常见的几种排序算法，包括冒泡排序、选择排序、插入排序、快速排序、归并排序、希尔排序和堆排序，冒泡排序、选择排序和插入排序的时间复杂度为O(n^2)，希尔排序的时间复杂度为O(n^1.25)，快速排序、归并排序和堆排序的时间复杂度为O(nlog2(n))。
列出几种算法的代码实现：
- 冒泡排序
def bubble_sort(sort_list):
iter_len = len(sort_list)
if iter_len<2:
return sort_list
for i in range(iter_len-1):
for j in range(iter_len-i-1):
if sort_list[j]>sort_list[j+1]:
sort_list[j], sort_list[j+1] = sort_list[j+1], sort_list[j]
return sort_list
- 选择排序
def selection_sort(sort_list):
iter_len = len(sort_list)
if iter_len<2:
return sort_list
for i in range(iter_len-1):
smallest = sort_list[i]
location = i
for j in range(i+1, iter_len):
if sort_list[j]<smallest:
smallest = sort_list[j]
location = j
if i != location:
sort_list[i], sort_list[location] = sort_list[location], sort_list[i]
return sort_list
- 插入排序
def insertion_sort(sort_list):
iter_len = len(sort_list)
if iter_len<2:
return sort_list
for i in range(1, iter_len):
key = sort_list[i]
location = i
for j in range(i-1, -1, -1):
if sort_list[j]>key:
sort_list[j+1] = sort_list[j]
location = j
sort_list[location] = key
return sort_list
- 快速排序
class quick_sort(object):
def _partition(self, alist, p, r):
i, j = p, r
x = alist[p]
while i<j:
while i<j and alist[j]>=x:
j -= 1
if i<j:
alist[i] = alist[j]
i += 1
while i<j and alist[i]<=x:
i += 1
if i<j:
alist[j] = alist[i]
j -= 1
alist[i] = x
return i
def _quicksort(self, alist, p, r):
if p<r:
q = self._partition(alist, p, r)
self._quicksort(alist, p, q-1)
self._quicksort(alist, q+1, r)
def __call__(self, sort_list):
self._quicksort(sort_list, 0, len(sort_list)-1)
return sort_list
- 归并排序
class merge_sort(object):
def _merge(self, alist, p, q, r):
left = alist[p:q+1]
right = alist[q+1:r+1]
for i in range(p, r+1):
if len(left)>0 and len(right)>0:
if left[0] <= right[0]:
alist[i] = left.pop(0)
else:
alist[i] = right.pop(0)
elif len(right) == 0:
alist[i] = left.pop(0)
elif len(left) == 0:
alist[i] = right.pop(0)
def _merge_sort(self, alist, p, r):
if p<r:
q = int((p+r)/2)
self._merge_sort(alist, p, q)
self._merge_sort(alist, q+1, r)
self._merge(alist, p, q, r)
def __call__(self, sort_list):
self._merge_sort(sort_list, 0, len(sort_list)-1)
return sort_list
- 希尔排序
def shell_sort(sort_list):
iter_len = len(sort_list)
if iter_len<2:
return sort_list
gap = int(iter_len/2)
while gap>0:
for i in range(0, gap):
for j in range(i+gap, iter_len, gap):
if sort_list[j]<sort_list[j-gap]:
key = sort_list[j]
k = j-gap
while k>=0 and sort_list[k]>key:
sort_list[k+gap] = sort_list[k]
k -= gap
sort_list[k+gap] = key
gap = int(gap/2)
return sort_list
- 堆排序
class heap_sort(object):
def _max_heapify(self, alist, i, heap_size=None):
length = len(alist)
if heap_size is None:
heap_size = length
l = 2*i+1
r = 2*i+2
largest = i
if l<heap_size and alist[l]>alist[i]:
largest = l
if r<heap_size and alist[r]>alist[largest]:
largest = r
if largest != i:
alist[i], alist[largest] = alist[largest], alist[i]
self._max_heapify(alist, largest, heap_size)
def _build_max_heap(self, alist):
root_end = int(len(alist)/2)
for i in range(root_end-1, -1, -1):
self._max_heapify(alist, i)
def __call__(self, sort_list):
self._build_max_heap(sort_list)
heap_size = len(sort_list)
for i in range(len(sort_list)-1, 0, -1):
sort_list[0], sort_list[i] = sort_list[i], sort_list[0]
heap_size -= 1
self._max_heapify(sort_list, 0, heap_size)
return sort_list
