数据流中的中位数
Find Median from Data Stream
题目详情
问题:数据流中的中位数
考察:数组、贪心
来源:Citadel
链接:https://www.jointaro.com/interviews/questions/find-median-from-data-stream/
The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values.
- For example, for
arr = [2,3,4], the median is3. - For example, for
arr = [2,3], the median is(2 + 3) / 2 = 2.5.
Implement the MedianFinder class:
MedianFinder()initializes theMedianFinderobject.void addNum(int num)adds the integernumfrom the data stream to the data structure.double findMedian()returns the median of all elements so far. Answers within10-5of the actual answer will be accepted.
Example 1:
Input ["MedianFinder", "addNum", "addNum", "findMedian", "addNum", "findMedian"] [[], [1], [2], [], [3], []] Output [null, null, null, 1.5, null, 2.0] Explanation MedianFinder medianFinder = new MedianFinder(); medianFinder.addNum(1); // arr = [1] medianFinder.addNum(2); // arr = [1, 2] medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2) medianFinder.addNum(3); // arr[1, 2, 3] medianFinder.findMedian(); // return 2.0
Constraints:
-105 <= num <= 105- There will be at least one element in the data structure before calling
findMedian. - At most
5 * 104calls will be made toaddNumandfindMedian.
Follow up:
- If all integer numbers from the stream are in the range
[0, 100], how would you optimize your solution? - If
99%of all integer numbers from the stream are in the range[0, 100], how would you optimize your solution?
解析
思路:维护一个大根堆保存较小的一半、一个小根堆保存较大的一半,并保持两个堆大小差不超过 1。中位数由堆顶或两个堆顶平均得到。
复杂度:插入 O(log n),查询中位数 O(1),空间 O(n)。