老實說，沒有比np.inner或np.dot更快的了。如果你覺得中間變量很煩人，你可以創建一個lambda函數：sqeuclidean = lambda x: np.inner(x, x)

np.inner和np.dot利用BLAS例程，幾乎肯定比標準的元素乘法加和快。In [1]: %%timeit -n 1 -r 100 a, b = np.random.randn(2, 1000000)

((a - b) ** 2).sum()

....:

The slowest run took 36.13 times longer than the fastest. This could mean that an intermediate result is being cached

1 loops, best of 100: 6.45 ms per loop

In [2]: %%timeit -n 1 -r 100 a, b = np.random.randn(2, 1000000)

np.linalg.norm(a - b, ord=2) ** 2

....:

1 loops, best of 100: 2.74 ms per loop

In [3]: %%timeit -n 1 -r 100 a, b = np.random.randn(2, 1000000)

sqeuclidean(a - b)

....:

1 loops, best of 100: 2.64 ms per loop

np.linalg.norm(..., ord=2)在內部使用np.dot，并提供與直接使用np.inner非常相似的性能。