Heap examples in java

Is there a Heap in java?

Examples for the heap data structure in java Example for max heap data structure in java to insert an element in the max heap – Program example 1 – An output of the above code is – As in the above program, the Heap class is created which contains heapify(), insert() and printHeap() functions. Solution 2: Min heap: max heap: Solution 3: In Java PriorityQueue can be used as a Heap.

Is there a Heap in java?

I am porting a C++ library to Java and I need a heap data structure. Is there a standard implementation or will I need to do it myself?

For Java 8, updating on an existing answer:

Min Heap: —> to keep the min element always on top, so you can access it in O(1).

PriorityQueue minHeap = new PriorityQueue(); 

Max Heap: —> to keep the max element always on top, the same order as above.

PriorityQueue maxHeap = new PriorityQueue<>(Comparator.reverseOrder()); 

Which is the same as (Integer o1, Integer o2) -> Integer.compare(o2, o1) or — Integer.compare(o1, o2) as suggested from other answers.

And you can use:
add —> to add element to the queue. O(log n)
remove —> to get and remove the min/max. O(log n)
peek —> to get, but not remove the min/max. O(1)

PriorityQueue minHeap = new PriorityQueue(); 

PriorityQueue maxHeap = new PriorityQueue(new Comparator() < @Override public int compare(Integer o1, Integer o2) < return - Integer.compare(o1, o2); >>); 

In Java PriorityQueue can be used as a Heap.

PriorityQueue minHeap = new PriorityQueue<>(); 

PriorityQueue maxHeap = new PriorityQueue<>(Comparator.reverseOrder()); 

PriorityQueue uses a heap. Based on the oracle documentation at https://docs.oracle.com/javase/8/docs/api/java/util/PriorityQueue.html it seems likely that it is an Implementation of a binary heap. I don’t think there is an official implementation of a fibonacci or pairing heap, though I’d love to see either one of the two available.

Heap Sort in Java, every node’s value must be less or equal to all values stored in its children; it’s a complete tree, which means it has the least possible height; …

Heap data structure in Java

Introduction to Heap data structure in Java

The following article provides an outline for Heap data structure in java. The heap is a special type of tree-based data structure where the tree is a complete binary tree. On the other hand, the binary tree is a tree in which each node can have a maximum of two children. Therefore, we must first understand the full binary tree before understanding more about the heap data structure.

The root node of a heap data structure is compared to its children and ordered according to the order. If x is a root node and y is one of its children, the property key (x)>= key (y) will produce a maximum heap. The “Heap Property” refers to the relationship between the root and the child node mentioned property key (x)>= key (y).

The heap can be one of two kinds, depending on the order of parent-child nodes:

The root node key in a Max-Heap is the greatest of all the keys in a heap. Therefore, it should be ensured that all sub trees in a heap have the same property recursively.

Below is an illustration of a Min-heap tree. The root node is greater than its children, as we can see.

The root node key in a Min-Heap is the smallest or least significant of all the other keys in a heap. This property should be recursively valid in all other subtrees in a heap, just as it is in the Max Heap.

Below is an illustration of a Min-heap tree. The root key is the smallest of all the keys in a heap, as we can see.

The time complexity of the is O( logn ) because the tree’s height determines the maximum number of comparisons available in the max heap. Thus, the full binary tree’s height is always logn .

A straightforward method for implementing a Heap data structure in Java –

A heapify is the method of converting a binary tree into a heap data structure. A Min-Heap or a Max-Heap can be made with it.

1. Accept an input array.
2. From the array, create a complete binary tree.
3. Begin with the first index of a non-leaf node, whose index is n/2 – 1.
4. Make the current element I the biggest.
5. The index of the left child is 2i + 1, and 2i+2 is the index of the right child.

Set leftChildIndex to largest if leftChild is greater than currentElement (i.e. element at ith index).

Set rightChildIndex to largest if rightChild is greater than the element in largest.

1. Replace the largest element with the currentElement.
2. Steps 3 to 7 should be repeated before the subtrees are heapified as well.

Step 1: make i the largest

Step 2: lChild = 2i + 1

Step 3: rchild = 2i + 2

Step 4: if lChild > arr[largest]

set lChildIndex as largest

Step 5: if rChild > arr[largest]

set rChildIndex as largest

Step 6: swap arr[i] and arr[largest]

loop from the non-leaf node’s first index to zero

Return value – The return value of this algorithm is the max heap.

