A summary of common centralized sorting algorithms
Merge sort
Merge sort, also called merge sort, is a typical application of the divide and conquer method. The idea of divide and conquer is to decompose each problem into small problems, solve each small problem, and then merge them.
The specific merge sort is to recursively decompose a set of unordered numbers into sub-items with only one element by n/2, and one element is already sorted. Then merge these ordered sub-elements.
The process of merging is to compare two sorted subsequences, first select the smallest element in the two subsequences, select the smallest subsequence of the two elements, and remove and add it from the subsequence
to the final result set until the two subsequences are merged.
The code is as follows:
#!/usr/bin/python
import sys
def merge(nums, first, middle, last):
''''' merge '''
# 切片边界,左闭右开并且是了0为开始
lnums = nums[first:middle+1]
rnums = nums[middle+1:last+1]
lnums.append(sys.maxint)
rnums.append(sys.maxint)
l = 0
r = 0
for i in range(first, last+1):
if lnums[l] < rnums[r]:
nums[i] = lnums[l]
l+=1
else:
nums[i] = rnums[r]
r+=1
def merge_sort(nums, first, last):
''''' merge sort
merge_sort函数中传递的是下标，不是元素个数
'''
if first < last:
middle = (first + last)/2
merge_sort(nums, first, middle)
merge_sort(nums, middle+1, last)
merge(nums, first, middle,last)
if __name__ == '__main__':
nums = [10,8,4,-1,2,6,7,3]
print 'nums is:', nums
merge_sort(nums, 0, 7)
print 'merge sort:', numsStable, time complexity O(nlog n)
Insertion sort
The code is as follows:
#!/usr/bin/python import sys def insert_sort(a):
''''' Insertion sort
There is an already ordered data sequence, It is required to insert a number into this arranged data sequence, but it is required that the data sequence remains in order after insertion. At the beginning, one element is obviously in order, then insert an element to the appropriate position, and then insert the third element, and so on
'''
a_len = len(a)
if a_len = 0 and a[j] > key:
a[j+1] = a[j]
j-=1
a[j+1] = key
return a
if __name__ == '__main__':
nums = [10,8,4,-1,2,6,7,3]
print 'nums is:', nums
insert_sort(nums)
print 'insert sort:', numsis stable, and the time complexity is O(n^2)
To exchange the values of two elements in python, you can write: a, b = b, a. In fact, this is because the left and right sides of the assignment symbol are tuples
(it needs to be emphasized here that in python, tuples Groups are actually delimited by commas "," rather than brackets).
Selection sort
Selection sort is a simple and intuitive sorting algorithm. Here's how it works. First, find the smallest (large) element in the unsorted sequence and store it at the beginning of the sorted sequence. Then, continue to find the smallest (large) element from the remaining unsorted elements, and then put it at the end of the sorted sequence. And so on, until all elements are sorted.
import sys def select_sort(a):
''''' Selection sort
In each pass, the smallest (or largest) element is selected from the data elements to be sorted, and
is placed at the end of the sorted sequence until all the elements are to be sorted. The sorted data elements are completed.
Selection sort is an unstable sorting method.
'' r a_len=len(a)
for i in range(a_len):#在0-n-1上依次选择相应大小的元素
min_index = i#记录最小元素的下标
for j in range(i+1, a_len):#查找最小值
if(a[j]Copy after login is unstable, time complexity O (n^2)
Hill sort
Hill sorting, also known as decreased incremental sorting algorithm, Hill's sorting is non -stable sorting algorithm. This method is also called reducing incremental sorting, because DL. Shell was named after it was proposed in 1959.
