Python实现一些常用排序算法
一些常用的排序
#系统内置排序算法
#list.sort()
#heapq模块
def sys_heap_sort(list): import heapq heap = [] for i in range(len(list)): heapq.heappush(heap,list[i]) for i in range(len(heap)): list[i] = heapq.heappop(heap)
#python操作列表的方法，它们的时间复杂度
#insert() ---> O(n)
#remove() ---> O(n)
#append() -----> O(1)
#in ------> 0(n)
#计数排序
#规定无序列表元素要求有范围
#统计个元素出现次数，最后修改列表
#对于年龄列表做大规模排序非常试用
def count_sort(list,maxNum): print("\ncount_sort:") counter = [0 for i in range(maxNum+1)] for i in list: counter[i] += 1 i = 0 for num,c in enumerate(counter): for j in range(c): list[i] = num i += 1
#找前n个大数
#方法一：
#时间复杂度O(n^2)
#将无序列表使用排序算法排序，取最后n个互异的数
#方法二：
#时间复杂度O(kn)
#构造一个n个大小的列表，对这个列表使用插入排序，每次从无序列表获取一个元素，插入构造列表，淘汰构造列表最小数，直到无序列表取完
#方法三：
#时间复杂度O(nlogk)
#将前n个数构造一个小根堆，每次从无序列表取一个元素和堆顶元素比较，小则淘汰，大则替换，然后调整堆，直至无序列表取完
#方法四：
#与方法三原理相同，只不过是使用系统heapq模块
#方法二
def topk_search(list,k): ltmp = list[0: k + 1] insert_sort(ltmp) ltmp.reverse() print("get_topk:") for i in range(k + 1,len(list)): j = k - 1 while j >= 0 and ltmp[j] < list[i]: ltmp[j + 1] = ltmp[j] j -= 1 ltmp[j + 1] = list[i] return ltmp[0: k]
#方法三：
def sift_small(list,low,high): i = low j = 2 * i + 1 temp = list[i] while j <= high: if j < high and list[j] > list[j+1]: j += 1 if temp > list[j]: list[i] = list[j] i = j j = 2 * i + 1 else: break list[i] = temp list[low],list[high] = list[low],list[high] def topn_search(list,n): print("\nget_topn:") heap = list[0:n] for i in range(n//2-1,-1,-1): sift_small(heap, i, n - 1) for i in range(n,len(list)): if heap[0] < list[i]: heap[0] = list[i] sift_small(heap, 0, n - 1) for i in range(n-1, -1, -1): heap[0],heap[i] = heap[i],heap[0] sift_small(heap, 0, i - 1) return heap
#方法四：
def sys_topn_search(list,n): import heapq return heapq.nlargest(n,list)
全部排序算法大杂烩
__author__ = 'cq' import time import random import sys import copy def time_cost(func): def wrapper(*args,**kwargs): sTime = time.time() func(*args,**kwargs) print("Time cost:%s"%(time.time()-sTime)) print(args[0]) return wrapper #-------------------系统自带排序-----------------------# @time_cost def sys_sort(list): list.sort() #-------------------冒泡排序-----------------------# @time_cost def bubble_sort(list): print("\nbubble_sort:") for i in range(len(list)-1): tag = 0 for j in range(len(list)-i-1): if list[j] > list[j+1]: list[j],list[j+1] = list[j+1],list[j] tag = 1 if not tag: return #-------------------插入排序-----------------------# @time_cost def insert_sort(list): print("\ninsert_sort:") for i in range(len(list)): tag = 0 for j in range(i,0,-1): if list[j] < list[j-1]: list[j],list[j-1] = list[j-1],list[j] tag = 1 if not tag: break #-------------------选择排序-----------------------# @time_cost def select_sort(list): print("\nselect_sort:") for i in range(len(list)-1): min = i for j in range(i+1,len(list)): if list[min] > list[j]: min = j if min != i: list[i],list[min] = list[min],list[i] #-------------------快速排序-----------------------# def part_sort(list,left,right): temp = list[left] while left < right: while left < right and temp <= list[right]: right -= 1 list[left] = list[right] while left < right and temp >= list[left]: left += 1 list[right] = list[left] list[left] = temp return left def _quckly_sort(list,left,right): if left < right: mid = part_sort(list,left,right) _quckly_sort(list,left,mid-1) _quckly_sort(list,mid+1,right) @time_cost def quckly_sort(list): print("\nquckly_sort:") return _quckly_sort(list,0,len(list)-1) #-------------------堆排序-----------------------# def sift(list,low,high): i = low j = 2 * i + 1 temp = list[i] while j <= high: if j < high and list[j] < list[j+1]: j += 1 if temp < list[j]: list[i] = list[j] i = j j = 2 * i + 1 else: break list[i] = temp list[low],list[high] = list[low],list[high] @time_cost def heap_sort(list): print("\nheap_sort:") n = len(list) for i in range(n // 2 - 1, -1, -1): sift(list, i, n - 1) for i in range(n-1, -1, -1): list[0],list[i] = list[i],list[0] sift(list, 0, i - 1) #-------------------系统自带堆排序------------------# @time_cost def sys_heap_sort(list): import heapq heap = [] for i in range(len(list)): heapq.heappush(heap,list[i]) for i in range(len(heap)): list[i] = heapq.heappop(heap) #-------------------归并排序-----------------------# def ont_megre_sort(list,low,mid,high): i = low j = mid + 1 ltmp = [] while i <= mid and j <= high: if list[i] < list[j]: ltmp.append(list[i]) i += 1 else: ltmp.append(list[j]) j += 1 while i <= mid: ltmp.append(list[i]) i += 1 while j <= high: ltmp.append(list[j]) j += 1 list[low:high+1] = ltmp def _megre_sort(list,low,high): if low < high: mid = (low+high)//2 _megre_sort(list,low,mid) _megre_sort(list,mid+1,high) ont_megre_sort(list,low,mid,high) @time_cost def megre_sort(list): print("\nmegre_sort:") return _megre_sort(list,0,len(list)-1) #-------------------希尔排序-----------------------# @time_cost def shell_sort(list): print("\nshell_sort:") gap = len(list) // 2 while gap > 0: for i in range(gap,len(list)): temp = list[i] j = i - gap while j >= 0 and temp < list[j]: list[j + gap] = list[j] j -= gap list[j + gap] = temp gap //= 2 #-------------------统计排序-----------------------# @time_cost def count_sort(list,maxNum): print("\ncount_sort:") counter = [0 for i in range(maxNum+1)] for i in list: counter[i] += 1 i = 0 for num,c in enumerate(counter): for j in range(c): list[i] = num i += 1 #-------------------插入排序获取Top前n的数-----------------------# def topk_search(list,k): ltmp = list[0: k + 1] insert_sort(ltmp) ltmp.reverse() print("get_topk:") for i in range(k + 1,len(list)): j = k - 1 while j >= 0 and ltmp[j] < list[i]: ltmp[j + 1] = ltmp[j] j -= 1 ltmp[j + 1] = list[i] return ltmp[0: k] #-------------------堆排序获取Top前n的数-----------------------# def sift_small(list,low,high): i = low j = 2 * i + 1 temp = list[i] while j <= high: if j < high and list[j] > list[j+1]: j += 1 if temp > list[j]: list[i] = list[j] i = j j = 2 * i + 1 else: break list[i] = temp list[low],list[high] = list[low],list[high] def topn_search(list,n): print("\nget_topn:") heap = list[0:n] for i in range(n//2-1,-1,-1): sift_small(heap, i, n - 1) for i in range(n,len(list)): if heap[0] < list[i]: heap[0] = list[i] sift_small(heap, 0, n - 1) for i in range(n-1, -1, -1): heap[0],heap[i] = heap[i],heap[0] sift_small(heap, 0, i - 1) return heap #-------------------系统堆排序获取Top前n的数-----------------------# # @time_cost def sys_topn_search(list,n): import heapq return heapq.nlargest(n,list) def main(): #生成列表 list0 = list(range(100)) first_name = ["陈","张","李","王","赵"] second_name = ["冰","鑫","程","爱","暖"] third_name = ["强","国","明","风","芬"] listname = [ {"id":"1000"+str(i), "name":random.choice(first_name)+ random.choice(second_name)+ random.choice(third_name), "age":random.randint(16,60) } for i in range(10) ] random.shuffle(list0) random.shuffle(listname) #copy四份打乱后的列表 list1 = copy.deepcopy(list0) list2 = copy.deepcopy(list0) list3 = copy.deepcopy(list0) list4 = copy.deepcopy(list0) list5 = copy.deepcopy(list0) list6 = copy.deepcopy(list0) list7 = copy.deepcopy(list0) list8 = copy.deepcopy(list0) list9 = copy.deepcopy(list0) list10 = copy.deepcopy(list0) list11 = copy.deepcopy(list0) list12 = copy.deepcopy(list0) #设置递归深度 sys.setrecursionlimit(10000) print("sort_list:") print(list0) # 排序算法 sys_sort(list0) bubble_sort(list1) select_sort(list2) insert_sort(list3) quckly_sort(list4) heap_sort(list5) sys_heap_sort(list11) megre_sort(list6) shell_sort(list7) count_sort(list8,1000) print(topk_search(list9,10)) print(topn_search(list10,10)) print(sys_topn_search(list12,10)) if "__main__" == __name__: main()
