[scikit-learn] annotated tag debian/0.9.0.dfsg-1 created (now 6569b2d)
Andreas Tille
tille at debian.org
Wed Dec 28 13:11:18 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a change to annotated tag debian/0.9.0.dfsg-1
in repository scikit-learn.
at 6569b2d (tag)
tagging bfd36aa504078ce58f727f7f37e17349ab290e7d (commit)
replaces debian/0.8.1.dfsg-1
tagged by Yaroslav Halchenko
on Wed Oct 19 16:54:31 2011 -0400
- Log -----------------------------------------------------------------
Debian release 0.9.0.dfsg-1
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Alexandre Gramfort (67):
pep8
ENH : using make_blobs in plot_affinity_propagation
ENH : using make_blobs in plot_mean_shift
ENH : using make_blobs in plot_mini_batch_kmeans
FIX : removing useless seed fix in plot_mean_shift
Merge pull request #178 from kwgoodman/master
Merge pull request #181 from lucaswiman/master
prettify plot_sparse_pca.py
adding authors in sparse pca
ENH : prettify dict learn example on image patches
pep8
prettify plot_sparse_pca.py
adding authors in sparse pca
FIX : using product form utils.fixes for python 2.5
pep8
MISC : fix docstring, cosmit in image.py
FIX; missing import in dict_learning.py (OMP in transform in not tested
ENH : new radius_neighbors_graph to build graph of nearest neighbor from radius
DOC: adding radius_neighbors_graph to doc
pep8
Merge pull request #230 from agramfort/radius_neighbors_graph
pep8
FIX : fix failing test in comparison between lassoCD and lars
pyflakes warnings
pep8
DOC: adding note on glmnet parameter correspondance in ElasticNet
ENH : adding LASSO model selection example based on BIC and AIC
BUG: s/empty/zeros in plot_lasso_bic_aic.py
pep8
Merge pull request #265 from JeanKossaifi/master
API : renaming LARS to Lars
MISC: s/larslasso_results/lars_lasso_results
pep8
Merge branch 'master' into rename_lars
Merge branch 'master' of github.com:scikit-learn/scikit-learn into rename_lars
Merge branch 'master' of github.com:scikit-learn/scikit-learn into rename_lars
ENH: adding LARS and LassoLARS deprecated classes
Merge pull request #278 from agramfort/rename_lars
Merge pull request #281 from glouppe/master
pep8
ENH : prettify OMP/LARS benchmark
Merge pull request #277 from vene/omp
ENH: speed up estimate_bandwidth with BallTree + use make_blobs in test_mean_shift.py
ENH : using make_blobs in cluster examples
pep8
FIX : using product form utils.fixes for python 2.5
pep8
MISC : fix docstring, cosmit in image.py
Merge pull request #295 from bdholt1/boston
DOC : fix doc building
ENH : new LassoLarsIC estimator
MISC : adding GaelVaroquaux to the authors of least_angle.py
ENH: addressing @ogrisel's comments on PR 298
ENH + DOC: addressing @GaelVaroquaux's comments
DOC: clarify doc on BIC/AIC
Merge branch 'master' of github.com:scikit-learn/scikit-learn into normalize_data
Style + typos
API : adding proper normalize options in Lasso and ElasticNet with clean up
ENH : more standard import of scipy.sparse
FIX : fix rounding error in test + pep8
FIX : putting back common.py
FIX : in meanshift typos, style, example
Merge pull request #346 from npinto/patch-1
DOC : fix sgd docstring
ENH : better plot_img_denoising
Merge pull request #350 from tinyclues/master
STY : pep8
Alexandre Passos (83):
The derivation of the variational algorithm for the DP mixture of gaussians
Beginning the code; so far only doing the E step
First draft of the code; untested
The dp is already fitting properly
Fixing indentation bug
Changing the DP derivation to rst---equations don't work
Fixed the math
Removing useless whitespace between methods
Reorganizing the directory structure
Adding variational inference for a finite gaussian mixture model
I'm returning precision, not covariance matrices. Make that clear
Editing the documentation
Making it clear that the covariances don't work
Merge branch 'master' into variational-infinite-gmm
Fixing small bug
Adding example; adding explicit lower bound computation; optionally monitoring convergence; full and tied work, somehow spherical and diag diverge.
Using a smaller example to speed things up
Simplifying the code a bit
Fixing last bugs in the bound and updates; improving docs
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into variational-infinite-gmm
Fix docstring find&replace issue; restoring VBGMM
Adding reference in the derivation
pep8 dpgmm.py
Fixing test failures in mixture
Fixing pyflakes warnings
Adding complexity note to the documentation
Replacing DP by dirichlet process
Don't use np.linalg
Explaining what is dpgmm
Adding see also sections to the mixture models
Fix the 'give' in plot-dpmm
Editing a single example for the GMM and DPGMM explaining the difference
Making the documentation findable
Editing the documentation substantially
Adding doc to VBGMM
Adding usage note to dp-derivation
Adding some test coverage. For some odd reason some tests fail on 'make test' but pass on 'nosetests scikits/learn/tests/test_mixture.py'. Any idea why?