First take an integer d1 less than n as the first increment, and divide all records in the file into d1 groups. All records whose distance is a multiple of d1 are placed in the same group. First sort within each group;
Then, take the second increment d2 Unstable, time complexity average time O(nlogn) worst time O(n^s)1 Heap Sort (Heap Sort) Definition of "heap" : In the "heap" with a starting index of 0: The right child node of node i is at position 2 * i + 24) The parent node of node i is at position floor( (i - 1) / 2) : Note that floor means "Rounding" operation Features of the heap: The key value of each node must always be greater (or less) than its parent node "Max Heap": The root node of the "heap" saves the key The node with the largest value. That is, the key value of each node in the "heap" is always greater than its child nodes. Move up, move down: When the key value of a node is greater than its parent node, then we have to perform a "move up" operation, that is, we move the node to the position of its parent node, Let its parent node move to its position, and then we continue to judge the node and do not stop "moving up" until the node is no longer larger than its parent node. Now let’s learn more about the “move down” operation. When we change the key value of a node to a smaller value, we need to "move it down". Method: We first build a maximum heap (time complexity O(n)), and then each time we only need to exchange the root node with the node at the last position, and then exclude the last position, and then After the exchange, the heap of the root node is adjusted (time complexity O(lgn)), that is, the root node can be "moved down". The total time complexity of heap sort is O(nlgn). The code is as follows: 不稳定，时间复杂度 O(nlog n) 快速排序 快速排序算法和合并排序算法一样，也是基于分治模式。对子数组A[p...r]快速排序的分治过程的三个步骤为： 分解：把数组A[p...r]分为A[p...q-1]与A[q+1...r]两部分，其中A[p...q-1]中的每个元素都小于等于A[q]而A[q+1...r]中的每个元素都大于等于A[q]； 解决：通过递归调用快速排序，对子数组A[p...q-1]和A[q+1...r]进行排序； 合并：因为两个子数组是就地排序的，所以不需要额外的操作。 对于划分partition 每一轮迭代的开始，x=A[r], 对于任何数组下标k，有： 1) 如果p≤k≤i，则A[k]≤x。 2) 如果i+1≤k≤j-1，则A[k]>x。 3) 如果k=r，则A[k]=x。 代码如下： 不稳定，时间复杂度 最理想 O(nlogn)最差时间O(n^2) 说下python中的序列： 列表、元组和字符串都是序列，但是序列是什么，它们为什么如此特别呢？序列的两个主要特点是索引操作符和切片操作符。索引操作符让我们可以从序列中抓取一个特定项目。切片操作符让我们能够获取序列的一个切片，即一部分序列,如：a = ['aa','bb','cc'], print a[0] 为索引操作，print a[0:2]为切片操作。def shell_sort(a):
''''' shell排序
'''
a_len=len(a)
gap=a_len/2#增量
while gap>0:
for i in range(a_len):#对同一个组进行选择排序
m=i
j=i+1
while jCopy after login#!/usr/bin env python
# 数组编号从 0开始
def left(i):
return 2*i +1
def right(i):
return 2*i+2
#保持最大堆性质 使以i为根的子树成为最大堆
def max_heapify(A, i, heap_size):
if heap_size <= 0:
return
l = left(i)
r = right(i)
largest = i # 选出子节点中较大的节点
if l A[largest]:
largest = l
if r A[largest]:
largest = r
if i != largest :#说明当前节点不是最大的，下移
A[i], A[largest] = A[largest], A[i] #交换
max_heapify(A, largest, heap_size)#继续追踪下移的点
#print A
# 建堆
def bulid_max_heap(A):
heap_size = len(A)
if heap_size >1:
node = heap_size/2 -1
while node >= 0:
max_heapify(A, node, heap_size)
node -=1
# 堆排序 下标从0开始
def heap_sort(A):
bulid_max_heap(A)
heap_size = len(A)
i = heap_size - 1
while i > 0 :
A[0],A[i] = A[i], A[0] # 堆中的最大值存入数组适当的位置，并且进行交换
heap_size -=1 # heap 大小 递减 1
i -= 1 # 存放堆中最大值的下标递减 1
max_heapify(A, 0, heap_size)
if __name__ == '__main__' :
A = [10, -3, 5, 7, 1, 3, 7]
print 'Before sort:',A
heap_sort(A)
print 'After sort:',ACopy after login#!/usr/bin/env python
# 快速排序
'''''
划分 使满足 以A[r]为基准对数组进行一个划分，比A[r]小的放在左边，
比A[r]大的放在右边
快速排序的分治partition过程有两种方法，
一种是上面所述的两个指针索引一前一后逐步向后扫描的方法,
另一种方法是两个指针从首位向中间扫描的方法。
'''
#p,r 是数组A的下标
def partition1(A, p ,r):
'''''
方法一，两个指针索引一前一后逐步向后扫描的方法
'''
x = A[r]
i = p-1
j = p
while j < r:
if A[j] < x:
i +=1
A[i], A[j] = A[j], A[i]
j += 1
A[i+1], A[r] = A[r], A[i+1]
return i+1
def partition2(A, p, r):
'''''
两个指针从首尾向中间扫描的方法
'''
i = p
j = r
x = A[p]
while i = x and i < j:
j -=1
A[i] = A[j]
while A[i]<=x and i < j:
i +=1
A[j] = A[i]
A[i] = x
return i
# quick sort
def quick_sort(A, p, r):
'''''
快速排序的最差时间复杂度为O(n2)，平时时间复杂度为O(nlgn)
'''
if p < r:
q = partition2(A, p, r)
quick_sort(A, p, q-1)
quick_sort(A, q+1, r)
if __name__ == '__main__':
A = [5,-4,6,3,7,11,1,2]
print 'Before sort:',A
quick_sort(A, 0, 7)
print 'After sort:',ACopy after login
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