插入排序
每次将一个待排序的记录，按其关键字大小插入到前面已经排好序的子序列中，直到全部记录插入完成
- 直接插入排序
边比较边移动元素直到找到待插入元素的位置，最后插入
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
比较次数：O(n)~O(n^2)
def insert_sort(a):
n = len(a)
if n <= 1:
return
for i in range(1, n):
key = a[i]
j = i -1
while j > -1 and key < a[j]:
a[j+1] = a[j]
j -= 1
a[j+1] = key
pass
- 折半插入排序
将比较和移动操作分离开，先折半查找出待插入元素的位置，再统一移动待插入位置之后的所有元素
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
比较次数：O(nlogn)
def binary_insert_sort(a):
n = len(a)
if n <= 1:
return
for i in range(1, n):
key = a[i]
low, high = 0, i - 1
while low <= high:
mid = (low + high) // 2
if key < a[mid]:
high = mid - 1
else:
low = mid + 1
for j in range(i-1, high, -1):
a[j+1] = a[j]
a[high+1] = key
pass
交换排序
根据两个元素关键字的比较结果来交换两个元素在序列中的位置
- 冒泡排序
每趟冒泡都会使一个元素被移动到最终位置
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
def bubble_sort(a):
n = len(a)
if n <= 1:
return
flag = False
for i in range(n-1):
flag = False
for j in range(n-1, i, -1):
if a[j] < a[j-1]:
Sort.__swap(a, j-1, j)
flag = True
if flag == False: #如果一趟冒泡过程没有发生一次交换，则列表已经有序
break
pass
- 快速排序
基于分治的思想，每次划分都有一个元素被移动到最终位置
时间复杂度：平均O(nlogn) 最坏O(n^2)
空间复杂度：O(1)
不稳定
def quick_sort(a):
n = len(a)
if n <= 1:
return
Sort.__quickSort(a, 0, n-1, partition = Sort.__partitionRandom)
pass
def __quickSort(a, low, high, partition):
if low < high:
pos = partition(a, low, high)
Sort.__quickSort(a, low, pos-1, partition)
Sort.__quickSort(a, pos+1, high, partition)
pass
def __partition(a, low, high):
"""
以列表第一个元素为基准划分
"""
key = a[low]
while low < high:
while low < high and a[high] >= key:
high -= 1
a[low] = a[high]
while low < high and a[low] <= key:
low += 1
a[high] = a[low]
a[low] = key
return low
pass
def __partitionRandom(a, low, high):
"""
随机划分
"""
k = random.randint(low, high)
if k != low:
Sort.__swap(a, k, low)
return Sort.__partition(a, low, high)
pass
选择排序
选择待排序列中最小或最大的元素作为有序子序列的尾元素，直到待排序列为一个元素
- 简单选择排序
时间复杂度：O(n^2)
空间复杂度：O(1)
不稳定
def select_sort(a):
n = len(a)
if n <= 1:
return
for i in range(n-1):
min = i
for j in range(i, n):
if a[j] < a[min]:
min = j
if min != i:
Sort.__swap(a, i, min)
pass
- 堆排序
以升序排序为例
a.建立大根堆
b.输出堆顶元素，即交换堆底元素与堆顶元素
c.将剩余元素调整为大根堆
时间复杂度：O(nlogn)
空间复杂度：O(1)
不稳定
def heap_sort(a):
n = len(a)
if n < 1:
return
Sort.__buildMaxHeap(a, n) #建立大根堆
for i in range(n-1, 0, -1):
Sort.__swap(a, 0, i) #将堆顶元素与堆底元素交换
Sort.__adjustDown(a, 0, i) #将数组前i-1个元素调整为大根堆
pass
def __buildMaxHeap(a, n):
#自下往上逐渐调整为大根堆
for i in range(n//2, -1, -1):
Sort.__adjustDown(a, i, n)
pass
def __adjustDown(a, k, n):
#将元素a[k]向下进行调整
left = 2 * k + 1
while left < n:
#父节点与最大的子节点比较，若小于则交换
max_child = left + 1 if left + 1 < n and a[left+1] > a[left] else left
if a[k] < a[max_child]:
Sort.__swap(a, k, max_child)
k = max_child
left = 2 * k + 1
else:
break
pass
归并排序
递归形式的归并排序是基于分治的思想
首先将待排序列分成若干子序列
然后递归地对子序列进行排序
最后将已排序子序列合并