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into variational-infinite-gmm
Fixing the docs
Changing the image url in the doc
Even seeding the RNG in setup_func doesn't make the tests consistent
There was a bug in the setup, now things are working deterministically
Deleting stray print statement
Adding an rng parameter to the GMM classes
Fixing the imports
Inlining the helper norms
Beginning to vectorize the code
more vectorizing
Finish removing quadratic dependence on n_states; update docs
Adding norm to scikits.learn.base, using that
Putting norm in utils
Vectorizing parts of the VBGMM, which I had skipped due to it being a lot less useful than DPGMM
Incorporating some caching and vectorizing to improve performance as per line profiles
Fixing typo bug
Caching another computation
Small typo bug in _bound_z
a no-op that fixes tests
Change monitor to verbose, better output
Fixing typo-bug in the full covar update. There are still a couple of nondeterministic bugs to be taken care of
Making test_sample stop failing for no reason
Removing the square from norm() and creating helper sqnorm() in dpgmm
Prevent setting the covariance parameters
Caching the computation of the constant part on _bound_pxgivenz
Caching part of the bound for diag that was missing
moving some parameters from fit to __init__.
Merge branch 'variational-infinite-gmm' of https://github.com/GaelVaroquaux/scikit-learn into gael-variational
Fixing the names in the hmm test
Merging gael's branch
Merge branch 'variational-infinite-gmm' of https://github.com/GaelVaroquaux/scikit-learn into variational-infinite-gmm
Renaming bound_pxgivenz
Renaming covar to prec
Finishing the renamings
Adding a squiggly curve example for the mixture models
Improving the coverage of dpgmm
Testing lognormalize
Splitting test_mixture
Preventing underflow in wishart_logz
Fixing 0* problem in z log z
Fixing another underflow bug in digamma. Now the bound for spherical covariance never diverges as a cluster gets empty
Also, no warnings when running these tests
Fixing test failures resulting from the merge
Fixing some under and overflows; this doesn't fix all test errors yet
Removing some more underflows, still not all
Alexis Metaireau (3):
fix a typo in neighbors docs
fix restructured text problems in the developers doc
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
Amit Aides (8):
Added Multinomial Naive Bayes classifier
Fix to the documentation of the Multinomial Naive Bayes.
Pep 8 compliance and cleanup for the multinomial naive bayes
Merge remote branch 'upstream/master'
Some more pep8
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
Merge remote branch 'upstream/master'
naive bayes name change MNNB->MultinomialNB
Andreas Mueller (21):
Fixed issue 82: bug in init of Kmeans.
Minor documentation: how passing a callable for init works.
Changed default initialization method to "k-means++" for consistency with k_means
k-means clustering test: changed data points to be far away from zero. Now
transpose data on input and sources on output.
Adjusted examples to new ICA interface
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
typo
I don't really understand this, but it makes the error go away.
Added warning to fastica
pep8
fixed bug
pep8
typo
mention LDA in docstring
print docstring in examples
typo
pep8 and starting with X in right shape
rst fix
Notes on Fortran-ordering in fastica
test for vectorizer_inverse_transform
Brian Holt (12):
Added the Boston House Prices dataset
Fixed imports and run unit test
Merge pull request #6 from vene/boston
Corrected import of the data: all 506 columns are now usable
merge
Updated documentation for boston house prices dataset
Merge with master
Fixed memory leak in libsvm
raise ValueErrors with appropriate messages
Standardise error messages
Further clarification of error messages
Merge branch 'upstream-master' into crossval
Bryan Silverthorn (3):
Test KernelPCA support for n_components.
Add support for n_components in KernelPCA.
PEP8 fix.
Conrad Lee (30):
Modified learn.cluster.mean_shift_.py so that the mean_shift function uses a KDTree to efficiently calculate distance within some threshold. KDTree implementation is in C and is from scipy.spatial. Tested only using the example located in examples/cluster/plot_mean_shift.py
Added another variant of mean shift clustering (in scikits/learn/cluster/mean_shift_.py that seeds using a binning technique on a grid.
Modified learn.cluster.mean_shift_.py in the following ways: Replaced old seeding strategy with bucket strategy which should be scalable. Modified nearest neighbor lookup to make it more scalable by adding a maximum number of neighbors -- in most cases this will not make a difference in the results --- the impact of this change is tunable with the max_per_kernel parameter. It is now possible to force all points to belong to a cluster (default) or only those points that are within t [...]
Modified learn/cluster/mean_shift_.py in the following ways: Added more efficent and proper removal of duplicate clusters. Took seed detection out of mean_shift function and put it in its own function. Default bucket size for seed detection is now the bandwidth.
Made following changes to cluster.mean_shift_.py: Added documentation for new functions. Made following changes to cluster.__init__.py: this module now imports the get_bucket_seeds function from mean_shift_.py
scikits.learn.cluster.mean_shift_.py modified in the following way: improved documentation
Changed plot_mean_shift.py example to use larger data set to show how bandwidth estimation dominates the runtime.
Changed scikits.learn.cluster.mean_shift.py: Updated reference for mean_shift algorithm
Changed scikits.learn.cluster.mean_shift.py: Added Conrad Lee as author.
Changed scikits.learn.cluster.mean_shift: modified so that complies with pep8.
Changed scikits.learn.cluster.__init__.py and examples/cluster/plot_mean_shift.py: modified so that complies with pep8.
Changed scikits.learn.cluster.mean_shift_.py: Now uses BallTree because of built in query_radius function, allowing us to get rid of the get_points_within_range function. Changed MeanShift to not use bucket seeding by default.
Hard coded bandwidth to 1.30 because otherwise its calculation is too slow.
