZ7d2sTvkkoubeh36WNm3YP5jsBY4K8Ju6X1GNamhxxtCnevNbPD41PkAg97MD4FsMjdZxL7H9KvzKyK2MXGq6KLuH4p5vINi427UUssl+siC7gBZvY4sbtIeltsbJDvErv4hrs/CMwidsdMFbAX+HI0kTYuhVy+ANxoZrXAPuCqDP0D5EzgWuDt4Hw3wL8AR0Gb+11SyaWt/C79gUfNLJdYEXvC3Z8K8ximV22IiEhS7ekUk4iINIEKhIiIJKUCISIiSalAiIhIUioQIiKSlAqESCPMrC7uTZ9LLMnbc1vw2SX1vf1VJGrt5jkIkRbYF7zeQKRdUQ9CpJnMbI2Z/cjM3g7e0z8iWF9iZvOCFyXONbOjgvXFZvbH4KVwb5rZGcFH5ZrZz4J3/M8OnpLFzL5hsXEM3jKzGRGlKe2YCoRI4zomnGK6Mm7bTnc/AZgG/CRYdx/waPCixN8A9wbr7wWeD14KdyqwNFhfCtzv7scBO4DLg/W3A6cEn3NDOKmJ1E9PUos0wsyq3b1LkvVrgPPdfVXwQrgP3b2XmW0F+rv7wWD9RnfvbWZbgEFxL1E8/ArqOe5eGiz/M5Dn7v9uZs8A1cTeLvqnuLEARFqFehAiLeP1zDdFTdx8HR9fG/wscD+x3sbC4O2cIq1GBUKkZa6M+/fVYP4VPn5J2heBF4P5ucCNcGTgl271faiZ5QCD3X0+8M/EXtv8qV6MSJj0F4lI4zrGvQkU4Bl3P3yraw8ze4tYL+DqYN3XgUfM7JvAFj5+0+kUYLqZXU+sp3AjUN8rsnOBXwdFxIB7gzEARFqNrkGINFNwDaLc3bdGHYtIGHSKSUREklIPQkREklIPQkREklKBEBGRpFQgREQkKRUIERFJSgVCRESS+v+0ab41X5GuUQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       0.98      0.91      0.94       100\n",
            "         1.0       0.92      0.98      0.95       100\n",
            "\n",
            "    accuracy                           0.94       200\n",
            "   macro avg       0.95      0.95      0.94       200\n",
            "weighted avg       0.95      0.94      0.94       200\n",
            "\n"
          ]
        }
      ]
    }
  ]
}