Python_inner product

아래를 참고하였습니다.

L03_perceptron_slides (sebastianraschka.com)

import numpy as np

x, w = np.random.rand(100000), np.random.rand(100000) 

def forloop(x, w):
    z = 0.
    for i in range(len(x)):
        z += x[i] * w[i]
    return z

def listcomprehension(x, w):
    return sum(x_i*w_i for x_i, w_i in zip(x,w))

def vectorize(x, w):
    x_vec = np.array(x)
    w_vec = np.array(w)
    return x_vec.dot(w_vec)
    
%timeit -r 100 -n 10 forloop(x, w)
>>> 52 ms ± 3.89 ms per loop (mean ± std. dev. of 100 runs, 10 loops each)
%timeit -r 100 -n 10 listcomprehension(x, w)
>>> 43.3 ms ± 2.05 ms per loop (mean ± std. dev. of 100 runs, 10 loops each)
%timeit -r 100 -n 10 vectorize(x, w)
>>> 924 µs ± 156 µs per loop (mean ± std. dev. of 100 runs, 10 loops each)