给定一个数组arr, 和一个数num, 请把小于num的数放在数组的
左边, 等于num的数放在数组的中间, 大于num的数放在数组的
右边。
要求额外空间复杂度O(1), 时间复杂度O(N)
def Nether_land_flag(arr,num):
n = len(arr)
less = -1
more = n
cur = 0
while cur < more:
if arr[cur] num:
# more, cur不用++
more -= 1
arr[more], arr[cur] = arr[cur], arr[more]
else:
cur += 1
return arr
arr = [1,3,6,2,4,4,5]
num = 4
Nether_land_flag(arr,num)
[1, 3, 2, 4, 4, 5, 6]
快速排序
经典快排
def quick_sort(nums,first,last):
if first >= last:
return
mid_value = nums[first]
high = last
low = first
while low < high:
while low = mid_value:
high -= 1
nums[low] = nums[high]
while low < high and nums[low] < mid_value:
low += 1
nums[high] = nums[low]
nums[low] = mid_value
quick_sort(nums,first,low)
quick_sort(nums,low+1,last)
arr = [1,3,6,2,4,4,5]
quick_sort(arr,0,len(arr)-1)
arr
[1, 2, 3, 4, 4, 5, 6]
改进快排(相等的值不用再次进行排序,加上随机选取需要排序的值)
import random
def Nether_land_flag(arr,first,last):
less = first - 1
more = last
cur = first
random_index = random.randint(first,last)
arr[random_index], arr[last] = arr[last], arr[random_index]
num = arr[last]
while cur < more:
if arr[cur] num:
# more, cur不用++
more -= 1
arr[more], arr[cur] = arr[cur], arr[more]
else:
cur += 1
arr[last], arr[more] = arr[more], arr[last]
# 返回 小于num 的第一个下标,和大于num的第一个下标
return less + 1, more
def re_quick_sort(nums,first,last):
if first < last:
p = Nether_land_flag(nums,first,last)
re_quick_sort(nums,first,p[0]-1)
re_quick_sort(nums,p[1]+1,last)
nums = [1,3,2,4,5,7,8,4,3,4]
last = len(nums) - 1
re_quick_sort(nums,0,last)
nums
[1, 2, 3, 3, 4, 4, 4, 5, 7, 8]
堆排序
i 的左节点 2i+1 右节点 2i+2 父节点 (i-1)//2
heapify 自顶向下 一个值变小 下沉操作(大根堆)(本身是大根堆)
def heapify(arr,i,heapsize):
# 堆的大小个数
left = 2 * i + 1
while left < heapsize:
max_index = i
right = left + 1
if left < heapsize and arr[max_index] < arr[left]:
max_index = left
if right < heapsize and arr[max_index] < arr[right]:
max_index = right
if i != max_index:
arr[i], arr[max_index] = arr[max_index], arr[i]
i = max_index
left = 2 * i + 1
else:
break
heap_insert 自底向上
# 底部插入一个数,如果是最大,则推至堆顶
def heap_insert(arr,i):
while arr[i] > arr[(i-1) // 2]:
arr[i], arr[(i-1) // 2] = arr[(i-1) // 2], arr[i]
i = (i-1) // 2
if i <= 0:
break
def heap_sort(arr):
n = len(arr)
if n 0:
heapify(arr,0,heap_size)
arr[0], arr[heap_size-1] = arr[heap_size-1], arr[0]
heap_size -= 1
nums = [3,4,5,6,7,8,9,9,15]
n = len(nums)
# for i in range(0,n):
# heap_insert(nums,i)
heap_sort(nums)
nums
[3, 4, 5, 6, 7, 8, 9, 9, 15]
桶排序
给定一个数组, 求如果排序之后, 相邻两数的最大差值, 要求时
间复杂度O(N), 且要求不能用非基于比较的排序。
def gap(nums):
n = len(nums)
if n <= 1:
return 0
for i in range(n):
if i == 0:
max_num = nums[i]
min_num = nums[i]
max_num = max(nums[i], max_num)
min_num = min(nums[i], min_num)
if max_num == min_num:
return 0
hash_num = [False for i in range(n+1)]
mins = [None for i in range(n+1)]
maxs = [None for i in range(n+1)]
for i in range(n):
bid = bucket(nums[i],n,min_num,max_num)
mins[bid] = min(mins[bid], nums[i]) if hash_num[bid] else nums[i]
maxs[bid] = max(maxs[bid], nums[i]) if hash_num[bid] else nums[i]
hash_num[bid] = True
print(hash_num)
print(mins)
print(maxs)
lastmax = maxs[0]
res = 0
for i in range(n+1):
if hash_num[i]:
res = max(res , mins[i] - lastmax)
lastmax = maxs[i]
return res
def bucket(num, length, min_num, max_num):
return int(length * (num - min_num)/(max_num - min_num))
nums = [5,10,2,7,8,8,9,10,7,8]
gap(nums)
[True, False, False, True, False, False, True, True, True, False, True]
[2, None, None, 5, None, None, 7, 8, 9, None, 10]
[2, None, None, 5, None, None, 7, 8, 9, None, 10]
3
栈结构
class stack():
def __init__(self,size=0):
self.stack = []
self.size = size
def stack_pop(self):
if self.stack != []:
return self.stack.pop()
else:
return False
def stack_peek(self):
if self.stack != []:
return self.stack[-1]
else:
return False
def stack_push(self,num):
if len(self.stack) < self.size:
self.stack.append(num)
else:
return False
队列
class Queue():
def __init__(self,size=0):
self.queue = []
self.size = size
def queue_pop(self):
if self.queue != []:
return self.queue.pop(0)
else:
return False
def queue_push(self,num):
if len(self.queue) < self.size:
self.queue.append(num)
else:
return False
实现一个特殊的栈,在实现栈的基本功能的基础上,再实现返回栈中最小元素的操作。
【要求】
1.pop、push、getMin操作的时间复杂度都是0(1)。
2.设计的栈类型可以使用现成的栈结构。
class stack():
def __init__(self,size=0):
self.stack = []
self.size = size
self.min_list = []
def stack_pop(self):
if self.stack != []:
self.min_list.pop()
return self.stack.pop()
else:
return False
def stack_peek(self):
if self.stack != []:
return self.stack[-1]
else:
return False
def stack_push(self,num):
if len(self.stack) < self.size:
if self.stack == []:
self.stack.append(num)
self.min_list.append(num)
else:
self.stack.append(num)
if num < self.min_list[-1]:
self.min_list.append(num)
else:
self.min_list.append(self.min_list[-1])
else:
return False
def get_min(self):
if self.min_list != []:
return self.stack[-1]
else:
return False
如何仅用队列结构实现栈结构?
如何仅用栈结构实现队列结构?
class Queue_Stack():
# queue -> stack
def __init__(self):
self.data = []
self.helper = []
def re_push(self,num):
self.data.append(num)
def re_pop(self):
if not self.data:
return False
while self.data != []:
tmp = self.data.pop(0)
if self.data == []:
break
self.helper.append(tmp)
self.data, self.helper = self.helper, self.data
return tmp
class Stack_Queue():
# stack -> queue
def __init__(self):
self.push_queue = []
self.pop_queue = []
def re_push(self,num):
self.push_queue.append(num)
def re_pop(self):
if self.pop_queue == []:
if self.push_queue == []:
return False
else:
while self.push_queue:
self.pop_queue.append(self.push_queue.pop())
return self.pop_queue.pop()
禾-Ming
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