本文主要是介绍排序算法,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
快排
import random
from typing import List
class QuickSortMethod:
def quick_sort(self, nums):
self.quick_sort_help(nums, 0, len(nums) - 1)
return nums
def quick_sort_help(self, nums, left, right):
if left >= right:
return nums
if left < right:
idx = random.randint(left, right)
pivot = nums[idx]
nums[idx], nums[left] = nums[left], nums[idx]
i = left
j = right
while i < j:
while i < j and nums[j] > pivot:
j -= 1
nums[i] = nums[j]
while i < j and nums[i] <= pivot:
i += 1
nums[j] = nums[i]
nums[i] = pivot
self.quick_sort_help(nums, left, i - 1)
self.quick_sort_help(nums, i + 1, right)
return nums
归并排序
class MergeSolution:
def merge_sort(self, nums, l, r):
if l == r:
return
mid = (l + r) // 2
self.merge_sort(nums, l, mid)
self.merge_sort(nums, mid + 1, r)
tmp = []
i, j = l, mid + 1
while i <= mid or j <= r:
if i > mid or (j <= r and nums[j] < nums[i]):
tmp.append(nums[j])
j += 1
else:
tmp.append(nums[i])
i += 1
nums[l: r + 1] = tmp
def sortArray(self, nums: List[int]) -> List[int]:
self.merge_sort(nums, 0, len(nums) - 1)
return nums
堆排序
class HeapSort:
def sortArray(self, nums):
self.heap_sort(nums)
return nums
def heap_sort(self, nums):
self.heap_init(nums)
for i in range(len(nums) - 1, -1, -1):
nums[i], nums[0] = nums[0], nums[i]
self.adjust_heap_down(nums, i - 1, 0)
def heap_init(self, nums):
n = len(nums) - 1
for i in range(n, -1, -1):
self.adjust_heap_down(nums, n, i)
def adjust_heap_down(self, nums, heap_size, idx):
left = 2 * idx + 1
while left <= heap_size:
child_idx = left
right = left + 1
if right <= heap_size and nums[right] > nums[left]:
child_idx = right
if nums[child_idx] <= nums[idx]:
break
nums[idx], nums[child_idx] = nums[child_idx], nums[idx]
idx = child_idx
left = 2 * idx + 1
from collections import Counter
class Solution2:
def topKFrequent(self, nums: List[int], k: int) -> List[int]:
def site_up(arr, idx):
parent = (idx - 1) // 2
while parent >= 0:
if arr[parent][1] > arr[idx][1]:
arr[idx], arr[parent] = arr[parent], arr[idx]
idx = parent
parent = (idx - 1) // 2
else:
break
def site_down(arr, idx, size):
left_child = idx * 2 + 1
while left_child <= size:
child = left_child
right_child = left_child + 1
if right_child <= size and arr[right_child][1] < arr[left_child][1]:
child = right_child
if arr[child][1] < arr[idx][1]:
arr[idx], arr[child] = arr[child], arr[idx]
idx = child
left_child = 2 * idx + 1
else:
break
stat = Counter(nums)
stat = list(stat.items())
heap = []
for i in range(k):
heap.append(stat[i])
site_up(arr=heap, idx=len(heap) - 1)
for i in range(k, len(stat)):
if stat[i][1] > heap[0][1]:
heap[0] = stat[i]
site_down(heap, 0, k - 1)
return [item[0] for item in heap[:k]]
class Solution3:
def frequencySort(self, s: str) -> str:
c = Counter(s)
res = dict(sorted(c.items(), key=lambda x: -x[1]))
return ''.join(k * v for k, v in res.items())
import collections
import heapq
class Solution4:
def isPossible(self, nums: List[int]) -> bool:
mp = collections.defaultdict(list)
print(mp)
for x in nums:
print("mp:", mp.items())
if mp.get(x - 1):
queue = mp.get(x - 1)
prevLength = heapq.heappop(queue)
heapq.heappush(mp[x], prevLength + 1)
else:
heapq.heappush(mp[x], 1)
return not any(queue and queue[0] < 3 for queue in mp.values())
if __name__ == "__main__":
s = Solution4()
# nums = [5, -3, 9, 1, 7, 7, 9, 10, 2, 2, 10, 10, 3, -1, 3, 7, -9, -1, 3, 3]
# k = 3
s.isPossible([1, 2, 3, 3, 4, 4, 5, 5])
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