Co-authored-by: Sylvain MARIE <sylvain.marie@se.com>
Co-authored-by: Thomas J Fan <thomasjpfan@gmail.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Joel Nothman <joel.nothman@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com>
Co-authored-by: Tom Dupré la Tour <tom.dupre-la-tour@m4x.org>
* ENH: Add regularization to the main NMF class
* Update _nmf with suggestions from code review
Update `_nmf.py` with suggestion from review
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
* Refactor tests, fix linter errors
* Change default value to None
* Revert back to default value of "both"
* Update default value documentation acc to @thomasjpfan
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* CLN Places regularization at the end
* Add whatsnew entry
* DOC Fix
* DOC Fix
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* init varimax
* fix py2 and add example text
* fix print again
* test, docs
* fix test
* fix doctstring
* np.allclose for test
* add refrence
* address comments
* update
* Fix linting failure.
* Fix deprecation warning.
* a bit of cleanup
* Address @jnothman's review comments.
* Update section divider
* Add note in what's new page and versionadded to docstring.
Co-authored-by: Eric Prestat <eric.prestat@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* ENH: Add positivity option for code and dictionary
Provides an option for dictionary learning to positively constrain the
dictionary and the sparse code. This is useful in applications of
dictionary learning where the data is know to be positive (e.g. images),
but the sparsity constraint that dictionary learning has is better
suited for factorizing the data in contrast to other positively
constrained factorization techniques like NMF, which may not be
similarly sparse.
* TST: Test positivity with code and dictionary
Ensure that when the positivity constraint is applied that the
dictionary and code end up having only positive values in the respective
results depending on whether dictionary and/or code are positively
constrained.
* DOC: Positivity constraints dictionary learning
Shows the various positivity constraints on dictionary learning and what
the results of these look like using a Red to Blue color map. These are
included in the examples and also in the docs below dictionary learning.
All of these use the Olivetti faces as a training set.
* update default LDA method to batch
* update tests to match default LDA learning scheme
* DOC trim information on faster learning scheme with LDA
* Add deprecation warning and revert learning_method back to the default
After the discussion, it was decided that a proper deprecation cycle
for the parameter is required. Therefore, in this PR only the warning is
added. The default learning_method will be changed to 'batch' in 0.19.
* Revert back tests to the default LDA
* DOC deprecate in 0.20 instead of 0.19
Oleksandr Husak
Hanmin Qin