Examples for the heap data structure in java

Example for max Heap Data Structure in java to insert an element in the max heap –

Program example 1 –

package jex;
import java.util.ArrayList;
class Heap <
void heapify(ArrayList mh, int i) <
int size = mh.size();
int largest = i;
int lc = 2 * i + 1;
int rc = 2 * i + 2;
if (lc < size && mh.get(lc) >mh.get(largest))
largest = lc;
if (rc < size && mh.get(rc) >mh.get(largest))
largest = rc;
if (largest != i) <
int temp = mh.get( largest );
mh.set( largest, mh.get(i) );
mh.set( i, temp );
heapify(mh, largest);
>
>
void insert(ArrayList mh, int n) <
int size = mh.size();
if (size == 0) <
mh.add( n );
> else <
mh.add( n );
for (int i = size / 2 — 1; i >= 0; i—) <
heapify( mh, i);
>
>
>
void printHeap(ArrayList a, int size) <
for (Integer i : a) <
System.out.print(i + » «);
>
System.out.println();
>
public static void main(String args[]) <
ArrayList a = new ArrayList ();
int size = a.size();
Heap h = new Heap();
h.insert(a, 13);
h.insert(a, 14);
h.insert(a, 65);
h.insert(a, 50);
h.insert(a, 20);
System.out.println(«The converted Max Heap array is : «);
h.printHeap(a, size);
>
>

An output of the above code is –

As in the above program, the Heap class is created which contains heapify(), insert() and printHeap() functions. The heapify() function is used to create the max heap from the passed ArrayList by setting the leftchild or rightchild largest based on the condition. The insert() function is used to insert the number into the max heap, and the printHeap() function is used to print the max heap. Then, in the main function, the ArrayList of an integer is created, and also the Heap class object is created. Next, insert the element into the heap object by calling the insert() function on the heap object. After inserting all the elements calling the printHeap() function which prints the max heap, as we can see in the above output.

Conclusion

The heap is based on the complete binary tree data structure. The binary tree is a tree in which each node can have a maximum of two children. Thus, there are two kinds of heap which are max heap and min-heap. The time complexity of the max heap is O(logn).

Final thoughts

This is a guide to Heap data structure in Java. Here we discuss the full binary tree before understanding more about the heap data structure.

Java Program to Implement Leftist Heap, Now we will write a java program for performing certain operations on a leftist Heap (Inorder Traversal) like insert, delete, clear, and check for empty. …

Java Program to Implement Binomial Heap

A Binomial Heap is a set of Binomial Trees where each Binomial Tree follows Min Heap property. And there can be at most one Binomial Tree of any degree. It is explained to depth in the below illustration as follows:

Illustration:

There are 2 binomial heaps been depicted below out here saw follows:

BINOMIAL HEAP 1 12------------10--------------------20 / \ / | \ 15 50 70 50 40 | / | | 30 80 85 65 | 100 A Binomial Heap with 13 nodes. It is a collection of 3 Binomial Trees of orders 0, 2 and 3 from left to right. BINOMIAL HEAP 2 10--------------------20 / \ / | \ 15 50 70 50 40 | / | | 30 80 85 65 | 100 A Binomial Heap with 12 nodes. It is a collection of 2 Binomial Trees of orders 2 and 3 from left to right.

Let us now do discuss the binary representation of a number and binomial heaps. A Binomial Heap with n nodes has the number of Binomial Trees equal to the number of set bits in the binary representation of n.

For example, let n be 13, there are 3 set bits in the binary representation of n (00001101), hence 3 Binomial Trees. We can also relate the degree of these Binomial Trees with positions of set bits. With this relation, we can conclude that there are O(Logn) Binomial Trees in a Binomial Heap with ‘n’ nodes.

Operations of the binomial heap are as follows :

1. Insert(K): Insert an element K into the binomial heap
2. Delete(k): Deletes the element k from the heap
3. getSize(): Returns the size of the heap
4. makeEmpty(): Makes the binomial heap empty by deleting all the elements
5. checkEmpty(): Check if the binomial heap is empty or not
6. displayHeap(): Prints the binomial heap

Implementation:

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Heap Data Structure Tutorial with Examples

Heap is a tree-based data structure in which all nodes in the tree are in a specific order. There are 2 types of heap, typically:

• Max Heap: all parent node’s values are greater than or equal to children node’s values, root node value is the largest.

