[英]numpy.linalg.norm VS scipy cdist for L2 norm
Very in advance apologies for my basic question!非常提前为我的基本问题道歉!
a = np.random.rand(6, 3)
b = np.random.rand(6, 3)
Using scipy.spatial.distance.cdist
and d = cdist(a, b, 'euclidean')
, results in:使用
scipy.spatial.distance.cdist
和d = cdist(a, b, 'euclidean')
,结果:
[[0.8625803 0.29814357 0.97548993 0.84368212 0.66530478 0.95367553]
[0.67858887 0.27603821 0.76236585 0.80857596 0.48560167 0.84517836]
[0.53097997 0.41061975 0.66475479 0.54243987 0.47469843 0.70178229]
[0.37678898 0.7855905 0.25492161 0.79870147 0.37795642 0.58136674]
[0.73515058 0.90614048 0.88997676 0.15126486 0.82601188 0.63733843]
[0.34345477 0.7927319 0.52963369 0.27127254 0.64808932 0.66528862]]
But d = np.linalg.norm(a - b, axis=1)
, returns only the diagonal of scipy
answer:但是
d = np.linalg.norm(a - b, axis=1)
,只返回scipy
答案的对角线:
[0.8625803 0.27603821 0.66475479 0.79870147 0.82601188 0.66528862]
Is it possible to get the result of scipy.spatial.distance.cdist
using only np.linalg.norm
or numpy
?是否可以仅使用
np.linalg.norm
或numpy
获得scipy.spatial.distance.cdist
的结果?
You can use numpy broadcasting as follows:您可以使用numpy 广播,如下所示:
d = np.linalg.norm(a[:, None, :] - b[None, :, :], axis=2)
Performace should be similar to scipy.spatial.distance.cdist
, in my local machine:在我的本地机器中,性能应该类似于
scipy.spatial.distance.cdist
:
%timeit np.linalg.norm(a[:, None, :] - b[None, :, :], axis=2)
13.5 µs ± 1.71 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit cdist(a,b)
15 µs ± 236 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
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