On 28 March 2013 23:24, Lars Buitinck wrote:
> On Thu, Mar 28, 2013 at 2:35 PM, Nelle Varoquaux
> wrote:
> > But in general, I don't think we can "force" the user to use sparse
> > matrices. They are an absolute pain to work with because of the
> > inconsistencies of interface with ndarray and c
On Fri, Mar 29, 2013 at 7:24 AM, Lars Buitinck wrote:
> I know the "need to rewrite scipy.sparse" itch. I for one would be
> very excited if you were to volunteer ;)
I almost started this project twice but then I realized I would shoot
me in the foot if I started it alone and gave up. If we do i
On Thu, Mar 28, 2013 at 2:35 PM, Nelle Varoquaux
wrote:
> But in general, I don't think we can "force" the user to use sparse
> matrices. They are an absolute pain to work with because of the
> inconsistencies of interface with ndarray and conversion between sparse and
> dense can be time consumin
On Thu, Mar 28, 2013 at 2:35 PM, Nelle Varoquaux
wrote:
> But in general, I don't think we can "force" the user to use sparse
> matrices. They are an absolute pain to work with because of the
> inconsistencies of interface with ndarray and conversion between sparse and
> dense can be time consumin
On 28 March 2013 18:19, Jacob Vanderplas wrote:
> On Thu, Mar 28, 2013 at 10:10 AM, Lars Buitinck wrote:
>
>> 2013/3/28 Mathieu Blondel :
>> > Encoding missing values with np.nan doesn't scale to very
>> > high-dimensional problems with mostly missing values.
>> > Personally, for encoding missing
On Thu, Mar 28, 2013 at 10:10 AM, Lars Buitinck wrote:
> 2013/3/28 Mathieu Blondel :
> > Encoding missing values with np.nan doesn't scale to very
> > high-dimensional problems with mostly missing values.
> > Personally, for encoding missing data, I just use sparse matrices.
> > Values which are
2013/3/28 Mathieu Blondel :
> Encoding missing values with np.nan doesn't scale to very
> high-dimensional problems with mostly missing values.
> Personally, for encoding missing data, I just use sparse matrices.
> Values which are actually zero can be stored explicitly in the .data
> attribute.
+
On Fri, Mar 29, 2013 at 12:57 AM, Nelle Varoquaux
wrote:
> We need to find a uniform way over the whole scikit to indicate missing
> data. Hence, 0 cannot be how missing data is spotted.
> A solution would be to use "Nan" but it is not very satisfying either, as
> this could lead to think there is
On Thu, Mar 28, 2013 at 04:57:37PM +0100, Nelle Varoquaux wrote:
> Maybe we should add an argument named missing, with how the missing data is
> indicated in the matrices ? For example, the signature of the MDS, using nan
> as
> missing data would be something like:
> mds.fit(X, missing=np.na
Hi Terry,
We need to find a uniform way over the whole scikit to indicate missing
data. Hence, 0 cannot be how missing data is spotted.
A solution would be to use "Nan" but it is not very satisfying either, as
this could lead to think there is missing data, while there isn't.
Maybe we should add
Hi all,
I was discussing with Nelle to add an algorithm to solve the classical MDS
with svd. but one thing we don't sure is how to check missing data so we can
fall back to SMACOF in that case.
my idea is to check if there are any 0 in non-diagonal elements.
what do you think?
Thanks & Regar
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