基于Python装饰器的向量化计算速度对比

from time import time
import numpy as np

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,return %.4f" %(func.__name__,usedTime,result))
        return result
    return inner

@timer
def sumOfLoop(np_array):
    result = 0
    for i in np_array:
        result += i * i
    return result

@timer
def sumOfComprehension(np_array):
    return sum([i * i for i in np_array])

@timer
def sumOfVectorization(np_array):
    return np.dot(np_array,np_array)

if __name__ == "__main__":
    print("计算小数平方和三种方法对比:")
    n = np.random.rand(3000000)
    a = sumOfLoop(n)
    print(a)
    sumOfComprehension(n)
    sumOfVectorization(n)
    print("计算整数平方和三种方法对比:")
    n = np.array(range(3000000)).astype('int64')
    sumOfLoop(n)
    sumOfComprehension(n)
    sumOfVectorization(n)