priority Queue as min heap code example

Example 1: min heap priority queue c++

#include<queue>
std::priority_queue <int, std::vector<int>, std::greater<int> > minHeap;

Example 2: priority queue min heap

#include <bits/stdc++.h>
using namespace std;
 
// User defined class, Point
class Point
{
   int x;
   int y;
public:
   Point(int _x, int _y)
   {
      x = _x;
      y = _y;
   }
   int getX() const { return x; }
   int getY() const { return y; }
};
 
// To compare two points
class myComparator
{
public:
    int operator() (const Point& p1, const Point& p2)
    {
        return p1.getX() > p2.getX();
    }
};
 
// Driver code
int main ()
{
    // Creates a Min heap of points (order by x coordinate)
    priority_queue <Point, vector<Point>, myComparator > pq;
 
    // Insert points into the min heap
    pq.push(Point(10, 2));
    pq.push(Point(2, 1));
    pq.push(Point(1, 5));
 
    // One by one extract items from min heap
    while (pq.empty() == false)
    {
        Point p = pq.top();
        cout << "(" << p.getX() << ", " << p.getY() << ")";
        cout << endl;
        pq.pop();
    }
 
    return 0;
}

Example 3: priority queue max heap in java

//Sol1
PriorityQueue<Integer> queue = new PriorityQueue<>(10, Collections.reverseOrder());


//Sol2
// PriorityQueue<Integer> pq = new PriorityQueue<>((x, y) -> y - x);
PriorityQueue<Integer> pq =new PriorityQueue<>((x, y) -> Integer.compare(y, x));


//Sol3
PriorityQueue<Integer> pq = new PriorityQueue<Integer>(defaultSize, new Comparator<Integer>() {
    public int compare(Integer lhs, Integer rhs) {
        if (lhs < rhs) return +1;
        if (lhs.equals(rhs)) return 0;
        return -1;
    }
});

Example 4: Difference between Priority Queue and Heap

This website provides a really clear explanation. http://pages.cs.wisc.edu/~vernon/cs367/notes/11.PRIORITY-Q.html

In short, a priority queue can be implemented using many of the data structures that we've already studied (an array, a linked list, or a binary search tree). However, those data structures do not provide the most efficient operations. To make all of the operations very efficient, we'll use a new data structure called a heap.