Changed scikits.learn.cluster.mean_shift_.py: now uses blas nrm2 to compute norm.
Modified file scikits.learn.cluster.mean_shift_ Replaced a list comprehension and a for loop with numpy operations to improve efficiency.
Modified file scikits.learn.cluster.mean_shift_: removed print lines used for debugging, made code compliant with pep8
Modified file scikits.examples.plot_mean_shift.py: updated reference.
Mean shift: now uses norm function from utils.extmath
Mean shift: removed obsolete reference to KD-Tree with reference to BallTree
Removed obsolete import of izip, made description of complexity more concise and accurate
Mean shift: settled on term 'bin' and removed unnecessary references to 'bucketing' or 'discretization' from variable names and documentation
Mean shift: Fixed a minor type
Mean shift: Moved a test file in preparation for merge with agramfort's branch
Merged agramfort's branch with my own
Mean shift: removed my old test script due to merge with agramfort, changed num points in plot example to ten thousand to speed it up.
Brought my branch for mean shift modification up to date with current head on github
Mean shift: modified get_bin_seeds so that it no longer has to copy all points
Mean shift: Fixed a bug that occurs when the cluster_all argument is False
Merge remote-tracking branch 'upstream/master'
Mean shift: fixed bug introduced during upstream merge
David Warde-Farley (17):
Rephrase motivation for Sparse PCA
Misc rephrasings of sparse PCA docs.
Remove 'structured sparsity not implemented' comment
Prefix explanation of sparse PCA formulation with 'Note that'
atoms -> components for clarity
Trailing whitespace fix.
Rewording in docstring
gradient descent -> coordinate descent in docstring
'Returns' section of the _update_code docstring
Wrap np.seterr reset in a try..finally block
ImporError -> ImportError
Added loader code for (Roweis) Olivetti faces dataset.
Added imports to __init__.py for Olivetti faces
Documentation for the Olivetti Faces dataset.
Remove 'load_' alias for 'fetch_'
Use prints for now instead of logging at Gael's request
Add a shuffle keyword, default False
Emmanuelle Gouillart (1):
In spectral clustering example, forced the solver to be arpack
Fabian Pedregosa (155):
Initial implementation of Locally Linear Embedding.
pep8 clean.
Start of 0.9 development cycle
Welcome Lars & Edouard.
FIX: pls docstring.
DOC: added section on complexity for LLE.
Rename embdding_vectors_ --> embedding_
Add submodule for manifold.
Cosmetic changes.
Merge pull request #3 from GaelVaroquaux/manifold
More on practical tips.
Typo
FIX: bad import
Move cache_size out of model parameters.
Cosmetic changes in the docs.
Docstring for test.
Test for non-contiguous input for svms
Implement predict_proba for sparse svms.
FIX: doctests in svm doc
ENH: support instance of BallTree as input to kneighbors_graph.
Merge branch 'master' of github.com:scikit-learn/scikit-learn into manifold
Implement transform method in LLE.
FIX: fix test.
more fixes.
FIX: fix segfault in cases of infeasible nu (NuSVM)
FIX: transform method.
Merge pull request #153 from fabianp/manifold
FIX: use NeighborsClassifier in test.
FIX: some bugs in locally_linear_embedding.
DOC: remove obsolete information in neighbors.rst
Add max_iter to LARS.
DOC: fix errors in manifold doc + style tweaks.
Explicit cmap in swissroll example.
Add test and cleanup for 2c1c88
Test: test for unnormalized predictors.
Add failing test.
DOC: add reference to FastICA from the ICA docs.
DOC: add fit_intercept to LinearSVC docstring.
Refactoring in ridge.py
Rename of cg -> dense_cg and 'default'-> 'dense_cholesky'.
Some docstring updates.
Move scipy_future into utils.arpack
Add Jake to the mainfold credits.
Merge pull request #222 from jakevdp/balltree-doc
Explicit cmap for plot_compare_methods.
Cosmetic cleanup.
FIX: bad logic in Pipeline.
Revert "FIX: bad logic in Pipeline."
Refactoring in libsvm bindings.
FIX: fix bug in LLE with dense solver
Update ARPACK from scipy.
Backward compatibility fixes for testing LLE.
FIX: arpack doctest
comment LLE arpack test
Protect against MemoryError in libsvm.fit
FIX: doctest Ridge.
FIX: add newline after autosummary:: sphinx directive.
Layout & consistency fixes linear models documentation.
cosmetic linear_model.rst
FIX doc linear_model.rst
Layout tweaks.
DOC: new example for Ridge + more rst docs
Merge pull request #236 from JeanKossaifi/sparse_matrix_type
Don't use np.atleast_2d when interfacing with native code.
Some documentation for hmm module, and a warning.
Revert "pyflakes warnings"
Covariance with residual at the end for path is zero.
FIX: LARS doctest in linear_model.rst
Update rsync command
Merge branch 'variational-infinite-gmm' of https://github.com/GaelVaroquaux/scikit-learn
Replace logsum by np.logaddexpr in hmm, tweaked some tests.
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge pull request #218 from fabianp/fix_lars
Rename n_states --> n_components in mixture & hmm + cosmetic changes.
FIX: support numpy < 1.3
Merge pull request #280 from vene/lars_n_features
Remove max_features keyword from lars_path.
Use default value for n_nonzero_coefs
Remove hardcoded n_jobs from examples.
Revert "Remove hardcoded n_jobs from examples."
