for i in datalist:
if i >= L and i <= H:
P.append(i)
if k >= Lp and k <= Lh and len(P) <= (4 * datasize + 1):
P.sort()
return P[k - Lp]
#return None
if __name__ == "__main__":
# datasize_list = [1000, 2000, 5000, 10000, 20000, 50000, 100000]
# 从列表中随机选择一个数据集大小
# datasize = random.choice(datasize_list)
# 随机生成datasize个范围再(0,1000000)的数
datasize = 50000
datalist = brand.get_many_data(datasize, 0, 1000000)
# print datalist[0:30]
#k=input('请输入k值:(k<%d)'%datasize)
k = 10
# cishu=0
# sum=0
#for i in range(1):
start = time.time()
m = lazy_select(datalist, 0, datasize - 1, k)
#if m != None:
end = time.time()
print m
# cishu+=1
thistime = (end - start) * 1000
# sum+=thistime
##用于验证正确
三路快排函数
输入：待排序数组，l:数组起始位置，r：数组长度-1
作用：将数组进行排序
输出：返回已排好的数组
'''
def quick_sort_three_ways(nums,l,r):
if l<r:
lt,gt= partition_three_ways(nums,l,r)
quick_sort_three_ways(nums,l,lt-1)
quick_sort_three_ways(nums,gt,r)
return nums
if __name__ == "__main__":
#尝试无重复数据集的排序
for i in range(11):
repeatnumber=100000*i # print repeatnumber
test=bigdata.get_many_data(1000000,0,1000000,repeatnumber)
print "开始10^6个，重复数为：%d的内置函数sort排序...." %repeatnumber
# shuffle(test)
start=time.time()
#print len(test)
quick_sort_three_ways(test, 0, 1000000-1)
#quicksort(test,0,10000-1)
# test.sort()
end=time.time()
print "10^6个重复数：%d的数据集排序时间" %repeatnumber,round(end-start,2)
print "排序后前50个数："
print test[0:50]