Heap_Sort.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Heap Sort.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP1M+r24RZ9O3RrUn2tN2GF",
      "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/saima8/DS-and-Algo/blob/main/Heap_Sort.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4w2TWYBMiWwi"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtdgSvSvdqWw"
      },
      "source": [
        "**1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-hckVeObNZv",
        "outputId": "8312b304-343b-4576-f98c-f84250ec5c6e"
      },
      "source": [
        "# Python program for implementation of heap Sort\n",
        "#geeksforgeek\n",
        "# To heapify subtree rooted at index i.\n",
        "# n is size of heap\n",
        "\n",
        "\n",
        "def heapify(arr, n, i):\n",
        "\tlargest = i # Initialize largest as root\n",
        "\tl = 2 * i + 1\t # left = 2*i + 1\n",
        "\tr = 2 * i + 2\t # right = 2*i + 2\n",
        "\n",
        "\t# See if left child of root exists and is\n",
        "\t# greater than root\n",
        "\tif l < n and arr[largest] < arr[l]:\n",
        "\t\tlargest = l\n",
        "\n",
        "\t# See if right child of root exists and is\n",
        "\t# greater than root\n",
        "\tif r < n and arr[largest] < arr[r]:\n",
        "\t\tlargest = r\n",
        "\n",
        "\t# Change root, if needed\n",
        "\tif largest != i:\n",
        "\t\tarr[i], arr[largest] = arr[largest], arr[i] # swap\n",
        "\n",
        "\t\t# Heapify the root.\n",
        "\t\theapify(arr, n, largest)\n",
        "\n",
        "# The main function to sort an array of given size\n",
        "\n",
        "\n",
        "def heapSort(arr):\n",
        "\tn = len(arr)\n",
        "\n",
        "\t# Build a maxheap.\n",
        "\tfor i in range(n//2 - 1, -1, -1):\n",
        "\t\theapify(arr, n, i)\n",
        "\n",
        "\t# One by one extract elements\n",
        "\tfor i in range(n-1, 0, -1):\n",
        "\t\tarr[i], arr[0] = arr[0], arr[i] # swap\n",
        "\t\theapify(arr, i, 0)\n",
        "\n",
        "\n",
        "# Driver code\n",
        "arr = [12, 11, 13, 5, 6, 7]\n",
        "heapSort(arr)\n",
        "n = len(arr)\n",
        "print(\"Sorted array is\")\n",
        "for i in range(n):\n",
        "\tprint(\"%d\" % arr[i]),\n",
        "# This code is contributed by Mohit Kumra\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sorted array is\n",
            "5\n",
            "6\n",
            "7\n",
            "11\n",
            "12\n",
            "13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcYeMnmjdtjW"
      },
      "source": [
        "**2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quf14sWJdNqv",
        "outputId": "efea9ef8-5903-4d1b-d5b5-ef5893bdacfa"
      },
      "source": [
        "#programiz\n",
        "\n",
        "def heapify(arr, n, i):\n",
        "  # find largest among root and children\n",
        "  largest = i\n",
        "  l = 2*i+1\n",
        "  r = 2*i+2\n",
        "\n",
        "  if l < n and arr[i] < arr[l]:\n",
        "    largest = l\n",
        "  if r < n and arr[largest] < arr[r]:\n",
        "    largest = r\n",
        "  #if not root is largest, swap with largest and continue heapifying\n",
        "  if largest != i:\n",
        "    arr[i], arr[largest] = arr[largest], arr[i]\n",
        "    heapify(arr, n, largest)\n",
        "\n",
        "def heapsort(arr):\n",
        "  n = len(arr)\n",
        "\n",
        "  #build max heap\n",
        "  for i in range(n//2, -1, -1):\n",
        "    heapify(arr, n, i)\n",
        "  for i in range(n-1, 0, -1):\n",
        "    arr[i], arr[0] = arr[0], arr[i]\n",
        "\n",
        "    #Heapify root element\n",
        "    heapify(arr, i, 0)\n",
        "\n",
        "arr = [1, 12, 9, 5, 6, 10]\n",
        "heapsort(arr)\n",
        "n = len(arr)\n",
        "print(arr)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 5, 6, 9, 10, 12]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7cPb0nOlfaFK"
      },
      "source": [
        "**3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ekG0W9qTfErF",
        "outputId": "05900f2d-825d-43ab-caf0-f3e49dab5c4c"
      },
      "source": [
        "#mygreatlearning\n",