Don't use n_jobs=-1 in the examples.
Refactor tests for SVR.
Correct NuSVR impl in the sparse case.
Add tests for last commit.
Remove fit params from all objects.
Merge pull request #316 from jakevdp/cython-ball-tree
Compatibility fixes for Python 2.6 and doctest fixes
FIX: py3k compatibility.
FIX: py3k compat.
Merge pull request #326 from bdholt1/fix/svm
Welcome Brian Holt
FIX: broken example
Generate thumbnails in the example gallery
Link images to example file in new gallery
FIX some broken examples.
Rename face_recognition so that the result is plotted.
Revert "Rename face_recognition so that the result is plotted."
FIX: linnerud dataset mixed variables.
Layout tweaks
Cosmetic changes in example docstring
layout tweaks
Move project directory from scikits.learn to sklearn
Add a compatibility layer for some modules.
Forgot to add a blank image for the docs.
Revert "Add a compatibility layer for some modules."
Revert "Move project directory from scikits.learn to sklearn"
Move project directory from scikits.learn to sklearn
Add a compatibility layer for some modules.
correct imports
Merge pull request #335 from fabianp/rename
Add more modules for compatibility layer.
More renaming.
scipy.lena() no longer works on scipy's dev version.
FIX: fix variable referenced before assignement in libsvm.pyx
Do not import mixture from top-level sklearn.
DOC: add parameter C to docstring.
Use LinearSVC's docstring instead of outdated one.
scipy.lena has moved to scipy.misc.lena in scipy's dev version.
Use 1 / n_features as value for gamma.
FIX broken tests by last commit.
Add changelog for changing gamma parameter.
FIX example logisitic regression.
Move matrix factorization to work in progress.
Initial changelog -- to be completed.
More changelog and .mailmap
Why, emacs, why ??
Update changelog
DOC: broken link to example
FIX: add test fixtures to distribution.
FIX: broken link to example
DOC: always generate pages for autosummary.
FIX: some sklear.test() fixes.
Add Vlad as GSOCer
Complete Changelog.
FIX: import path under scipy's dev version.
Comment tests that depend on PIL.
Comment out tests for the current release.
FIX typo
FIX: docstring for RadiusNeighborsRegressor
sklearn.test() does not like doctest that don't print.
Doc: Print --> Issue
Safer assert_all_finite.
Some more doctest fixes for sklear.test()
Update commiters list
0.9 release
Some Python 2.5 fixes.
Comment out non-python2.5 test
More python2.5 fixes
FIX: assign NaN to an integer array has no effect on old numpy
Some more changelog stuff.
Update MANIFEST.in: scikit-learn --> sklearn
Revert "Comment out non-python2.5 test"
Comment out test non compat with python2.5
Add mldata loader and olivetti dataset to changelog.
Comment out doctest for fetch_mldata
Gael Varoquaux (174):
DOC: Tweak DPGMM docs
COSMIT: lognormalize->log_normalize
COSMIT: avoid 'import as'
COSMITs
COSMIT: avoid one-liner
COSMIT: Local imports last
Revert "COSMIT: avoid one-liner"
MISC: move mixture's test to sub directory
COSMIT
COSMIT
COSMIT
ENH: Vectorize bound computing
ENH: Speed up _bound_z in DPGMM
COSMIT: DPGMM: Move bound computing to functions
ENH: Speed improvements in DPGMM
DOC: Improve the GMM vs DPGMM example
BUG: Fix bug introduced by moving test_mixture
DOC: Fix layout
DOC: Tweaks in mixture
BUG: fix testing (heisen) bugs in hmm
TEST: Heisen-bug fixing
MISC: Prettify GMM example
COSMIT: Pep 8 and remove useless imports
ENH: avoid useless computation and warnings
BUG: Make SVMs work on non contiguous arrays
COSMIT: Better fix for continuity in SVMs.
MISC: Recythonize the ball_tree
DOC: Fix links in covariance
MISC: Remove unused imports
ENH: Make LLE work with older PyAMG
MISC: Prettify the swiss roll example
COSMIT: Remove unused import
DOC: Prettify MiniBatchKmean example
BUG: Fix pyflakes warning in k_means
BUG: params not applied in MiniBatchKMeans
ENH: MiniBatchKMeans: avoid useless computation
BUG: MiniBatchKMeans: Error in stopping criteria
MISC: Cosmit in k_means_
DOC: cosmit in MiniBatchKMeans docs
TEST: Control seed in fastica tests
ENH: Capture different data length in grid_search
BUG: Avoid NaNs in lars_path
ENH: add pre_dispatch to GridSearchCV
ENH: Catter for lists in grid_search
Merge pull request #185 from amueller/master
BUG: Minor bugs in cross_val
Merge pull request #203 from amueller/docs_fix_again
Python2.5 compatibility
BUG: explicit imports in doctests
ENH: Lasso and LassoCV: fit params -> class params
DOC: Tweak the cross-val lasso path example
ENH: LARS: fit_params -> class params
COSMIT: Minor refactor in lars_path
ENH: Add a ShuffleSplit cross-validation iterator
BUG: Fix bug introduced in 83cf11c
Merge pull request #216 from ametaireau/master
BUG: change alpha scaling in LassoLARS
BUG: LassoLARS: X: modified during the normalization
BUG: LassoLARS didn't renormalize the coefs
ENH: Update joblib to 0.5.2
ENH: Update joblib
MISC: minor cleanups
ENH: Add small info on diabetes
COSMIT: Simplify the lena ward example
Cosmit
Merge pull request #247 from NelleV/FIX_doc
Cosmit
ENH: l1_distance: gaussian_process -> metrics
Doc: fix minor error in docstring
DOC: sparse_pca: put maths at the end
DOC on sparse_pca
DOC: Add l1_distances to classes.rst
TEST: faster tests, and more coverage
Merge pull request #248 from dwf/misc_fixes
BUG: Fix gmm bug + test failures
FIX/ENH: numerical stability in GMM
Comsit: PEP8
Cosmit: remove useless comments
MISC: Restructure compound decomposition example
TEST: SparsePCA: testing fit_transform useless
TEST: testing HMM more robust
Merge pull request #212 from vene/sparsepca
ENH: mixture: better numerical stability
Cosmit: Fix (some) pyflakes warnings
ENH: olivetti faces: control RNG in shuffle
DOC: Add a descr to olivetti_faces
DOC: fix some formating issues
DOC: Fix layout
DOC: fix layout
DOC: fix layout
Add forgotten 'install' for mixture.
