生成数组

百万元素

import numpy as np

inputs = [ np.random.random() for _ in range(1000000)]

计时器

from functools import wraps
import time

def func_timer(function):
    @wraps(function)
    def function_timer(*args, **kwargs):
        print('[Function: {name} start]'.format(name = function.__name__))
        t0 = time.time()
        result = function(*args, **kwargs)
        t1 = time.time()
        print('[Function: {name} finished, spent time: {time:.2f}s]'.format(name = function.__name__,time = t1 - t0))
        return result
    return function_timer

算法

$O(Nklog(k))$

@func_timer
def topk(inputs, k):
    assert(len(inputs)>k and k>=0)
    
    output = []
    for number in inputs:
        if len(output) < k:
            output.append(number)
        else:
#             output = heapq.nsmallest(k, output)  # 2.18s
#             output = sorted(output)[:k]  # 1.70s
            output.sort()  # 用时：1.07s
            if number < output[0]:
                continue
            else:
                output[0] = number
    return output[::-1]

print(topk(inputs,100))
'''
[Function: topk start...]
[Function: topk finished, spent time: 1.07s]
[0.9999996850874242, 0.9999986732620001, 0.9999966250630344, 0.9999965512918702, 0.9999918270818978, 0.9999908482147212, 0.9999904061966568, 0.9999898873637068, 0.9999894591229037, 0.9999885915510304, 0.9999880191296788, 0.9999875751372768, 0.9999871692714755, 0.9999864191450215, 0.9999856657239012, 0.9999844443591834, 0.999983190441878, 0.9999820489498568, 0.9999810894337499, 0.9999798321198289, 0.9999786907049485, 0.9999783739745713, 0.9999778918753542, 0.9999776934842499, 0.9999766904951057, 0.9999763063439243, 0.9999721491854319, 0.9999718129550014, 0.9999690380974163, 0.9999667084475456, 0.9999655424378023, 0.9999652281775585, 0.9999627432692645, 0.9999609286879659, 0.9999595897335395, 0.9999577801424298, 0.9999577731076444, 0.9999577422439052, 0.9999573787572927, 0.9999545285610502, 0.9999536027375621, 0.9999517383688369, 0.9999505332989274, 0.9999467936557633, 0.9999462369888729, 0.9999434710302963, 0.9999428328375325, 0.9999415741653278, 0.9999395817201105, 0.9999383994805728, 0.9999372632501194, 0.9999353698560306, 0.9999347634017355, 0.9999342421914866, 0.9999333493394821, 0.9999327463812188, 0.9999326525157203, 0.9999323359413231, 0.9999319116353387, 0.9999318213132441, 0.9999311379877591, 0.9999278784463489, 0.999927795786208, 0.9999275307723007, 0.9999256814570631, 0.9999246819583866, 0.9999242658863041, 0.99992388656362, 0.9999218118780165, 0.9999214050287274, 0.9999213871879659, 0.9999212511033996, 0.9999200515736231, 0.9999186201014783, 0.9999177853796155, 0.9999141870066893, 0.9999122366576134, 0.9999109936676648, 0.9999096934306023, 0.9999096419907019, 0.9999089127419833, 0.99990882593983, 0.9999087135374473, 0.9999059676663873, 0.9999043718775731, 0.9999041573645822, 0.9999036853969006, 0.9999032218497446, 0.9999027069924663, 0.999901741000922, 0.9999016699195791, 0.9999009761556364, 0.9998998985072448, 0.9998987815252058, 0.9998975056695498, 0.9998950521471704, 0.9998944129310622, 0.9998942554330341, 0.9998936486586968, 0.9998927647795649]
'''

最强的还是 heapq

$O(Nlog(k))$

0.24s

import heapq
import random
 
import time
 
 
class TopkHeap(object):
    def __init__(self, k):
        self.k = k
        self.data = []
 
    def Push(self, elem):
        if len(self.data) < self.k:
            heapq.heappush(self.data, elem)
        else:
            topk_small = self.data[0]
            if elem > topk_small:
                heapq.heapreplace(self.data, elem)
 
    def TopK(self):
        return [x for x in reversed([heapq.heappop(self.data) for _ in range(len(self.data))])]
 
 

start=time.time()
th = TopkHeap(100)
for i in inputs:
    th.Push(i)
print(th.TopK())
print("cost time",time.time()-start)
'''
[0.9999996850874242, 0.9999986732620001, 0.9999966250630344, 0.9999965512918702, 0.9999918270818978, 0.9999908482147212, 0.9999904061966568, 0.9999898873637068, 0.9999894591229037, 0.9999885915510304, 0.9999880191296788, 0.9999875751372768, 0.9999871692714755, 0.9999864191450215, 0.9999856657239012, 0.9999844443591834, 0.999983190441878, 0.9999820489498568, 0.9999810894337499, 0.9999798321198289, 0.9999786907049485, 0.9999783739745713, 0.9999778918753542, 0.9999776934842499, 0.9999766904951057, 0.9999763063439243, 0.9999721491854319, 0.9999718129550014, 0.9999690380974163, 0.9999667084475456, 0.9999655424378023, 0.9999652281775585, 0.9999627432692645, 0.9999609286879659, 0.9999595897335395, 0.9999577801424298, 0.9999577731076444, 0.9999577422439052, 0.9999573787572927, 0.9999545285610502, 0.9999536027375621, 0.9999517383688369, 0.9999505332989274, 0.9999467936557633, 0.9999462369888729, 0.9999434710302963, 0.9999428328375325, 0.9999415741653278, 0.9999395817201105, 0.9999383994805728, 0.9999372632501194, 0.9999353698560306, 0.9999347634017355, 0.9999342421914866, 0.9999333493394821, 0.9999327463812188, 0.9999326525157203, 0.9999323359413231, 0.9999319116353387, 0.9999318213132441, 0.9999311379877591, 0.9999278784463489, 0.999927795786208, 0.9999275307723007, 0.9999256814570631, 0.9999246819583866, 0.9999242658863041, 0.99992388656362, 0.9999218118780165, 0.9999214050287274, 0.9999213871879659, 0.9999212511033996, 0.9999200515736231, 0.9999186201014783, 0.9999177853796155, 0.9999141870066893, 0.9999122366576134, 0.9999109936676648, 0.9999096934306023, 0.9999096419907019, 0.9999089127419833, 0.99990882593983, 0.9999087135374473, 0.9999059676663873, 0.9999043718775731, 0.9999041573645822, 0.9999036853969006, 0.9999032218497446, 0.9999027069924663, 0.999901741000922, 0.9999016699195791, 0.9999009761556364, 0.9998998985072448, 0.9998987815252058, 0.9998975056695498, 0.9998950521471704, 0.9998944129310622, 0.9998942554330341, 0.9998936486586968, 0.9998927647795649]
cost time 0.24733829498291016
'''