Public speaking course notes Read "Dynamo, Amazon’s Highly Available Key-value Store" Read "Bigtable, A Distributed Storage System for Structured Data" Read "Streaming Systems" 3, Watermarks Read "Streaming Systems" 1&2, Streaming 101 Read "F1, a distributed SQL database that scales" Read "Zanzibar, Google’s Consistent, Global Authorization System" Read "Spanner, Google's Globally-Distributed Database" Read "Designing Data-intensive applications" 12, The Future of Data Systems IOS development with Swift Read "Designing Data-intensive applications" 10&11, Batch and Stream Processing Read "Designing Data-intensive applications" 9, Consistency and Consensus Read "Designing Data-intensive applications" 8, Distributed System Troubles Read "Designing Data-intensive applications" 7, Transactions Read "Designing Data-intensive applications" 6, Partitioning Read "Designing Data-intensive applications" 5, Replication Read "Designing Data-intensive applications" 3&4, Storage, Retrieval, Encoding Read "Designing Data-intensive applications" 1&2, Foundation of Data Systems Three cases of binary search TAMU Operating System 2 Memory Management TAMU Operating System 1 Introduction Overview in cloud computing 2 TAMU Operating System 7 Virtualization TAMU Operating System 6 File System TAMU Operating System 5 I/O and Disk Management TAMU Operating System 4 Synchronization TAMU Operating System 3 Concurrency and Threading TAMU Computer Networks 5 Data Link Layer TAMU Computer Networks 4 Network Layer TAMU Computer Networks 3 Transport Layer TAMU Computer Networks 2 Application Layer TAMU Computer Networks 1 Introduction Overview in distributed systems and cloud computing 1 A well-optimized Union-Find implementation, in Java A heap implementation supporting deletion TAMU Advanced Algorithms 3, Maximum Bandwidth Path (Dijkstra, MST, Linear) TAMU Advanced Algorithms 2, B+ tree and Segment Intersection TAMU Advanced Algorithms 1, BST, 2-3 Tree and Heap TAMU AI, Searching problems Factorization Machine and Field-aware Factorization Machine for CTR prediction TAMU Neural Network 10 Information-Theoretic Models TAMU Neural Network 9 Principal Component Analysis TAMU Neural Network 8 Neurodynamics TAMU Neural Network 7 Self-Organizing Maps TAMU Neural Network 6 Deep Learning Overview TAMU Neural Network 5 Radial-Basis Function Networks TAMU Neural Network 4 Multi-Layer Perceptrons TAMU Neural Network 3 Single-Layer Perceptrons Princeton Algorithms P1W6 Hash Tables & Symbol Table Applications Stanford ML 11 Application Example Photo OCR Stanford ML 10 Large Scale Machine Learning Stanford ML 9 Anomaly Detection and Recommender Systems Stanford ML 8 Clustering & Principal Component Analysis Princeton Algorithms P1W5 Balanced Search Trees TAMU Neural Network 2 Learning Processes TAMU Neural Network 1 Introduction Stanford ML 7 Support Vector Machine Stanford ML 6 Evaluate Algorithms Princeton Algorithms P1W4 Priority Queues and Symbol Tables Stanford ML 5 Neural Networks Learning Princeton Algorithms P1W3 Mergesort and Quicksort Stanford ML 4 Neural Networks Basics Princeton Algorithms P1W2 Stack and Queue, Basic Sorts Stanford ML 3 Classification Problems Stanford ML 2 Multivariate Regression and Normal Equation Princeton Algorithms P1W1 Union and Find Stanford ML 1 Introduction and Parameter Learning

Binary Heap in Java

2016-11-01

Binary Heap

Good Articles

These two articles give pretty good demo about Binary heap.

In Java

import java.util.Arrays;
import java.util.NoSuchElementException;

class BinaryHeap {
    /** The number of children each node has **/
    private static final int d = 2;
    private int heapSize;
    private int[] heap;
 
    /** Constructor **/    
    public BinaryHeap(int capacity) {
        heapSize = 0;
        heap = new int[capacity];
        Arrays.fill(heap, -1);
    }
 
    /** Function to check if heap is empty **/
    public boolean isEmpty() {
        return heapSize == 0;
    }
 
    /** Check if heap is full **/
    public boolean isFull(){
        return heapSize == heap.length;
    }
 
    /** Clear heap */
    public void makeEmpty() {
        heapSize = 0;
    }
 
    /** Function to  get index parent of i **/
    private int parent(int i) {
        return (i - 1)/d;
    }
 
    /** Function to get index of k th child of i **/
    private int kthChild(int i, int k) {
        return d * i + k;
    }
 
    /** Function to insert element */
    public void insert(int x) {
        if (isFull())
            throw new NoSuchElementException("Overflow Exception");
        /** Percolate up **/
        heap[heapSize++] = x;
        heapifyUp(heapSize - 1);
    }
 
    /** Function to find least element **/
    public int findMin() {
        if (isEmpty())
            throw new NoSuchElementException("Underflow Exception");           
        return heap[0];
    }
 
    /** Function to delete min element **/
    public int deleteMin() {
        int keyItem = heap[0];
        delete(0);
        return keyItem;
    }
 
    /** Function to delete element at an index **/
    public int delete(int ind) {
        if (isEmpty() )
            throw new NoSuchElementException("Underflow Exception");
        int keyItem = heap[ind];
        heap[ind] = heap[heapSize - 1];
        heapSize--;
        heapifyDown(ind);        
        return keyItem;
    }
 
    /** Function heapifyUp  **/
    private void heapifyUp(int childInd) {
        int tmp = heap[childInd];    
        while (childInd > 0 && tmp < heap[parent(childInd)])
        {
            heap[childInd] = heap[ parent(childInd) ];
            childInd = parent(childInd);
        }                   
        heap[childInd] = tmp;
    }
 
    /** Function heapifyDown **/
    private void heapifyDown(int ind) {
        int child;
        int tmp = heap[ ind ];
        while (kthChild(ind, 1) < heapSize)
        {
            child = minChild(ind);
            if (heap[child] < tmp)
                heap[ind] = heap[child];
            else
                break;
            ind = child;
        }
        heap[ind] = tmp;
    }
 
    /** Function to get smallest child **/
    private int minChild(int ind) {
        int bestChild = kthChild(ind, 1);
        int k = 2;
        int pos = kthChild(ind, k);
        while ((k <= d) && (pos < heapSize)) 
        {
            if (heap[pos] < heap[bestChild]) 
                bestChild = pos;
            pos = kthChild(ind, k++);
        }    
        return bestChild;
    }
 
    /** Function to print heap **/
    public void printHeap() {
        System.out.print("\nHeap = ");
        for (int i = 0; i < heapSize; i++)
            System.out.print(heap[i] +" ");
        System.out.println();
    }
    
    public static void main(String[] args) {
    	BinaryHeap heap = new BinaryHeap(10);
    	int[] arr = {35,33,42,10,14,19,27,44,26,31};
    	for(int i:arr){
    		heap.insert(i);
    		System.out.print(i + " ");
    	}
    	heap.printHeap(); // should be {10 14 19 26 31 42 27 44 35 33}
    	heap.deleteMin();
    	heap.printHeap(); // should be {14 26 19 33 31 42 27 44 35}
    }
}

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