- 二路归并排序
时间复杂度：O(nlogn)
空间复杂度：O(n)
稳定
def merge_sort(a):
n = len(a)
if n <= 1:
return
Sort.__mergeSort(a, 0, n-1)
pass
def __mergeSort(a, low, high):
if low < high:
mid = (low + high) // 2
Sort.__mergeSort(a, low, mid)
Sort.__mergeSort(a, mid+1, high)
Sort.__merge_other(a, low, mid, high)
pass
def __merge(a, low, mid, high):
"""
合并两个有序列表
"""
b = a[:]
i, j = low, mid+1
k = low
while i <= mid and j <= high:
if b[i] <= b[j]:
a[k] = b[i]
i += 1
else:
a[k] = b[j]
j += 1
k += 1
while i <= mid:
a[k] = b[i]
i += 1
k += 1
while j <= high:
a[k] = b[j]
j += 1
k += 1
pass
def __merge_other(a, low, mid, high):
"""
合并两个有序序列，另一种写法
"""
help = []
i, j = low, mid+1
while i <= mid and j <= high:
if a[i] <= a[j]:
help.append(a[i])
i += 1
else:
help.append(a[j])
j += 1
while i <= mid:
help.append(a[i])
i += 1
while j <= high:
help.append(a[j])
j += 1
for i in range(low, high+1):
a[i] = help.pop(0)
pass
完整的代码 github
sort.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
several sorting algorithms
"""
import random
class Sort(object):
def __init__(self):
pass
# 插入排序
def insert_sort(a):
"""
直接插入排序
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
比较次数：O(n)~O(n^2)
"""
n = len(a)
if n <= 1:
return
for i in range(1, n):
key = a[i]
j = i -1
while j > -1 and key < a[j]:
a[j+1] = a[j]
j -= 1
a[j+1] = key
pass
def binary_insert_sort(a):
"""
折半插入排序
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
比较次数：O(nlogn)
"""
n = len(a)
if n <= 1:
return
for i in range(1, n):
key = a[i]
low, high = 0, i - 1
while low <= high:
mid = (low + high) // 2
if key < a[mid]:
high = mid - 1
else:
low = mid + 1
for j in range(i-1, high, -1):
a[j+1] = a[j]
a[high+1] = key
pass
# 选择排序
def select_sort(a):
"""
简单选择排序
时间复杂度：O(n^2)
空间复杂度：O(1)
不稳定
"""
n = len(a)
if n <= 1:
return
for i in range(n-1):
min = i
for j in range(i, n):
if a[j] < a[min]:
min = j
if min != i:
Sort.__swap(a, i, min)
pass
def heap_sort(a):
"""
堆排序
时间复杂度：O(nlogn)
空间复杂度：O(1)
不稳定
"""
n = len(a)
if n < 1:
return
Sort.__buildMaxHeap(a, n) #建立大根堆
for i in range(n-1, 0, -1):
Sort.__swap(a, 0, i) #将堆顶元素与堆底元素交换
Sort.__adjustDown(a, 0, i) #将数组前i-1个元素调整为大根堆
pass
def __buildMaxHeap(a, n):
#自下往上逐渐调整为大根堆
for i in range(n//2, -1, -1):
Sort.__adjustDown(a, i, n)
pass
def __adjustDown(a, k, n):
#将元素a[k]向下进行调整
left = 2 * k + 1
while left < n:
#父节点与最大的子节点比较，若小于则交换
max_child = left + 1 if left + 1 < n and a[left+1] > a[left] else left
if a[k] < a[max_child]:
Sort.__swap(a, k, max_child)
k = max_child
left = 2 * k + 1
else:
break
pass
# 归并排序
def merge_sort(a):
"""
归并排序
时间复杂度：O(nlogn)
空间复杂度：O(n)
稳定
"""
n = len(a)
if n <= 1:
return
Sort.__mergeSort(a, 0, n-1)
pass
def __mergeSort(a, low, high):
if low < high:
mid = (low + high) // 2
Sort.__mergeSort(a, low, mid)
Sort.__mergeSort(a, mid+1, high)
Sort.__merge_other(a, low, mid, high)
