* Set lower_bound_ to max lower bound at the end of BaseMixture.fit(), fixes#10869
* Use a local lower_bound variable rather than self.lower_bound_ during training, in BaseMixture.fit()
* Remove extra empty line
* Update documentation and reduce test_init() iterations from 100 to 25
* Update whats_new/v0.20.rst to add mention of issue 10869, and reformat file to fit on 80 columns
* Remove extra line in whats_new/v0.20.rst
* Add tests for convergence detection in Gaussian mixtures when warm_start=True
* Remove unnecessary catch_exception blocks
* Fix tests since n_iter_ was recently fixed to be increased by 1
* Revert changes unrelated to PR 10870 in doc/whats_new/v0.20.rst
* Replace assert_* with plain asserts because of the move to pytest
* Remove comment in whats_new/v0.20.rst that will be added upon merging
* Replace single backticks with double backticks in doc string
* Limit is 79 characters per line, not 80.
* Remove the false convergence fix to treat it in a separate PR
* rm dupes
* add check_supervised_y_no_nan in classifier checks: this implies changes for Ridge classifiers
* fix docstrings/comments
* FIX check fitting 1d X array raises error and FIX check fitting 2d array with only 1 feature either works or returns informative message
* modify check_fit2d_1sample in common tests so that it checks fitting either works or returns an informative message
* rm SpectralClustering case for the moment
* uniformize error messages for 1 sample case and fix SpectralClustering with ensure_min_samples=2
* add unit test for mean_shift when n_samples * quantile < 1
* FIX travis with ensure_min_samples=2 in _PLS
* try fix for failing tSNE test
* typos
* take @agramfort's review into account
* sc to fix string in gaussian_process
* add the class that is present to preserve information of previous message in gpc.py
Use a sparse matrix representation of the neighbors.
Re-factored the QuadTree implementation to avoid insertion errors.
Various fixes in the gradient descent schedule to get the Barnes Hut and exact solvers to behave more robustly and consistently.
* we do not transform y
* more
* added Deprecation Warning to transform() to remove Y parameter
* more
* ENH ensure FunctionTransformer's transform/inverse_transform doesn't permit y
* Undo changes to pls_. It will be done in a separate PR (see #9160)
* flake8
* Update whatsnew
* Fully undo PLS changes
* Add the new BayesianGaussianMixture class.
Add the test file for the BayesianGaussianMixture.
* Add the use of the cholesky decomposition of the precision matrix.
* Fix some bugs.
* Modification of GaussianMixture class.
The purpose here is to prepare the integration of BayesianGaussianMixture.
* Fix comments.
* Modification of the Docstring.
* Add license and author.
* Fix pb typo of eq 10.64 and 10.62.
* Correct VBGMM bugs.
* Fix full version.
* Fix the precision normalisation pb.
* Fix all cov_type algo for BayesianGaussianMixture.
* Optimisation of spherical and diag computation.
* Code simplification.
* Check the Gaussian Mixture tests are ok.
* Add test.
* Add new tests for BayesianGaussianMixture and GaussianMixture.
* Add the bayesian_gaussian_example and the doc.
* Fix comments.
* Fix review comments and add license and author.
* Fix test compare covar type.
* Fix reviews.
* Fix tests.
* Fix review comments.
* Correct reviews.
* Fix travis pb.
* Fix circleci pb.
* Fix review comments.
* Fix typo.
* Fix comments.
Add reg_covar and what's new.
* Fix comments.
* Fix comments.
* [ci skip] Correct legend.
* Modification of GaussianMixture class.
The purpose here is to prepare the integration of BayesianGaussianMixture.
* Fix comments.
* Modification of the Docstring.
* Add license and author.
* Fix review and add tests for init.
Depreciation of the GMM class.
Modification of the GaussianMixture class.
Some functions from the original GSoC code have been removed, renamed or simplified.
Some new functions have been introduced (as the 'check_parameters' function).
Some parameters names have been changed :
- covars_ -> covariances_ : to be coherent with sklearn/covariances
Addition of the parameter 'warm_start' allowing to fit data by using the previous computation.
The old examples have been modified to replace the deprecated GMM class by the new GaussianMixture class.
Every exemple use the eigenvectors norm to solve the scale ellipse problem (Issues 6548).
Correction of all commentaries from the PR
- Rename MixtureBase -> BaseMixture
- Remove n_features_
- Fix some problems
- Add some tests
Correction of the bic/aic test.
Fix the test_check_means and test_check_covariances.
Remove all references to the deprecated GMM class.
Remove initialized_.
Add and correct docstring.
Correct the order of random_state.
Fix small typo.
Some fix in prevision of the integration of the new BayesianGaussianMixture class.
Modification in preparation of the integration of the BayesianGaussianMixture class.
Add 'best_n_iter' attribute.
Fix some bugs and tests.
Change the parameter order in the documentation.
Change best_n_iter_ name to n_iter_.
Fix of the warm_start problem.
Fix the divergence error message.
Correction of the random state init in the test file.
Fix the testing problems.
Update and add comments into the monotonic test.
Andreas Mueller