怎么实现堆排序（详细解析和代码实现） _二叉堆

本文概述

C++
Java
python
C#
PHP

堆排序是基于二叉堆数据结构的基于比较的排序技术。它类似于选择排序, 在这里我们首先找到最大元素, 然后将最大元素放在最后。我们对其余元素重复相同的过程。
什么是二叉堆？?
让我们首先定义一个完整的二叉树。完整的二叉树是一个二叉树, 其中除最后一个级别外, 每个级别都已完全填充, 并且所有节点都尽可能地靠左(来源维基百科：http://en.wikipedia.org/wiki/Binary_tree#Types_of_binary_trees)
一种二叉堆是一个完整的二叉树, 其中项以特殊顺序存储, 使得父节点中的值大于(或小于)其两个子节点中的值。前者称为最大堆, 后者称为最小堆。堆可以由二叉树或数组表示。
为什么要基于数组表示二叉堆？
由于Binary Heap是完整的Binary Tree, 因此可以很容易地将其表示为数组, 并且基于数组的表示节省空间。如果父节点存储在索引I处, 则可以通过2 * I + 1来计算左子节点, 通过2 * I + 2来计算右子节点(假设索引从0开始)。
用于按升序排序的堆排序算法：
1. 根据输入数据构建最大堆。
2. 此时, 最大的项目存储在堆的根目录中。将其替换为堆的最后一项, 然后将堆的大小减小1。最后, 堆放树的根。
3. 堆大小大于1时, 请重复步骤2。
如何构建堆？
只有在堆满了其子节点后, 堆过程才能应用于该节点。因此, 必须按自下而上的顺序执行堆化。
让我们借助示例来理解：

Input data: 4, 10, 3, 5, 1 4(0) /\ 10(1)3(2) /\ 5(3)1(4)The numbers in bracket represent the indices in the array representation of data.Applying heapify procedure to index 1: 4(0) /\ 10(1)3(2) /\ 5(3)1(4)Applying heapify procedure to index 0: 10(0) /\ 5(1)3(2) /\ 4(3)1(4) The heapify procedure calls itself recursively to build heap in top down manner.

C++

// C++ program for implementation of Heap Sort #include < iostream> using namespace std; // To heapify a subtree rooted with node i which is // an index in arr[]. n is size of heap void heapify( int arr[], int n, int i) { int largest = i; // Initialize largest as root int l = 2*i + 1; // left = 2*i + 1 int r = 2*i + 2; // right = 2*i + 2// If left child is larger than root if (l < n & & arr[l] > arr[largest]) largest = l; // If right child is larger than largest so far if (r < n & & arr[r] > arr[largest]) largest = r; // If largest is not root if (largest != i) { swap(arr[i], arr[largest]); // Recursively heapify the affected sub-tree heapify(arr, n, largest); } }// main function to do heap sort void heapSort( int arr[], int n) { // Build heap (rearrange array) for ( int i = n / 2 - 1; i > = 0; i--) heapify(arr, n, i); // One by one extract an element from heap for ( int i=n-1; i> 0; i--) { // Move current root to end swap(arr[0], arr[i]); // call max heapify on the reduced heap heapify(arr, i, 0); } }/* A utility function to print array of size n */ void printArray( int arr[], int n) { for ( int i=0; i< n; ++i) cout < < arr[i] < < " " ; cout < < "\n" ; }// Driver program int main() { int arr[] = {12, 11, 13, 5, 6, 7}; int n = sizeof (arr)/ sizeof (arr[0]); heapSort(arr, n); cout < < "Sorted array is \n" ; printArray(arr, n); }

Java

// Java program for implementation of Heap Sort public class HeapSort { public void sort( int arr[]) { int n = arr.length; // Build heap (rearrange array) for ( int i = n / 2 - 1 ; i > = 0 ; i--) heapify(arr, n, i); // One by one extract an element from heap for ( int i=n- 1 ; i> 0 ; i--) { // Move current root to end int temp = arr[ 0 ]; arr[ 0 ] = arr[i]; arr[i] = temp; // call max heapify on the reduced heap heapify(arr, i, 0 ); } }// To heapify a subtree rooted with node i which is // an index in arr[]. n is size of heap void heapify( int arr[], int n, int i) { int largest = i; // Initialize largest as root int l = 2 *i + 1 ; // left = 2*i + 1 int r = 2 *i + 2 ; // right = 2*i + 2// If left child is larger than root if (l < n & & arr[l] > arr[largest]) largest = l; // If right child is larger than largest so far if (r < n & & arr[r] > arr[largest]) largest = r; // If largest is not root if (largest != i) { int swap = arr[i]; arr[i] = arr[largest]; arr[largest] = swap; // Recursively heapify the affected sub-tree heapify(arr, n, largest); } }/* A utility function to print array of size n */ static void printArray( int arr[]) { int n = arr.length; for ( int i= 0 ; i< n; ++i) System.out.print(arr[i]+ " " ); System.out.println(); }// Driver program public static void main(String args[]) { int arr[] = { 12 , 11 , 13 , 5 , 6 , 7 }; int n = arr.length; HeapSort ob = new HeapSort(); ob.sort(arr); System.out.println( "Sorted array is" ); printArray(arr); } }

