np.nan_to_num replaces NaN's with zeros. If you want to take into account
the fact that you are normalizing over less entries, you need to do
normalize(np.nan_to_num(X)) * np.sqrt(np.isnan(X).sum(0) /
float(X.shape[0]))
On Mon, Jun 15, 2015 at 5:28 PM, Andreas Mueller wrote:
> Hey.
> Not wit
Hey.
Not with scikit-learn but it should be about three lines in numpy to do
it yourself.
I would replace them with 0 for computing the norm, that is all there
is, right?
Andy
On 06/15/2015 10:43 AM, William Correa beltran wrote:
Hello,
I would like to know if there is a way to normalize a
