minProject1_Khosravi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMOohwHWLS4o1QjBCAwdgF6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohmmdkhosravi/git-learning/blob/main/minProject1_Khosravi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1.2\n",
        "from sklearn.datasets import make_classification\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# توليد داده‌ها\n",
        "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1)\n",
        "\n",
        "# نمايش داده‌ها\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis')\n",
        "plt.title(\"Generated Data\")\n",
        "plt.xlabel(\"Feature 1\")\n",
        "plt.ylabel(\"Feature 2\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "wALh6ubQ9ZPM",
        "outputId": "1d3c68fc-714a-4c4f-d1fa-81d9eaf7419f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1.3\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# تقسيم داده‌ها به آموزش و تست\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# تعريف مدل\n",
        "model = LogisticRegression()\n",
        "\n",
        "# تعريف فراپارامترها\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10, 100],\n",
        "    'solver': ['liblinear']\n",
        "}\n",
        "\n",
        "# جستجوی فراپارامترها با استفاده از GridSearchCV\n",
        "grid_search = GridSearchCV(model, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# بهترين مدل\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# پيش‌بینی با استفاده از مدل بهترين\n",
        "y_pred = best_model.predict(X_test)\n",
        "\n",
        "# محاسبه دقت\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1PezBHm9wuA",
        "outputId": "00ea7e5d-e55a-4fb6-b41e-afd16b113e91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1.4\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# تعریف تابع برای رسم مرز تصمیم‌گیری\n",
        "def plot_decision_boundary(model, X, y):\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "                         np.arange(y_min, y_max, 0.1))\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    plt.contourf(xx, yy, Z, alpha=0.4, cmap='viridis')\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k', cmap='viridis')\n",
        "    plt.title(\"Decision Boundary\")\n",
        "    plt.show()\n",
        "\n",
        "# رسم مرز تصمیم‌گیری\n",
        "plot_decision_boundary(best_model, X_test, y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "b1g8oU_o96UT",
        "outputId": "17c6e663-3978-45c7-e22b-a5fe85176bb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1.5\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "def extract_features(data_matrix):\n",
        "    features = []\n",
        "    for sample in data_matrix:\n",
        "        feature_vector = [\n",
        "            np.mean(sample),                   # Mean\n",
        "            np.std(sample),                    # Standard Deviation\n",
        "            np.max(sample),                    # Maximum\n",
        "            np.min(sample),                    # Minimum\n",
        "            np.median(sample),                 # Median\n",
        "            np.percentile(sample, 25),         # 25th Percentile\n",
        "            np.percentile(sample, 75),         # 75th Percentile\n",
        "            np.var(sample)                     # Variance\n",
        "        ]\n",
        "        features.append(feature_vector)\n",
        "    return pd.DataFrame(features, columns=['Mean', 'Std', 'Max', 'Min', 'Median', '25th Percentile', '75th Percentile', 'Variance'])\n",
        "\n",
        "# Assuming matrix_normal and matrix_fault have been created already\n",
        "\n",
        "# Extract features for both normal and faulty data\n",
        "features_normal = extract_features(matrix_normal)\n",
        "features_fault = extract_features(matrix_fault)\n",
        "\n",
        "# Display the feature dataframes\n",
        "print(\"Features for Normal Data:\")\n",
        "print(features_normal.head())\n",
        "\n",
        "print(\"\\nFeatures for Fault Data:\")\n",
        "print(features_fault.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "VDvnDFuO-Hwb",
        "outputId": "30edfce9-2ad3-44fa-f422-661c20a01c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'matrix_normal' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-494290946e2f>\u001b[0m in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m# Extract features for both normal and faulty data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mfeatures_normal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix_normal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0mfeatures_fault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix_fault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'matrix_normal' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2.2\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# فرضی که داده‌ها را دانلود و بارگذاری کرده‌اید\n",
        "normal_data = pd.read_csv('Normal_X1.csv')\n",
        "faulty_data = pd.read_csv('IR007_X_12k.csv')\n",
        "\n",
        "# تقسيم داده‌ها به ماتريس‌ها\n",
        "def create_matrix(data, M, N):\n",
        "    return np.array([data[i:i+N] for i in range(0, len(data)-N, N//2)][:M])\n",
        "\n",
        "M = 1001\n",
        "N = 200\n",
        "normal_matrix = create_matrix(normal_data.values.flatten(), M, N)\n",
