trace_time_check_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNYe0yCZd9IyEV++S/bS35c",
      "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/AhmedCode99/TraceOfArrayCode/blob/main/trace_time_check_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mat = [[2,1,1,1],[1,3,1,1],[1,1,4,1],[1,1,1,5]] # Insert a square matrix\n",
        "\n",
        "\n",
        "rows,columns = np.shape(mat) # Save the number of rows and columns\n",
        "\n",
        "def recursive_trace(matrix): # Defining a Function that calculates Trace using Recursion\n",
        "    n = len(matrix)\n",
        "    if n == 1:\n",
        "        return matrix[0][0]\n",
        "    else:\n",
        "        return matrix[0][0] + recursive_trace([matrix[i][1:] for i in range(1, n)])\n",
        "\n",
        "if  rows == columns:\n",
        "\n",
        "  t0 = time.time()\n",
        "  trace = np.einsum('ii', mat) # Calculating Trace Using EinSum\n",
        "  print(\"Trace Using EinSum = \", trace)\n",
        "  t1 = time.time()\n",
        "\n",
        "  t2 = time.time()\n",
        "  trace = np.trace(mat) # Calculating Trace Using NumPy\n",
        "  print(\"Trace Using NumPy = \", trace)\n",
        "  t3 = time.time()\n",
        "\n",
        "  t4 = time.time()\n",
        "  trace = sum(mat[i][i] for i in range(len(mat))) # Calculating Trace Using List Comprehension\n",
        "  print(\"Trace Using List Comprehension = \", trace)\n",
        "  t5 = time.time()\n",
        "\n",
        "\n",
        "  #Calculating Trace using For Loop\n",
        "  t6 = time.time()\n",
        "  trace = 0\n",
        "  for j in range(len(mat)):\n",
        "    trace = trace + mat[j][j]\n",
        "  print(\"Trace Using For Loop = \", trace)\n",
        "  t7 = time.time()\n",
        "\n",
        "\n",
        "  #Calculating Trace using While Loop\n",
        "\n",
        "  t8 = time.time()\n",
        "  trace = 0\n",
        "  i=0\n",
        "\n",
        "  while i < len(mat):\n",
        "    trace = trace + mat[i][i]\n",
        "    i=i+1\n",
        "\n",
        "  print(\"Trace Using While Loop = \", trace)\n",
        "  t9 = time.time()\n",
        "\n",
        "\n",
        "  # Calculating Trace using SymPy\n",
        "\n",
        "  from sympy import Matrix\n",
        "\n",
        "  t10 = time.time()\n",
        "  matrix = Matrix(mat)\n",
        "\n",
        "  trace = matrix.trace()\n",
        "\n",
        "  print(\"Trace Using SymPy = \", trace)\n",
        "  t11 = time.time()\n",
        "\n",
        "  # Calculating Trace Using Recursion\n",
        "  t12 = time.time()\n",
        "  trace = recursive_trace(mat)\n",
        "  t13 = time.time()\n",
        "  print(\"Trace Using Recursion = \", trace)\n",
        "\n",
        "  # Calculating  Trace Using Eigenvalues Mthod\n",
        "\n",
        "  t14= time.time()\n",
        "  eigenvalues, _ = np.linalg.eig(mat)\n",
        "  trace = eigenvalues.sum()\n",
        "  t15 = time.time()\n",
        "  print(\"Trace Using Eigenvalues = \", trace)\n",
        "  # Plotting Results\n",
        "\n",
        "  # Lists of what will be plotted\n",
        "  method = [\"EinSum\",\"NumPy\",\"List\\nComprehension\",\n",
        "          \"For Loop\", \"While Loop\",\"SymPy\", \"Recursion\", \"Eigenvalues\"] # List of Methods Used\n",
        "\n",
        "  dt = [t1-t0, t3-t2, t5-t4, t7-t6, t9-t8, t11-t10, t13-t12, t15-t14] # Time it took for each method\n",
        "\n",
        "\n",
        "  # Create a bar graph\n",
        "  plt.figure(figsize=(9, 9))  # Optional: Set the figure size\n",
        "  plt.bar(method, dt)\n",
        "\n",
        "  # Adding labels and title\n",
        "  plt.xlabel('Method')\n",
        "  plt.ylabel('Time (s)')\n",
        "  plt.title('Execution Time of a Program Calculating Trace of a {}x{} Matrix'.format(rows,rows))\n",
        "\n",
        "  # Display the plot\n",
        "  plt.show()\n",
        "\n",
        "else:\n",
        "    print(\"The Matrix You Inserted Is Not Square. The Trace Operator is Only Defined For Square Matrices. Please Insert A Square Matrix\")"
      ],
      "metadata": {
        "id": "xRU4J7AvNSqY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 954
        },
        "outputId": "da1ada63-1de3-4846-a8c0-7ac8a5f61788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trace Using EinSum =  14\n",
            "Trace Using NumPy =  14\n",
            "Trace Using List Comprehension =  14\n",
            "Trace Using For Loop =  14\n",
            "Trace Using While Loop =  14\n",
            "Trace Using SymPy =  14\n",
            "Trace Using Recursion =  14\n",
            "Trace Using Eigenvalues =  14.000000000000004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5eriPYG154PN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}