輸入n個整數,找出其中最小的K個數。例如輸入4,5,1,6,2,7,3,8這8個數字,則最小的4個數字是1,2,3,4
最大堆的查找時間複雜度爲O(logK)
替換的複雜度也爲O(logK)
輔助數組的空間複雜度爲O(K)
如果換爲用數組解決該問題,那麼
查找的時間複雜度爲O(logK)(採用折半查找)
替換的複雜度爲O(K)
輔助數組的空間複雜度爲O(K)
兩種方案的主要區別在於替換的複雜度,因此採用最大堆解決該問題。遇到類似的情況時,最小堆也有同樣的優勢。
# -*-coding:utf-8 -*-
class Solution:
def GetLeastNumbers_Solutions(self, tinput, k):
# 創建或者是插入最大堆
def createMaxHeap(num):
maxHeap.append(num)
currentIndex = len(maxHeap) - 1
while currentIndex != 0:
parentIndex = (currentIndex - 1) >> 1
if maxHeap[parentIndex] < maxHeap[currentIndex]:
maxHeap[parentIndex], maxHeap[currentIndex] = maxHeap[currentIndex], maxHeap[parentIndex]
else:
break
# 調整最大堆,頭節點發生改變
def adjustMaxHeap(num):
if num < maxHeap[0]:
maxHeap[0] = num
maxHeapLen = len(maxHeap)
index = 0
while index < maxHeapLen:
leftIndex = index * 2 + 1
rightIndex = index * 2 + 2
largerIndex = 0
if rightIndex < maxHeapLen:
if maxHeap[rightIndex] < maxHeap[leftIndex]:
largerIndex = leftIndex
else:
largerIndex = rightIndex
elif leftIndex < maxHeapLen:
largerIndex = leftIndex
else:
break
if maxHeap[index] < maxHeap[largerIndex]:
maxHeap[index], maxHeap[largerIndex] = maxHeap[largerIndex], maxHeap[index]
index = largerIndex
maxHeap = []
inputLen = len(tinput)
if len(tinput) < k or k <= 0:
return []
for i in range(inputLen):
if i < k:
createMaxHeap(tinput[i])
else:
adjustMaxHeap(tinput[i])
maxHeap.sort()
return maxHeap
if __name__ == '__main__':
tinput=[1,2,4,6,100,20,9,10]
s=Solution()
result = s.GetLeastNumbers_Solutions(tinput,4)
for i in range(0,4):
print(result[i])
運行結果爲:
1
2
4
6