BUG: Fix clone for ndarrays and sparse matrices
BUG: Fix clone for nadrrays and sparse matrices
Removing unused code
ENH: Avoid np.logaddexp.reduce
DOC: more precisions in univariate_selection
Merge pull request #266 from glouppe/master
BUG: fix dotests
DOC: stress that only chi2 works with sparse
COSMIT: remove unused import
DOC: Improve the Bayesian regression docs
Typo
Sorry, other typo
Merge pull request #279 from JeanKossaifi/master
ENH: Add a subset="all" to 20news
API: load_20newsgroups is depreciated
Cosmit
API+ENH: load data by default in mlcomp and 20news
ENH: compression in 20newsgroup caching
DOC: leftover false info in docstrings
DOC: load_filenames -> load_files
DOC: Link the Olivetti docs in the main docs
DOC: more explicit docs on alpha/rho in elasticnet
ENH: cv objects created by a helper function
COSMIT: fix doc indentation to PEP8
BUG+COSMIT: rewamp the lasso path examples
ENH: Add a LassoCV using LARS
COSMIT: Nobody expects the PEP8 inquisition
API: add import paths for LarsCV and LassoLarsCV
MISC: Follow changes to alpha scaling
ENH: Add normalization of X to LarsCV
BUG: Propagate fix 086b58f5 to LassoLarsCV
DOC: LARS docstring
BUG: Avoid div by 0 in lars_path_residues
ENH: Expose eps in LARS
DOC: Tweak the bayesian ridge docs
DOC+TEST: LarsCV
TEST: Improve test coverage of LarsCV
DOC: document eps in least_angle better
MISC: LarsCV: preallocate mse_path
ENH: use _check_cv in LassoLarsCV
DOC; fix documentation
MISC: mse_path in LassoLarsCV is now the mean
DOC: add example comparing LassoCV and LassoLarsCV
DOC: typos
API: _check_cv -> check_cv
Merge remote branch 'jakevdp/kernelpca-arpack'
TEST: Robustify LLE tests
BUG: Fix a bug introduced in rebasing
BUG: normalize before center in lars_path_residue
DOC: cosmetic changes to lars-bic doc and examples
DOC: make lasso docs easier to read
COSMIT: remove unused import
BUG: make lobpcg work with non-sparse matrices
COSMIT: tweak plot_compare_methods example layout
COSMIT: print time in plot_lle_digits example
MISC: fix image in manifold doc
MISC: prettify the faces example
COSMIT: doc and examples in decomposition
Merge pull request #314 from emmanuelle/spectral
ENH: More interesting benchmarks for OMP
API: eps -> tol in bayes
Merge pull request #317 from agramfort/normalize_data
Merge pull request #318 from JeanKossaifi/master
DOC: change the name scikits.learn to scikit-learn
Merge pull request #331 from JeanKossaifi/master
DOC: Fix doctest
DOC: scikits.learn -> scikit-learn
DOC: fix link
DOC: scikits.learn -> sklearn
DOC: Minor scikits -> scikit
BUG: sklearn/setup.py : learn -> sklearn
BUG: Backward compatibility layer sklearn.externals
ENH: Add verbosity control to LinearModelCV
BUG: scikits.learn -> sklearn: backward compatibility
COSMIT: PEP08
Unused import
BUG: backward compat: scikits.learn -> sklearn
ENH: add control of n_init in spectral clustering
BUG: scikits.learn -> sklearn backward compat
DOC: larger lena size in denoising example
Cosmit: make in-place modifications explicit
DOC: update whats_new.rst
BUG: ShuffleSplit: repr for random_state not number
Gilles Louppe (57):
DOC: Missing dot in Pipeline class description
Enforce axis=1 in Normalizer.transform + doc fixes
DOC: Fixed issue #110
DOC: Missing import in doctests
BUG: `copy=None` in `Scaler.transform` instead of `copy=False`
Complete rewriting of samples_generator.py
Fixes for broken tests due to the API changes in samples_generator.py (1)
Merge remote-tracking branch 'upstream/master' into samples_generator
Merge remote-tracking branch 'upstream/master' into samples_generator
Fixes for broken tests due to the API changes in samples_generator.py (2)
Fixes for broken benchmarks due to API changes in samples_generator.py
Fixes for broken examples due to changes in samples_generator.py
`seed` renamed to `random_state` and default value set to None.
