In: Computer Science
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Heap sort is a comparison based sorting technique based on Binary Heap data structure. It is similar to selection sort where we first find the maximum element and place the maximum element at the end. We repeat the same process for the remaining elements.
What is Binary Heap?
Let us first define a Complete Binary Tree. A complete binary tree
is a binary tree in which every level, except possibly the last, is
completely filled, and all nodes are as far left as possible
(Source Wikipedia)
A Binary Heap is a Complete Binary Tree where items are stored in a special order such that value in a parent node is greater(or smaller) than the values in its two children nodes. The former is called as max heap and the latter is called min-heap. The heap can be represented by a binary tree or array.
Why array based representation for Binary
Heap?
Since a Binary Heap is a Complete Binary Tree, it can be easily
represented as an array and the array-based representation is
space-efficient. If the parent node is stored at index I, the left
child can be calculated by 2 * I + 1 and right child by 2 * I + 2
(assuming the indexing starts at 0).
Heap Sort Algorithm for sorting in increasing
order:
1. Build a max heap from the input data.
2. At this point, the largest item is stored at
the root of the heap. Replace it with the last item of the heap
followed by reducing the size of heap by 1. Finally, heapify the
root of the tree.
3. Repeat step 2 while size of heap is greater
than 1.
How to build the heap?
Heapify procedure can be applied to a node only if its children
nodes are heapified. So the heapification must be performed in the
bottom-up order.
Lets understand with the help of an example:
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.
// 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);
}