0.產(chǎn)生7000長(zhǎng)度的亂序列表
import random
a_list = list(range(1,7000 + 1))
normal_list = random.sample(a_list, k=len(a_list))
normal_list[:5]
上面一段代碼的運(yùn)行結(jié)果如下,因?yàn)槭请S機(jī)打亂順序,讀者運(yùn)行結(jié)果會(huì)不同:
[2780, 397, 5063, 6494, 1245]
0.1 保存此亂序列表
import pickle
with open('normal_list.pickle', 'wb') as file:
pickle.dump(normal_list, file)
0.2 加載此亂序列表
import pickle
with open('normal_list.pickle', 'rb') as file:
normal_list = pickle.load(file)
0.3 計(jì)時(shí)裝飾器
裝飾器是python的高級(jí)用法,初學(xué)者需要單獨(dú)學(xué)習(xí)1天才能理解并且熟練運(yùn)用。
讀者如果不理解本節(jié)內(nèi)容,不影響后續(xù)內(nèi)容的理解。
此裝飾器只是計(jì)算函數(shù)運(yùn)行花費(fèi)的時(shí)間,讀者可以自己用其他方法實(shí)現(xiàn)相同效果。
from time import time
def timer(func):
def inner(*args,**kwargs):
start = time()
result = func(*args,**kwargs)
end = time()
usedTime = 1000 * (end - start)
print("%s function used %.2f ms" %(func.__name__,usedTime))
return result
return inner
1.冒泡排序
@timer
def bubble_sort(normal_list):
length = len(normal_list)
for i in range(length, 1, -1):
for j in range(0, i-1):
if normal_list[j] > normal_list[j+1]:
normal_list[j], normal_list[j+1] = normal_list[j+1], normal_list[j]
with open('normal_list.pickle', 'rb') as file:
normal_list = pickle.load(file)
bubble_sort(normal_list)
print(normal_list[:10])
print(normal_list[-10:])
上面一段代碼的運(yùn)行結(jié)果如下:
bubble_sort function used 7858.98 ms
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
[6991, 6992, 6993, 6994, 6995, 6996, 6997, 6998, 6999, 7000]
2.選擇排序
@timer
def select_sort(normal_list):
length = len(normal_list)
new_list = []
for i in range(length, 1, -1):
max_index = 0
max_value = normal_list[0]
for j in range(1, i):
if normal_list[j] > max_value:
max_value = normal_list[j]
max_index = j
normal_list[i-1], normal_list[max_index] = normal_list[max_index], normal_list[i-1]
with open('normal_list.pickle', 'rb') as file:
normal_list = pickle.load(file)
select_sort(normal_list)
print(normal_list[:10])
print(normal_list[-10:])
上面一段代碼的運(yùn)行結(jié)果如下:
select_sort function used 2018.90 ms
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
[6991, 6992, 6993, 6994, 6995, 6996, 6997, 6998, 6999, 7000]
3.結(jié)論
雖然冒泡排序和選擇排序的時(shí)間復(fù)雜度都是O(n^2),但是經(jīng)過實(shí)踐檢驗(yàn),在python實(shí)現(xiàn)2種排序算法后,選擇排序花費(fèi)的時(shí)間明顯第冒泡排序花費(fèi)的時(shí)間。