Added references to functions in the `datasets` module.
Merge remote-tracking branch 'upstream/master' into samples_generator
Fixed a broken test.
Added tests for the samples generator module.
Added references to samples_generator.make_* functions in the documentation.
Small improvements in the documentation of the toy datasets.
dictionnary -> dictionary
Merge remote-tracking branch 'upstream/master'
Improvements of the RFE module.
Merge remote-tracking branch 'upstream/master'
Documentation + PEP8
More robust test on `step`.
Fixed a syntax error
Small code simplification.
Merge remote-tracking branch 'upstream/master'
Improved test coverage of rfe.py to 100%
Fixes of minor bugs + improved test coverage (now 100%)
Addressed Gael's comments.
Addresses Gael's comments. (2)
Addresses Gael's comments. (3)
Typo.
Improved test coverage of samples_generator and feature_extraction modules.
Fixed a small introduced due to a previous commit.
Merge remote-tracking branch 'upstream/master' into test-coverage
Improved documentation + predict/score.
Cosmit
Typo
Typo (2)
Merge remote-tracking branch 'upstream/master'
PEP8
Merge remote-tracking branch 'upstream/master'
Fixed examples
Improved test coverage to 100%
Added RFE into the narrative documentation
Doc: grammar
Added n_features_ attribute to RFE
Moved "feature selection" section back into the "supervised learning" chapter
Ensure 0.0 on diagonal elements if X is Y
Doc: Implementation details of euclidean_distances
Merge pull request #343 from glouppe/euclidean_distances
ENH: `np.fill_diagonal` replaced with more portable code. Added an explanatory comment.
scikits-learn -> sklearn
Added link to personal web page
Changes on the feature_selection module.
Jake VanderPlas (127):
fixed bug in BallTree cython wrapper
fixed small bug in cython wrapper for BallTree
updated ball_tree documentation
Merge commit 'upstream/master'
added MLLE, made some small fixes to manifold module
wrapped brute force neighbor search
added cython wrapper to BallTree.query_ball
query_ball -> query_radius, removed knn_brute
speed up BallTree.h
slight speedups to BallTree.h and ball_tree.pyx
added unit test for BallTree.query_radius
fixed reference-passing bug in BallTree.h
vastly improved MLLE speed
added HLLE code
sped up HLLE code
added ability to return distances and specify multiple search radii for BallTree.query_radius()
fixed r shape bug
Merge branch 'manifold' of git://github.com/fabianp/scikit-learn into manifold
pep8 changes
cosmetic changes
added arpack support in scipy_future; wrapped MLLE and HLLE into locally_linear function
removed old files; moved example to examples directory
pep8 changes
added LTSA method
pep8
fixed bug in modified LLE: now works for higher dimensions
added method argument to digits example
Merge pull request #1 from ogrisel/jakevdp-manifold
minor changes
NeighborsClassifier: changed window_size to leaf_size & updated documentation as discussed in Issue #195
fixed doc formatting
merged with sparse classifier commit
merged changes in master
pep8
merge with previous commits
H_tol/M_tol -> hessian_tol/modified_tol
Initial commit
fixed bug in calculating tau
added cythonized Floyd-Warshall algorithm
speed tweaks in Floyd-Warshall, and renamed graph_search->shortest_path
speedup in Floyd-Warshall: unsigned ints to prevent negativity checks
Added Dijkstra's algorithm with Fibonacci Heaps for significant speed gains in path searches
bug fix: free allocated memory
changed shortest_path() to accept a sparse distance matrix for more flexibility
cleanups & pep8
add tests, doc update
combined manifold examples
manifold doc update
Revert "combined manifold examples"
fixed bug in shortest path; consolodated isomap examples
ex. change
Merge branch 'manifold-test' into manifold-doc
cleaned up and documented Fibonacci code
added tests; cleanup; pep8
remove unused imports
first stab at implementation via KernelPCA
add arpack support to KernelPCA
small efficiency boost to KernelCenterer
np.random -> RandomState
K_pred_cols -> K_pred_cols_
Merge branch 'master' into manifold-isomap
manifold/shortest_path -> utils/graph_shortest_path
Implement Isomap + transform in terms of KernelPCA
add description to isomap transform
added Isomap.reconstruction_error()
store BallTree in Isomap for faster transform()
fix conflicts with master
Merge branch 'manifold-isomap' into manifold-doc
update manifold documentation
Merge commit 'upstream/master' into manifold-doc
changes to manifold doc
speed improvements on LLE variants for high dimensional data
manifold example updates
typo in HLLE
examples: make out_dim explicit
remove lobpcg from LocallyLinearEmbedding
merge with master; remove lobpcg references
initial commit
added compiled cython
assure C-ordered on init
fix NeighborsClassifier doctest
make memory allocation more efficient
documentation clarifications
ball_tree protocol 2, but paths are broken
Merge branch 'cython-ball-tree'
move ball_tree.pyx to scikits/learn/ and write pickle test
Merge commit 'upstream-RW/master' into cython-ball-tree
add BallTree pickle test cases
Merge branch 'cython-ball-tree'
refactor neighbors module
doc fixes
merge with upstream/master
Merge commit 'upstream/master' into neighbors-refactor
scikits.learn -> sklearn
add neighbors benchmark
change implementation to mixin pattern
move neighbors.py -> neighbors
fix doctests
merge upstream/master
move barycenter_weights to manifold
deprecation of NeighborsClassifier and NeighborsRegressor
Merge commit 'upstream/master' into neighbors-refactor
add deprecation warning to sklearn.ball_tree
Note neighbors module changes in doc/whats_new.rst
fix typos