        "faulty_matrix = create_matrix(faulty_data.values.flatten(), M, N)\n",
        "\n",
        "# ماتریس نهایی با برچسب‌ها\n",
        "normal_labels = np.zeros((normal_matrix.shape[0], 1))\n",
        "faulty_labels = np.ones((faulty_matrix.shape[0], 1))\n",
        "\n",
        "data_matrix = np.vstack((normal_matrix, faulty_matrix))\n",
        "labels = np.vstack((normal_labels, faulty_labels))\n",
        "\n",
        "# ادغام داده‌ها و برچسب‌ها\n",
        "dataset = np.hstack((data_matrix, labels))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "collapsed": true,
        "id": "dkJqJQXg_hT5",
        "outputId": "2fd72e0a-cfb1-43de-dadb-f9a4ac3c5d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'Normal_X1.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-bb59733a5dfb>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# فرضی که داده‌ها را دانلود و بارگذاری کرده‌اید\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mnormal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Normal_X1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mfaulty_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'IR007_X_12k.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Normal_X1.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2.2.B\n",
        "\n",
        "def extract_features(matrix):\n",
        "    features = np.zeros((matrix.shape[0], 8))\n",
        "    for i in range(matrix.shape[0]):\n",
        "        x = matrix[i]\n",
        "        features[i, 0] = np.std(x)\n",
        "        features[i, 1] = np.max(x)\n",
        "        features[i, 2] = np.mean(np.abs(x))\n",
        "        features[i, 3] = np.sqrt(np.mean(x**2))\n",
        "        features[i, 4] = np.mean(x)\n",
        "        features[i, 5] = np.sum(np.abs(x)) / len(x)\n",
        "        features[i, 6] = np.sum((x - np.mean(x))**3) / (len(x) * (np.std(x) ** 3))\n",
        "        features[i, 7] = np.sum((x - np.mean(x))**4) / (len(x) * (np.std(x) ** 4))\n",
        "    return features\n",
        "\n",
        "normal_features = extract_features(normal_matrix)\n",
        "faulty_features = extract_features(faulty_matrix)\n",
        "\n",
        "# ادغام ویژگی‌ها و برچسب‌ها\n",
        "features = np.vstack((normal_features, faulty_features))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "collapsed": true,
        "id": "gN7vHNXnA-Ld",
        "outputId": "256859de-8ff0-4ed9-9739-f5147e7b2d90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'normal_matrix' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-8030e0b19f19>\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mnormal_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormal_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mfaulty_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfaulty_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'normal_matrix' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42)\n"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "YJ4GihkmBS0I",
        "outputId": "1e3584c7-7bfe-4a86-b22f-a74fedabefe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'features' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-9d2c4c52d982>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'features' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2.2.D\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# آموزش مدل\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# پیش‌بینی\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# ارزیابی\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy:.2f}')\n"
      ],
      "metadata": {
        "id": "JQDEnJsEBdZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2.3\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# محاسبه تابع اتلاف\n",
        "loss = log_loss(y_test, y_pred)\n",
        "\n",
        "# رسم نمودار\n",
        "plt.plot(range(len(loss)), loss)\n",
        "plt.title(\"Loss Function\")\n",
        "plt.xlabel(\"Iterations\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "_nf9Ov7BCYDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2.5\n",
        "#part1\n",
        "import seaborn as sns\n",
        "\n",
        "# فرض بر این است که داده‌ها در یک DataFrame به نام weather_data بارگذاری شده‌اند\n",
        "correlation_matrix = weather_data.corr()\n",
        "\n",
        "# رسم هیتمپ\n",
        "sns.heatmap(correlation_matrix, annot=True)\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()\n",
        "\n",
        "# رسم هیستوگرام پراکندگی\n",
        "sns.pairplot(weather_data)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#part2\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# آموزش مدل LS\n",
        "ls_model = LinearRegression()\n",
        "ls_model.fit(X_train, y_train)\n",
        "\n",
        "# پیش‌بینی مدل LS\n",
        "y_pred_ls = ls_model.predict(X_test)\n",
        "\n",
        "# آموزش مدل RLS\n",
        "rls_model = Ridge(alpha=1.0)\n",
        "rls_model.fit(X_train, y_train)\n",
        "\n",
        "# پیش‌بینی مدل RLS\n",
        "y_pred_rls = rls_model.predict(X_test)\n",
        "\n",
        "# محاسبه خطاها\n",
        "mse_ls = mean_squared_error(y_test, y_pred_ls)\n",
        "mse_rls = mean_squared_error(y_test, y_pred_rls)\n",
        "\n",
        "print(f'MSE LS: {mse_ls:.2f}')\n",
        "print(f'MSE RLS: {mse_rls:.2f}')\n"
      ],