pass
def __merge(a, low, mid, high):
"""
合并两个有序列表
"""
b = a[:]
i, j = low, mid+1
k = low
while i <= mid and j <= high:
if b[i] <= b[j]:
a[k] = b[i]
i += 1
else:
a[k] = b[j]
j += 1
k += 1
while i <= mid:
a[k] = b[i]
i += 1
k += 1
while j <= high:
a[k] = b[j]
j += 1
k += 1
pass
def __merge_other(a, low, mid, high):
"""
合并两个有序序列，另一种写法
"""
help = []
i, j = low, mid+1
while i <= mid and j <= high:
if a[i] <= a[j]:
help.append(a[i])
i += 1
else:
help.append(a[j])
j += 1
while i <= mid:
help.append(a[i])
i += 1
while j <= high:
help.append(a[j])
j += 1
for i in range(low, high+1):
a[i] = help.pop(0)
pass
# 交换排序
def bubble_sort(a):
"""
冒泡排序
时间复杂度：O(n^2)
空间复杂度：O(1)
稳定
"""
n = len(a)
if n <= 1:
return
flag = False
for i in range(n-1):
flag = False
for j in range(n-1, i, -1):
if a[j] < a[j-1]:
Sort.__swap(a, j-1, j)
flag = True
if flag == False: #如果一趟冒泡过程没有发生一次交换，则列表已经有序
break
pass
def quick_sort(a):
"""
快速排序
时间复杂度：平均O(nlogn) 最坏O(n^2)
空间复杂度：O(1)
不稳定
"""
n = len(a)
if n <= 1:
return
Sort.__quickSort(a, 0, n-1, partition = Sort.__partitionRandom)
pass
def __quickSort(a, low, high, partition):
if low < high:
pos = partition(a, low, high)
Sort.__quickSort(a, low, pos-1, partition)
Sort.__quickSort(a, pos+1, high, partition)
pass
def __partition(a, low, high):
"""
以列表第一个元素为基准划分
"""
key = a[low]
while low < high:
while low < high and a[high] >= key:
high -= 1
a[low] = a[high]
while low < high and a[low] <= key:
low += 1
a[high] = a[low]
a[low] = key
return low
pass
def __partitionRandom(a, low, high):
"""
随机划分
"""
k = random.randint(low, high)
if k != low:
Sort.__swap(a, k, low)
return Sort.__partition(a, low, high)
pass
def __swap(a, i, j):
tmp = a[i];
a[i] = a[j];
a[j] = tmp
pass
sort_test.py
from sort import Sort
import random
import operator
class SortTest(object):
"""
the test class of sorting algorithm
"""
def __init__(self):
pass
def gen_random_list(n, min = 0, max = 100):
"""
generate a random int list
"""
if min > max or n < 1:
return []
random_lsit = []
for i in range(n):
random_lsit.append(random.randint(min, max))
return random_lsit
pass
def test(fun_sort):
"""
测试排序函数
成功：true
失败：false，并打印出错序列
"""
print(fun_sort.__doc__)
for i in range(10):
a = SortTest.gen_random_list(10)
b = sorted(a)
c = a[:]
fun_sort(c) #排序
# print(a)
# print(b)
# print(c)
if not operator.eq(b, c):
#打印出错序列
print(a)
print(b)
print(c)
print('false')
break
if i == 9:
print('true')
pass
if __name__ == '__main__':
SortTest.test(fun_sort = Sort.insert_sort)
SortTest.test(fun_sort = Sort.binary_insert_sort)
SortTest.test(fun_sort = Sort.select_sort)
SortTest.test(fun_sort = Sort.heap_sort)
SortTest.test(fun_sort = Sort.bubble_sort)
SortTest.test(fun_sort = Sort.quick_sort)
SortTest.test(fun_sort = Sort.merge_sort)
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