gitignore: scikits.learn -> scikit_learn
Merge commit 'upstream-RW/master'
move neighbors examples to examples/neighbors/
Nearest Neighbors examples & documentation
switch to dynamically generated docstrings
commit dynamic doc changes
add weighting to classification and regression
add neighbors/tools to commit
add tests for weighted regression and classification
documentation of weighted classification and regression
add graphical neighbors benchmark
pep8 + move weighted_mode to utils
add tests & example for weighted_mode
benchmark -> bar plot
make constants uppercase
return to simple docstrings
increase BallTree test coverage
fix BallTree linkage
fix typos
Merge pull request #3 from ogrisel/jakevdp-neighbors-refactor
increase test coverage
pep8 + cosmetic changes
Jean Kossaifi (32):
Changed the default return type of ward_tree from bool to int
adding a comment on the test for grid_to_graph
pep8 and using np.bool instead of bool
FIX : _to_graph failed if mask's data was not of type bool
Test to check that the grid_to_graph function works with every type of
COSMIT : used implicit continuation inside parenthesis instead of
Typo : fix the 0.5 coefficient
Added normalize parameter to LinearModel
Added parameter normalize to LinearRegression
LassoLARS now uses the normalize parameter
Completed the integration of the parameter normalize
Implementation of the parameter normalize in bayes.py
added parameter normalize to coordinate_descent
added parameter normalize to ridge.py
Added parameter normalize to omp.py
Added parameter normalize
Fixed some errors (mainly docstrings)
Merge remote branch 'upstream/master' into normalize_data
Added a function as_float_array in scikits.learn.utils
Fix : deleted a forgotten line
FIX : corrected a bug in as_float_array and added a test function
PEP8 : replaced tabulations by spaces
FIX : if X is already of the good type, we musn't modify it
FIX : if X.dtype is changed, then a copy of X is returned, even if overwrite_X is True
Test : lasso_lars_vs_lasso_*
Merge branch 'normalize_data'
FIX : Ellipsis in least_angle.py doctests
FIX : ELLIPSIS in least_angle.py doctests
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
Sorting parameters in BaseEstimtor.__repr__
FIX : docstest fail
Cross_val : Removed useless & tricky parameter iid
Keith Goodman (1):
DOC: minor typos in covariance doc.
Lars Buitinck (190):
cosmetic change to ball tree C++ code
cosmetic doc changes
cosmetic: pep8 in utils/ + rewrote factorial (2x as fast)
factorial should not use O(n) memory
Python 3-safe attempted import of factorial and combinations
typos in README
typos in covariance docs
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
Merge branch 'master' of https://github.com/amitibo/scikit-learn into amitibo-naive-bayes
naive bayes: copyedit + rename alpha_i to alpha
ENH: optional and user-settable priors in multinom naive bayes
naive bayes: minor fixes
Merge sparse and vanilla naive Bayes
docs + cosmit in naive_bayes
naive bayes: handle 1-d input
ball tree cleanup & 64-bit safety
naive bayes: fix predict_proba bug and change priors behavior
fix naive bayes docs and example + credit mblondel + vanity
typo: interation/iteration + re-Cythonize cd_fast.pyx
Merge branch 'master' of github.com:scikit-learn/scikit-learn
naive bayes: test pickling
naive bayes: safe_sparse_dot, doc and docstring updates
rename MultinomialNB params, rename GNB GaussianNB
reformulate MultinomialNB as linear classifier
NB: add class_log_prior_ and feature_log_prob_ back as properties
NB cosmit: *feature* independence
cosmit: expand MultinomialNB docstring
Safer importing in grid_search module
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge pull request #184 from larsmans/amitibo-naive-bayes
rm references to naive_bayes.sparse in docs
NB: rename use_prior to fit_prior
slightly improved logging in a few easy cases
rm self.sparse attr in MultinomialNB; not needed outside of fit
fix priors bug in MultinomialNB
2010 is so last year
Merge branch 'mldata' of https://github.com/pberkes/scikit-learn into pberkes-mldata
Improved error handling + reduce memory use
Simplify intercept fitting in MultinomialNB
Error in MultinomialNB docs
Added naive Bayes classifier for multivariate Bernoulli models
some documentation for BernoulliNB
Do binarizing in BernoulliNB
Simplify binarizing in BernoulliNB
fix message in document classification example
Merge branch 'master' into bernoulli-naive-bayes
Optimize BernoulliNB + improve docstring + add to doc-class example
Copyedit preprocessing docs
Refactor MultinomialNB: separate prior estimation and feature counting
Use unique from utils.fixes in naive_bayes
Fix bug in MultinomialNB: output transposed
Replace loop in MultinomialNB._count with dot product + pep8
BUG: binary classification failed in MultinomialNB, +regression test
Fix 404 from broken URL in release log
ENH: fit_transform on TfidfTransformer
add C parameter to LinearSVC docstring
Fix pprett's website URL (<> caused it to be a relative URL)
Merge branch 'master' into bernoulli-naive-bayes
Refactor MultinomialNB and BernoulliNB: introduce BaseDiscreteNB
vectorize loop in BernoulliNB for 100x speedup in sparse case
svmlight reader: don't use leading _ in identifiers
Merge branch 'svmlight_format' of git://github.com/mblondel/scikit-learn into mblondel-svmlight
SVMlight reader: minor fixes
SVMlight reader: ensure C calling conventions + docstring
Plumb memory leak in SVMlight reader
SVMlight reader: one more clear() instead of delete
SVMlight reader: cosmetic
SVMlight reader: skip one level of indirection
Simplify and document SVMlight/libSVM data reader
Use C++ exception handling in SVMlight reader.