        "def heapify(arr, n, i):\n",
        "  largest = i # initialize max as root\n",
        "  l = 2*i+1\n",
        "  r = 2*i+2\n",
        "\n",
        "  #if left child is greater than root\n",
        "  if l < n and arr[i] < arr[l]:\n",
        "    largest = l\n",
        "  # if right child is greater than root\n",
        "  if r < n and arr[largest] < arr[r]:\n",
        "    largest = r\n",
        "\n",
        "  # if max is not root\n",
        "  if largest !=i:\n",
        "     arr[i], arr[largest] = arr[largest], arr[i]\n",
        "     # heapify the root\n",
        "     heapify(arr, n, largest)\n",
        "\n",
        "#main function \n",
        "def heapSort(arr):\n",
        "  n = len(arr)\n",
        "\n",
        "  #build max heap\n",
        "  for i in range(n//2, -1, -1):\n",
        "    heapify(arr, n, i)\n",
        "\n",
        "  # Extract elements frrom heap one by one\n",
        "  for i in range(n-1, 0, -1):\n",
        "    arr[i], arr[0] = arr[0], arr[i]\n",
        "    heapify(arr, i, 0)\n",
        "\n",
        "#Driver code\n",
        "arr = [11, 34, 9, 5, 16, 10] \n",
        "n = len(arr) \n",
        "print(\"Original array:\") \n",
        "for i in range(n):       \n",
        "    print(\"%d \" % arr[i], end = '')\n",
        "heapSort(arr) \n",
        "print(\"Sorted array:\") \n",
        "for i in range(n):       \n",
        "    print(\"%d \" % arr[i], end = '')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original array:\n",
            "11 34 9 5 16 10 Sorted array:\n",
            "5 9 10 11 16 34 "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7i1tmePhrVm"
      },
      "source": [
        "**4**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7OzGkDzhFFV",
        "outputId": "2314b37f-7d83-4e31-fde9-1e3afa4da188"
      },
      "source": [
        "# function for heap sort which calls heapify function \n",
        "# to build the max heap and then swap last element of \n",
        "# the max-heap with the first element\n",
        "# exclude the last element from the heap and rebuild the heap\n",
        "def heapsort(MyList):\n",
        "  n = len(MyList)\n",
        "  for i in range(n//2, -1, -1):\n",
        "    heapify(MyList, n-1, i)\n",
        "\n",
        "  for i in range(n-1, -1, -1):\n",
        "    # swap last element of the max-heap with the first element\n",
        "    MyList[i], MyList[0] = MyList[0], MyList[i]\n",
        "    \n",
        "    # exclude the last element from the heap and rebuild the heap \n",
        "    heapify(MyList, i-1, 0)\n",
        "\n",
        "# heapify function is used to build the max heap\n",
        "# max heap has maximum element at the root which means\n",
        "# first element of the array will be maximum in max heap\n",
        "def heapify(MyList, n, i):\n",
        "  max, left, right = i, 2*i + 1, 2*i + 2\n",
        "  # if the left element is greater than root\n",
        "  if left <= n and MyList[left] > MyList[max]:\n",
        "    max = left\n",
        "\n",
        "  # if the right element is greater than root\n",
        "  if right <= n and MyList[right] > MyList[max]:\n",
        "    max = right\n",
        "\n",
        "  # if the max is not i\n",
        "  if max != i:\n",
        "    MyList[i], MyList[max] = MyList[max], MyList[i]\n",
        "    # Recursively heapify the affected sub-tree\n",
        "    heapify(MyList, n, max) \n",
        "\n",
        "# function to print list\n",
        "def PrintList(MyList):\n",
        "  for i in MyList:\n",
        "    print(i, end=\" \")\n",
        "  print(\"\\n\")\n",
        "  \n",
        "# test the code                 \n",
        "MyList = [10, 1, 23, 50, 7, -4]\n",
        "n = len(MyList)\n",
        "print(\"Original List\")\n",
        "PrintList(MyList)\n",
        "\n",
        "heapsort(MyList)\n",
        "print(\"Sorted List\")\n",
        "PrintList(MyList)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original List\n",
            "10 1 23 50 7 -4 \n",
            "\n",
            "Sorted List\n",
            "-4 1 7 10 23 50 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El22Nzioh1th"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}