Hi all,
Just sending an email for visibility. I've made a pull request to add Bm25
capabilities to complement TFIDF in feature_extraction.text. All tests
pass.
Sincerely,
Basil Beirouti
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CircleCI checks the documentation build (although apparently it ignores
changes only to docstrings). Travis runs all tests on a linux system.
AppVeyor tests on Windows.
On 12 July 2016 at 08:11, Basil Beirouti wrote:
>
> Hi,
>
> Joel thanks for pointing out the indentation issue. I have fixed it
Hi,
Joel thanks for pointing out the indentation issue. I have fixed it.
Can someone explain what the 3 tests that were automatically run on my code
are? And why did the Appveyor and Travis ones fail?
Sincerely,
Basil Beirouti
Sent from my iPhone
> On Jul 11, 2016, at 11:00 AM, scikit-lear
For the first question, look up the possible ways to construct
scipy.sparse.csr_matrix objects; one of them will take (data, indices, indptr).
Just pass a new array for data, and take the latter two from X.
For the second question, you can just do the elementwise operation in place on
the data
For the first question, look up the possible ways to construct
scipy.sparse.csr_matrix objects; one of them will take (data, indices, indptr).
Just pass a new array for data, and take the latter two from X.
For the second question, you can just do the elementwise operation in place on
the data
Oh yes that's exactly what I was looking for. So how do I initialize an array
with the same sparsity pattern as X? And then how do I do an element wise
divide of the numerator over the denominator, when both are sparse matrices?
Like you said it should only do this operation on the non zero elem
In the denominator you mean? It looks like you only need to add that to nonzero
elements, since the others would all have a 0 in the numerator, right? So the
final value would be zero there. Or am I missing something?
You can initialize an array with the same sparsity pattern as X, but its data
Hi Vlad,
Thanks for the quick reply. Unfortunately there's still the question of adding
a scalar to every element in sparse matrix, which is not allowed for sparse
matrices, and which is not possible to avoid in the equation.
Sincerely,
Basil Beirouti
> On Jul 1, 2016, at 4:36 PM, scikit-lea