• Min Heap: all parent node’s values are less than or equal to children node’s values, root node value is the smallest.

Basic operations

• Insert aka Push: add a new node into the heap
• Remove aka Pop: retrieves and removes the min or the max node of the heap
• Examine aka Peek: retrieves without removes the min or the max node of the heap
• Heapify: maintains max or min-heap property (all parent node’s values should be greater/less than or equal to the child node’s values)

Implementations

A common implementation of a heap is the binary heap which based on binary tree data structure

You can implement a binary heap with either a static array (capacity restricted) or a dynamic array

Binary Max Heap implementation example with Static Array

• Represented by 1-based integer array A[N+1]
• With a node A[k] (1 <=k<=N)
  • Its parent is A[k/2]
  • Left child is A[k*2] (k*2
  • Right child is A[k*2+1] (k*2+1

  public class MaxHeapByArray < private int[] heap; private int size; public MaxHeapByArray(int capacity) < this.heap = new int[capacity+1]; this.heap[0] = Integer.MAX_VALUE; this.size = 0; >private void swap(int i, int j) < int tmp = heap[i]; heap[i] = heap[j]; heap[j] = tmp; >private void heapifyDown(int k) < int largest = k; int leftIndex = 2*k; int rightIndex = 2*k + 1; if (leftIndex heap[largest]) < largest = leftIndex; >if (rightIndex heap[largest]) < largest = rightIndex; >if (largest != k) < swap(k, largest); heapifyDown(largest); >> private void heapifyUp(int k) < while (heap[k] >heap[k/2]) < swap(k , k/2); k = k/2; >> private void print() < for (int i = 1; i > public void push(int x) < heap[++size] = x; heapifyUp(size); >public int pop() < int head = heap[1]; heap[1] = heap[size--]; heapifyDown(1); return head; >public int peek() < return heap[1]; >public static void main(String[] args) < MaxHeapByArray maxHeap = new MaxHeapByArray(5); maxHeap.push(3); maxHeap.push(1); maxHeap.push(7); maxHeap.push(2); maxHeap.push(9); maxHeap.print(); System.out.println(maxHeap.peek()); System.out.println(maxHeap.pop()); System.out.println(maxHeap.peek()); >> 

  Binary Min Heap implementation example with Static Array

  • Represented by 1-based integer array A[N+1]
  • With a node A[k] (1 <=k<=N)
    • Its parent is A[k/2]
    • Left child is A[k*2] (k*2
    • Right child is A[k*2+1] (k*2+1

    public class MinHeapByArray < private int[] heap; private int size; public MinHeapByArray(int capacity) < this.heap = new int[capacity+1]; this.heap[0] = Integer.MIN_VALUE; this.size = 0; >private void swap(int i, int j) < int tmp = heap[i]; heap[i] = heap[j]; heap[j] = tmp; >private void heapifyDown(int k) < int smallest = k; int leftIndex = 2*k; int rightIndex = 2*k + 1; if (leftIndex if (rightIndex if (smallest != k) < swap(k, smallest); heapifyDown(smallest); >> private void heapifyUp(int k) < while (heap[k] < heap[k/2]) < swap(k , k/2); k = k/2; >> private void print() < for (int i = 1; i > public void push(int x) < heap[++size] = x; heapifyUp(size); >public int pop() < int head = heap[1]; heap[1] = heap[size--]; heapifyDown(1); return head; >public int peek() < return heap[1]; >public boolean isEmpty() < return size == 0; >public static void main(String[] args) < MinHeapByArray minHeap = new MinHeapByArray(5); minHeap.push(3); minHeap.push(1); minHeap.push(7); minHeap.push(2); minHeap.push(9); minHeap.print(); System.out.println(minHeap.peek()); System.out.println(minHeap.pop()); System.out.println(minHeap.peek()); >> 

    Applications

    • Heaps can be used to implement Priority Queues
    • Find the shortest paths between vertices in a Graph Data Structure The shortest path is a path between two vertices in a graph such that the total sum of the weights of the constituent edges is minimum You can implement an algorithm to find the shortest path by using a Min-Heap and Dijkstra algorithm Learn more

    Heaps in Java

    In Java, the binary heap data structure is used to implement priority queues including java.util.PriorityQueue and java.util.concurrent.PriorityBlockingQueue

    Giau Ngo

    Giau Ngo is a software engineer, creator of HelloKoding. He loves coding, blogging, and traveling. You may find him on GitHub and LinkedIn

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