I am actually -1 on this, because the consequence would be that np.std(X,
axis=-1) would no longer be one. I am afraid that it would confuse the
users.

I believe that the n/(n - 1) difference is completely irrelevent for
machine learning purpose. If a quantity is relevant, it is the norm of
the feature vectors, in the geometrical sens, and thus the current
implementation.

That said, I am OK adding an additional parameter, if people think that
it is important. The one used in numpy, "ddof", is somewhat cryptic,
though.

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