Problem Number 1300
Crossing the 1300 mark with a problem for which I was stuck in 2020. I was trying it before using sorted native data structures but it was too slow. This time I switched to my own Priority Queue implementation, and it barely worked. Code is down below, cheers, ACC.
K-th Smallest Prime Fraction - LeetCode
You are given a sorted integer array arr containing 1 and prime numbers, where all the integers of arr are unique. You are also given an integer k.
For every i and j where 0 <= i < j < arr.length, we consider the fraction arr[i] / arr[j].
Return the kth smallest fraction considered. Return your answer as an array of integers of size 2, where answer[0] == arr[i] and answer[1] == arr[j].
Example 1:
Input: arr = [1,2,3,5], k = 3 Output: [2,5] Explanation: The fractions to be considered in sorted order are: 1/5, 1/3, 2/5, 1/2, 3/5, and 2/3. The third fraction is 2/5.
Example 2:
Input: arr = [1,7], k = 1 Output: [1,7]
Constraints:
2 <= arr.length <= 10001 <= arr[i] <= 3 * 104arr[0] == 1arr[i]is a prime number fori > 0.- All the numbers of
arrare unique and sorted in strictly increasing order. 1 <= k <= arr.length * (arr.length - 1) / 2
Follow up: Can you solve the problem with better than
O(n2) complexity?public class Solution {
public class PQueuePrimeFraction
{
public int num = 0;
public int den = 0;
public PQueuePrimeFraction(int num, int den)
{
this.num = num;
this.den = den;
}
}
public int[] KthSmallestPrimeFraction(int[] arr, int k)
{
PriorityQueue pQueue = new PriorityQueue(true);
for (int i = 0; i < arr.Length; i++)
{
for (int j = i + 1; j < arr.Length; j++)
{
double w = (1.0 * arr[i]) / arr[j];
pQueue.Enqueue(new PQueuePrimeFraction(arr[i], arr[j]), w);
}
}
while (pQueue.Count > 0)
{
double w = 0;
PQueuePrimeFraction current = (PQueuePrimeFraction)pQueue.Dequeue(out w);
k--;
if (k == 0)
{
return new int[] { current.num, current.den };
}
}
return new int[] { -1, -1 };
}
}
public class PriorityQueue
{
public struct HeapEntry
{
private object item;
private double priority;
public HeapEntry(object item, double priority)
{
this.item = item;
this.priority = priority;
}
public object Item
{
get
{
return item;
}
}
public double Priority
{
get
{
return priority;
}
}
}
private bool ascend;
private int count;
private int capacity;
private HeapEntry[] heap;
public int Count
{
get
{
return this.count;
}
}
public PriorityQueue(bool ascend, int cap = -1)
{
capacity = 10000000;
if (cap > 0) capacity = cap;
heap = new HeapEntry[capacity];
this.ascend = ascend;
}
public object Dequeue(out double priority)
{
priority = heap[0].Priority;
object result = heap[0].Item;
count--;
trickleDown(0, heap[count]);
return result;
}
public object Peak(out double priority)
{
priority = heap[0].Priority;
object result = heap[0].Item;
return result;
}
public object Peak(/*out double priority*/)
{
//priority = heap[0].Priority;
object result = heap[0].Item;
return result;
}
public void Enqueue(object item, double priority)
{
count++;
bubbleUp(count - 1, new HeapEntry(item, priority));
}
private void bubbleUp(int index, HeapEntry he)
{
int parent = (index - 1) / 2;
// note: (index > 0) means there is a parent
if (this.ascend)
{
while ((index > 0) && (heap[parent].Priority > he.Priority))
{
heap[index] = heap[parent];
index = parent;
parent = (index - 1) / 2;
}
heap[index] = he;
}
else
{
while ((index > 0) && (heap[parent].Priority < he.Priority))
{
heap[index] = heap[parent];
index = parent;
parent = (index - 1) / 2;
}
heap[index] = he;
}
}
private void trickleDown(int index, HeapEntry he)
{
int child = (index * 2) + 1;
while (child < count)
{
if (this.ascend)
{
if (((child + 1) < count) &&
(heap[child].Priority > heap[child + 1].Priority))
{
child++;
}
}
else
{
if (((child + 1) < count) &&
(heap[child].Priority < heap[child + 1].Priority))
{
child++;
}
}
heap[index] = heap[child];
index = child;
child = (index * 2) + 1;
}
bubbleUp(index, he);
}
}
Comments
Post a Comment