      "metadata": {
        "id": "CVT7zK3iCmip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2.5 part3\n",
        "from numpy.linalg import qr\n",
        "\n",
        "# پیاده‌سازی RLS با استفاده از QR-Decomposition\n",
        "def qr_decomposition(X, y):\n",
        "    Q, R = qr(X)\n",
        "    return np.linalg.solve(R, Q.T @ y)\n",
        "\n",
        "# استفاده از تابع برای تخمین\n",
        "beta = qr_decomposition(X_train, y_train)\n",
        "y_pred_qr = X_test @ beta\n",
        "\n",
        "# محاسبه خطا\n",
        "mse_qr = mean_squared_error(y_test, y_pred_qr)\n",
        "print(f'MSE QR: {mse_qr:.2f}')\n"
      ],
      "metadata": {
        "id": "pqPzjo3pC3_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#3.1\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# بارگیری داده‌ها\n",
        "url = \"https://example.com/szeged-weather-data.csv\"\n",
        "data = pd.read_csv(url)\n",
        "\n",
        "# رسم ماتریس همبستگی\n",
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = data.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n",
        "\n",
        "# رسم هیستوگرام پراکندگی ویژگی‌ها\n",
        "sns.pairplot(data[['Temperature', 'Humidity', 'Apparent Temperature']])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "collapsed": true,
        "id": "PgXNhXlhDJLm",
        "outputId": "26b6661e-76a3-4efc-f5aa-676bcc2e183e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "HTTPError",
          "evalue": "HTTP Error 404: Not Found",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-32745a3b6f88>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# بارگیری داده‌ها\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"https://example.com/szeged-weather-data.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# رسم ماتریس همبستگی\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m     \u001b[0;31m# open URLs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m     ioargs = _get_filepath_or_buffer(\n\u001b[0m\u001b[1;32m    717\u001b[0m         \u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m         \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36m_get_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;31m# assuming storage_options is to be interpreted as headers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m         \u001b[0mreq_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq_info\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m             \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Content-Encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"gzip\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[0;34m(self, request, response)\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;31m# request was successfully received, understood, and accepted.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m             response = self.parent.error(\n\u001b[0m\u001b[1;32m    635\u001b[0m                 'http', request, response, code, msg, hdrs)\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, proto, *args)\u001b[0m\n\u001b[1;32m    561\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'default'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'http_error_default'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/urllib/request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[0;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 404: Not Found"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3.1 & 3.2\n",
        "\n",
        "from sklearn.linear_model import LinearRegression, Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# تقسیم داده‌ها به آموزش و تست\n",
        "X = data[['Temperature', 'Humidity']]\n",
        "y = data['Apparent Temperature']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# مدل LS\n",
        "ls_model = LinearRegression()\n",
        "ls_model.fit(X_train, y_train)\n",
        "y_pred_ls = ls_model.predict(X_test)\n",
        "mse_ls = mean_squared_error(y_test, y_pred_ls)\n",
        "\n",
        "# مدل RLS\n",
        "rls_model = Ridge(alpha=1.0)\n",
        "rls_model.fit(X_train, y_train)\n",
        "y_pred_rls = rls_model.predict(X_test)\n",
        "mse_rls = mean_squared_error(y_test, y_pred_rls)\n",
        "\n",
        "# نمایش نتایج\n",
        "print(f'MSE LS: {mse_ls:.2f}')\n",
        "print(f'MSE RLS: {mse_rls:.2f}')\n",
        "\n",
        "# رسم نمودارهای مناسب\n",
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(y_test, y_pred_ls, alpha=0.7)\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('LS Model')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(y_test, y_pred_rls, alpha=0.7)\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('RLS Model')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "collapsed": true,
        "id": "pDYyAPu-F4oM",
        "outputId": "eb27ce1d-c551-4553-cf41-d3d1dfce1af2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'data' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-e36ef9dd719e>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# تقسیم داده‌ها به آموزش و تست\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Temperature'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Humidity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Apparent Temperature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
          ]
        }
      ]
    }
  ]
}