> This is a known problem with np.linalg.norm, and so is the memory
> consumption. You should use sklearn.utils.extmath.norm for the
> Frobenius norm.
Hmm. Indeed I missed that, but still, this is a bit odd.
sklearn.utils.extmath.norm is slower than raveling on my anaconda with
MKL accelerate setup:
In [2]: from sklearn.utils.extmath import norm
In [3]: %timeit norm(X)
10 loops, best of 3: 21.4 ms per loop
In [4]: %timeit np.sqrt(np.dot(X.ravel(), X.ravel()))
1000 loops, best of 3: 548 µs per loop
And it looks like the sklearn blas call makes a memory copy too:
Line # Mem usage Increment Line Contents
================================================
7 47.0 MiB 0.0 MiB def sumsq(X):
8 47.0 MiB 0.0 MiB return np.sqrt(np.sum(X ** 2))
Filename: fro.py
Line # Mem usage Increment Line Contents
================================================
10 47.0 MiB 0.0 MiB def raveled(X):
11 47.6 MiB 0.6 MiB return
np.sqrt(np.dot(X.ravel(), X.ravel()))
Filename: fro.py
Line # Mem usage Increment Line Contents
================================================
4 39.0 MiB 0.0 MiB def blas(X):
5 47.0 MiB 8.0 MiB return norm(X)
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