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70 lines
2.0 KiB
Plaintext
70 lines
2.0 KiB
Plaintext
---
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title: '973. K Closest Points to Origin'
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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)
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sidebar:
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label: 'K Closest Points to Origin'
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badge: 'Medium'
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---
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<Badge variant="accent">Array</Badge>
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### Example 1:
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- Input: `points = [[1,3],[-2,2]], k = 1`
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- Output: `[[-2,2]]`
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- 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]].
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### Example 2:
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- Input: `points = [[3,3],[5,-1],[-2,4]], k = 2`
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- Output: `[[3,3],[-2,4]]`
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- Explanation: The answer [[-2,4],[3,3]] would also be accepted.
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### Constraints:
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- `1 <= k <= points.length <= 10^4`
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- `-10^4 <= xi, yi <= 10^4`
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## Approach
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```mermaid
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flowchart TD
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S(["kClosest(points, k)"]) --> I["minHeap = []"]
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I --> F{"more x, y in points?"}
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F -- yes --> D["dist = x**2 + y**2 — squared distance orders the same as sqrt"]
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D --> A["minHeap.append([dist, x, y])"]
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A --> F
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F -- no --> H["heapq.heapify(minHeap), res = [] — O(n) build, smallest dist on top"]
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H --> W{"k > 0?"}
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W -- yes --> P["dist, x, y = heapq.heappop(minHeap)"]
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P --> R["res.append([x, y]), k -= 1"]
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R --> W
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W -- no --> E(["return res"])
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```
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## Solution
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```py
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import heapq
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class Solution:
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def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
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minHeap = []
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for x, y in points:
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dist = (x**2) + (y**2)
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minHeap.append([dist, x, y])
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heapq.heapify(minHeap)
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res = []
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while k > 0:
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dist, x, y = heapq.heappop(minHeap)
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res.append([x, y])
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k -= 1
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return res
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```
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## Explanation
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<YouTube id="rI2EBUEMfTk" title="K Closest Points to Origin - Heap / Priority Queue - Leetcode 973 - Python" />
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