public class Entry  { K key; // the key V value; // the value int index; // position of the element in the array Entry associate; // reference to the associate element in the other heap /** * Returns the key stored in this entry. * @return the entry's key */ K getKey() { return key; } /* getKey */ /** * Returns the value stored in this entry. * @return the entry's value */ V getValue() { return value; } /* getValue */ public Entry( K k, V v ) { key = k; value = v; } public void setIndex(int i) { index= i; } public int getIndex() { return index; } public void setAssociate(Entry e) { associate= e; } public Entry getAssociate() { return associate; } } 

public class HeapPriorityQueue implements PriorityQueue {

private Entry [] storage; //The Heap itself in array form private int tail; //Index of last element in the heap public HeapPriorityQueue() {this(100);} public HeapPriorityQueue( int size ) { storage = new Entry [ size ]; tail = -1;} public int size() {return tail + 1;} public Entry insert( K key, V value) throws IllegalArgumentException { if( tail == storage.length - 1 ) throw new IllegalArgumentException ( "Heap Overflow" );

Entry e = new Entry ( key, value ); storage [ ++tail ] = e; e.setIndex(tail); upHeap ( tail ); return e; public Entry min() { if( isEmpty() ) return null; return storage [ 0 ]; } public Entry removeMin() { if( isEmpty() ) return null;

Entry ret = storage [ 0 ];

if( tail == 0 ) { tail = -1; storage [ 0 ] = null; return ret; } private void upHeap( int location ) { if( location == 0 ) return;

int parent = parent ( location );

if( storage [ parent ].key.compareTo ( storage [ location ].key ) > 0 ) { swap ( location, parent ); upHeap ( parent ); } } private void downHeap( int location ) { int left = (location * 2) + 1; int right = (location * 2) + 2;

//Both children null or out of bound if( left > tail ) return;

//left in right out; if( left == tail ) { if( storage [ location ].key.compareTo ( storage [ left ].key ) > 0 ) swap ( location, left ); return; }

int toSwap = (storage [ left ].key.compareTo ( storage [ right ].key )

if( storage [ location ].key.compareTo ( storage [ toSwap ].key ) > 0 ) { swap ( location, toSwap ); downHeap ( toSwap ); } } private int parent( int location ) { return (location - 1) / 2; } private void swap( int location1, int location2 ) { Entry temp = storage [ location1 ]; storage [ location1 ] = storage [ location2 ]; storage [ location2 ] = temp; storage[location1].index= location1; storage[location2].index= location2; } public void print() { for (Entry e : storage) System.out.println ( "(" + e.key.toString() + "," + e.value.toString() + ":" + e.index + "), " ); } }

public class HeapPriorityQueueTest {

static String alpha = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; public static void main( String [] args ) {

Random rng = new Random(12345); // do not change the seed

// A min-heap HeapPriorityQueue pq = new HeapPriorityQueue ( 500 ); // Insert elements in heap for( int i = 0; i e= pq.insert ( rng.nextInt ( 10000 ), alpha.charAt ( rng.nextInt ( 52 ) )); }

System.out.println();System.out.println();

// removeMin for( int i = 0; i e = pq.removeMin(); System.out.print ( "(" + e.key.toString() + "," + e.value.toString() + ":" + e.index + "), " ); }

System.out.println();System.out.println(); // removeMax /* for( int i = 0; i e = pq.removeMax(); System.out.print ( "(" + e.key.toString() + "," + e.value.toString() + ":" + e.index + "), " ); } */

System.out.println();System.out.println(); // removeMin for( int i = 0; i e = pq.removeMin(); System.out.print ( "(" + e.key.toString() + "," + e.value.toString() + ":" + e.index + "), " ); }

System.out.println();System.out.println(); // removeMax /* for( int i = 0; i e = pq.removeMax(); System.out.print ( "(" + e.key.toString() + "," + e.value.toString() + ":" + e.index + "), " ); } */ } }

public interface PriorityQueue  {

int size();

boolean isEmpty();

Entry insert( K key, V value ) throws IllegalArgumentException;

Entry min();

Entry removeMin();

Entrymax();

EntryremoveMax();

}

The objective of this programming assignment is to implement a double-ended priority queue; this is a priority queue for which you can obtain both the max and the min element. This double-ended queue will be implemented using two heaps: a min-heap and a max-heap. Half of the elements are stored in one of the heaps and the other half is stored in the second heap. Each element a of the min-heap is associated with one element of the max-heap b with a