方法一
class MedianFinder:
def __init__(self):
from sortedcontainers import SortedList
self.right = 0
self.arr = SortedList()
def addNum(self, num: int) -> None:
self.arr.add(num)
self.right += 1
def findMedian(self) -> float:
if self.right % 2 == 0 :
return self.arr[(self.right - 1) // 2] / 2 + self.arr[(self.right + 1) // 2] /2
else :
return self.arr[self.right // 2]
方法二
from heapq import *
class MedianFinder(object):
# 维护两个堆,一个大顶堆,一个小顶堆,小顶堆里的数比大顶堆里的数都要大,
# 如果有两个潜在的中位数(两个堆size相同),数据流的中位数就是两个堆顶之和除以2
# 如果只有一个中位数,就看size更小的那个堆的堆顶
# 新进来的数都丢进小顶堆,然后把小顶堆的堆顶丢到大顶堆,
# 调整两个堆,使得size 差最大为1
def __init__(self):
"""
initialize your data structure here.
"""
self.max_h = list()
self.min_h = list()
heapify(self.max_h)
heapify(self.min_h)
def addNum(self, num):
"""
:type num: int
:rtype: None
"""
heappush(self.min_h, num)
heappush(self.max_h, -heappop(self.min_h))
if len(self.max_h) > len(self.min_h):
heappush(self.min_h, -heappop(self.max_h))
def findMedian(self):
"""
:rtype: float
"""
max_len = len(self.max_h)
min_len = len(self.min_h)
if max_len == min_len: #有两个候选中位数
return (self.min_h[0] + -self.max_h[0]) / 2.
else:#小顶堆的size 一定 >= 大顶堆的size,所以答案就是小顶堆的堆顶
return self.min_h[0] / 1.
# Your MedianFinder object will be instantiated and called as such:
# obj = MedianFinder()
# obj.addNum(num)
# param_2 = obj.findMedian()