973. K Closest Points to Origin
Problem Description​
Given an array of points where points[i] = [xi, yi] represents a point on the X-Y plane and an integer k, return the k closest points to the origin (0, 0).
The distance between two points on the X-Y plane is the Euclidean distance (i.e., √(x1 - x2)2 + (y1 - y2)2).
You may return the answer in any order. The answer is guaranteed to be unique (except for the order that it is in).
Examples​
Example 1:
Input: points = [[1,3],[-2,2]], k = 1
Output: [[-2,2]]
Explanation:
The distance between (1, 3) and the origin is sqrt(10).
The distance between (-2, 2) and the origin is sqrt(8).
Since sqrt(8) < sqrt(10), (-2, 2) is closer to the origin.
We only want the closest k = 1 points from the origin, so the answer is just [[-2,2]].
Example 2:
Input: points = [[3,3],[5,-1],[-2,4]], k = 2
Output: [[3,3],[-2,4]]
Explanation: The answer [[-2,4],[3,3]] would also be accepted.
Constraints​
1 <= k <= points.length <= 10^4
-10^4 <= xi, yi <= 10^4
Solution for973. K Closest Points to Origin​
- Solution
Implementation​
Live Editor
function Solution(arr) { function kClosest(points, k) { // Calculate the distance of each point from the origin points.sort((a, b) => { const distA = a[0] * a[0] + a[1] * a[1]; const distB = b[0] * b[0] + b[1] * b[1]; return distA - distB; }); // Return the first k points return points.slice(0, k); } const input = points = [[1,3],[-2,2]], k = 1 const output = kClosest(input , k) return ( <div> <p> <b>Input: </b> {JSON.stringify(input)} </p> <p> <b>Output:</b> {output.toString()} </p> </div> ); }
Result
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Complexity Analysis​
- Time Complexity:
- Space Complexity:
Code in Different Languages​
- JavaScript
- TypeScript
- Python
- Java
- C++
function kClosest(points, k) {
const pq = new MinPriorityQueue({ priority: (point) => point[0] });
for (let i = 0; i < points.length; i++) {
const x = points[i][0];
const y = points[i][1];
const dist = x * x + y * y;
pq.enqueue([dist, i]);
}
const ans = [];
for (let i = 0; i < k; i++) {
const [_, index] = pq.dequeue().element;
ans.push(points[index]);
}
return ans;
}
class Solution {
kClosest(points: number[][], k: number): number[][] {
let pq = new MinPriorityQueue<{ dist: number, index: number }>({
priority: (point) => point.dist
});
for (let i = 0; i < points.length; i++) {
let x = points[i][0];
let y = points[i][1];
let z = x * x + y * y;
pq.enqueue({ dist: z, index: i });
}
let ans: number[][] = [];
while (k--) {
let { index } = pq.dequeue().element;
ans.push(points[index]);
}
return ans;
}
}
import heapq
class Solution:
def kClosest(self, points, k):
pq = []
for i, (x, y) in enumerate(points):
dist = x * x + y * y
heapq.heappush(pq, (dist, i))
ans = []
for _ in range(k):
_, i = heapq.heappop(pq)
ans.append(points[i])
return ans
import java.util.PriorityQueue;
class Solution {
public int[][] kClosest(int[][] points, int k) {
PriorityQueue<int[]> pq = new PriorityQueue<>((a, b) -> a[0] - b[0]);
for (int i = 0; i < points.length; i++) {
int x = points[i][0];
int y = points[i][1];
int z = x * x + y * y;
pq.offer(new int[] { z, i });
}
int[][] ans = new int[k][2];
for (int i = 0; i < k; i++) {
int index = pq.poll()[1];
ans[i] = points[index];
}
return ans;
}
}
class Solution {
public:
vector<vector<int>> kClosest(vector<vector<int>>& points, int k) {
vector<vector<int>>ans;
priority_queue<pair<int,int> , vector<pair<int,int>> , greater<pair<int,int>>>pq;
for(int i=0;i<points.size();i++)
{
int x=points[i][0];
int y=points[i][1];
int z = x*x + y*y;
pq.push({z , i});
}
while(k--)
{
int i = pq.top().second;
pq.pop();
ans.push_back(points[i]);
}
return ans;
}
};
References​
-
LeetCode Problem: 2348. Number of Zero-Filled Subarrays
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Solution Link: LeetCode Solution