"priority queue min heap" Code Answer's
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min heap in c++
priority_queue <int, vector<int>, greater<int>> minHeap;
min heap priority queue c++
#include<queue>
std::priority_queue <int, std::vector<int>, std::greater<int> > minHeap;
create a min heap in java using priority queue
int arr[]={1,2,1,3,3,5,7};
PriorityQueue<Integer> a=new PriorityQueue<>();
for(int i:arr){
a.add(i);
}
while(!a.isEmpty())
System.out.println(a.poll());
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;
}
});
Source: stackoverflow.com
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;
}
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.
Source: softwareengineering.stackexchange.com
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