python

# Python program for implementation of heap Sort# To heapify subtree rooted at index i. # n is size of heap def heapify(arr, n, i): largest = i # Initialize largest as root l = 2 * i + 1# left = 2*i + 1 r = 2 * i + 2# right = 2*i + 2# See if left child of root exists and is # greater than root if l < n and arr[i] < arr[l]: largest = l# See if right child of root exists and is # greater than root if r < n and arr[largest] < arr[r]: largest = r# Change root, if needed if largest ! = i: arr[i], arr[largest] = arr[largest], arr[i] # swap# Heapify the root. heapify(arr, n, largest)# The main function to sort an array of given size def heapSort(arr): n = len (arr)# Build a maxheap. for i in range (n / / 2 - 1 , - 1 , - 1 ): heapify(arr, n, i)# One by one extract elements for i in range (n - 1 , 0 , - 1 ): arr[i], arr[ 0 ] = arr[ 0 ], arr[i] # swap heapify(arr, i, 0 )# Driver code to test above arr = [ 12 , 11 , 13 , 5 , 6 , 7 ] heapSort(arr) n = len (arr) print ( "Sorted array is" ) for i in range (n): print ( "%d" % arr[i]), # This code is contributed by Mohit Kumra

// C# program for implementation of Heap Sort using System; public class HeapSort { public void sort( int [] arr) { int n = arr.Length; // Build heap (rearrange array) for ( int i = n / 2 - 1; i > = 0; i--) heapify(arr, n, i); // One by one extract an element from heap for ( int i=n-1; i> 0; i--) { // Move current root to end int temp = arr[0]; arr[0] = arr[i]; arr[i] = temp; // call max heapify on the reduced heap heapify(arr, i, 0); } }// To heapify a subtree rooted with node i which is // an index in arr[]. n is size of heap void heapify( int [] arr, int n, int i) { int largest = i; // Initialize largest as root int l = 2*i + 1; // left = 2*i + 1 int r = 2*i + 2; // right = 2*i + 2// If left child is larger than root if (l < n & & arr[l] > arr[largest]) largest = l; // If right child is larger than largest so far if (r < n & & arr[r] > arr[largest]) largest = r; // If largest is not root if (largest != i) { int swap = arr[i]; arr[i] = arr[largest]; arr[largest] = swap; // Recursively heapify the affected sub-tree heapify(arr, n, largest); } }/* A utility function to print array of size n */ static void printArray( int [] arr) { int n = arr.Length; for ( int i=0; i< n; ++i) Console.Write(arr[i]+ " " ); Console.Read(); }// Driver program public static void Main() { int [] arr = {12, 11, 13, 5, 6, 7}; int n = arr.Length; HeapSort ob = new HeapSort(); ob.sort(arr); Console.WriteLine( "Sorted array is" ); printArray(arr); } }// This code is contributed // by Akanksha Rai(Abby_akku)

PHP

< ?php// Php program for implementation of Heap Sort// To heapify a subtree rooted with node i which is // an index in arr[]. n is size of heap function heapify(& $arr , $n , $i ) { $largest = $i ; // Initialize largest as root $l = 2* $i + 1; // left = 2*i + 1 $r = 2* $i + 2; // right = 2*i + 2// If left child is larger than root if ( $l < $n & & $arr [ $l ] > $arr [ $largest ]) $largest = $l ; // If right child is larger than largest so far if ( $r < $n & & $arr [ $r ] > $arr [ $largest ]) $largest = $r ; // If largest is not root if ( $largest != $i ) { $swap = $arr [ $i ]; $arr [ $i ] = $arr [ $largest ]; $arr [ $largest ] = $swap ; // Recursively heapify the affected sub-tree heapify( $arr , $n , $largest ); } }// main function to do heap sort function heapSort(& $arr , $n ) { // Build heap (rearrange array) for ( $i = $n / 2 - 1; $i > = 0; $i --) heapify( $arr , $n , $i ); // One by one extract an element from heap for ( $i = $n -1; $i > 0; $i --) { // Move current root to end $temp = $arr [0]; $arr [0] = $arr [ $i ]; $arr [ $i ] = $temp ; // call max heapify on the reduced heap heapify( $arr , $i , 0); } }/* A utility function to print array of size n */ function printArray(& $arr , $n ) { for ( $i = 0; $i < $n ; ++ $i ) echo ( $arr [ $i ]. " " ) ; } // Driver program $arr = array (12, 11, 13, 5, 6, 7); $n = sizeof( $arr )/sizeof( $arr [0]); heapSort( $arr , $n ); echo 'Sorted array is ' . "\n" ; printArray( $arr , $n ); // This code is contributed by Shivi_Aggarwal ?>

输出如下：

Sorted array is 5 6 7 11 12 13

这里是以前的C代码供参考。
注意：
堆排序是一种就地算法。
它的典型实现方式不稳定, 但是可以使其稳定(参考lsin这篇文章)
时间复杂度：heapify的时间复杂度为O(Logn)。 createAndBuildHeap()的时间复杂度为O(n), 堆排序的整体时间复杂度为O(nLogn)。
HeapSort的应用
1.对几乎排序(或K排序)的数组进行排序
2.数组中的k个最大(或最小)元素
堆排序算法的用途有限, 因为Quicksort和Mergesort在实践中更好。但是, 堆数据结构本身已被大量使用。看到堆数据结构的应用
堆排序图解：