finish exception handling in SVMlight reader
Extend MultinomialNB tests to BernoulliNB
Update BernoulliNB docs
BUG: broken doctest in BernoulliNB
Glitches in BernoulliNB and DiscreteNB (mostly docs)
Merge pull request #210 from larsmans/bernoulli-naive-bayes
SVMlight reader: memory leak, type test
(Hopefully) full exception safety in SVMlight reader
datasets/mldata.py is not a script, chmod 644
Python 2.5 and SciPy 0.7 (tentative) compat in mldata
Fix broken doctest in mldata
document placement new in SVMlight reader
fit_transform does NOT return self + other docfixes
Parallel vectorizing is slower than serial
Rewrote SVMlight parse_line with C++ iostreams
SVMlight reader: some extra tests + cleanup
Adapt kNN classifier to sparse input
Use new utils.atleast2d_or_csr in naive_bayes as well
document placement new in SVMlight reader
Document sparsity in k-NN
Correctly document sparse input possibilities in naive_bayes
Merge branch 'master' into sparse-knn
Add sparse k-NN test, fix a bug
Extend sparse k-NN test to try pairs of sparse matrix types
Fix bug in sparse k-NN and add disabled (!) test for sparse regression
Better document scipy.sparse support in neighbors module
Prevent some copying in neighbors + docstring for euclidean_distances
Use 10 neighbors in k-NN document classification
neighbors: check string equality with ==, not is
Copyedit SparsePCA docs
Copyedit SparsePCA docs
Merge pull request #219 from larsmans/sparse-knn
Some doc copyediting
Change normalization behavior in TfidfTransformer
Docfixes in feature_extraction.text
Remove bogus sparse vectorizing tests
docfixes in feature_extraction.text
document classification example doesn't demo only linear classifiers anymore
make parse_file in SVMlight reader static
Fix broken doctest in NeighborsRegressor
Search tfidf__norm space in text class. grid search example
Merge pull request #228 from larsmans/tfidf
Use four categories instead of all in doc. class. example
Optimize CountVectorizer.fit_transform (+ minor refactoring)
pep8 feature_extraction.text + rm content word "computer" from stop list
DOC: Expand and copyedit naive Bayes docs
Recythonize libsvm.pyx with Cython 0.14
Refactor/simplify CountVectorizer
Refactor feature_extraction.text (again) to use Counter
Replace mixture.logsum with numpy.logaddexp
on demand inverse vocabulary
Implement fit_transform for Vectorizer as well and document it
Default argument safety + cosmit in feature_extraction.text
typo
DOC fixes in datasets
Merge pull request #234 from larsmans/inverse-vectorizer
FIX hmm.py to succeed tests; stopgap, put old logsum.py in that module
FIX and ENH feature_extraction.text.CountVectorizer
default arg safety + docfixes
Started one-hot transformer
Merge branch 'master' of github.com:scikit-learn/scikit-learn
FIX broken test for CountVectorizer
Revert "Started one-hot transformer"
DOC grid_search + pep8
Refactor naive_bayes and don't treat BernoulliNB as linear model
ENH show top 10 terms per category in document classifier example
DOCFIX typos in svm module
cosmetic changes to DBSCAN
vectorize loop in DBSCAN with np.where
Cosmit DBSCAN test
DOCFIX DBSCAN: we use arrays, not matrices
Streamline imports in lfw.py: don't try anything with PIL
Restore conditional PIL import in datasets.lfw
DOC: copyedit docstrings in pls.py + (almost) pep8-clean
pep8 and docfixes in various modules
pep8 and docfixes for LLE
suppress division by zero warnings from precision, recall, f1
simplify np.seterr handling in sparse_pca
Use isinstance instead of the ancient (Py2.1) types module in fastica
FIX handles NaNs in LogisticRegression, and many more classes
assert_all_finite: pre-check if we're dealing with floats.
Rework assert_all_finite and related functions in utils
callable now actually allowed in fastica
disallow sparse input in dense liblinear
Merge pull request #259 from larsmans/input-validation
Chmod 644 feature_extraction/image.py: not a script
FIX more useful diagnostics in mlcomp_sparse_document_classification.py
Add χ² feature selection
Demo chi2 feature selection on document classification
document Chi2 feature selection
ENH test and fix chi2 feature selection
Rename f_chi2 to chi2
avoid mutable default arguments
s/euclidian/euclidean/g
More mutable default args
ENH decorator to mark functions and classes as deprecated
New-style deprecation of datasets.load_files
deprecated decorator won't work on __init__; skip it
make deprecated work on classes
typo
ENH optimize euclidean_distances for memory use
document and test sparse matrix support in euclidean_distances
ENH optimize idf computation in TfidfTransformer using np.binsort
ENH and DOC TfidfTransformer
FIX add idf smoothing to Vectorizer as well, defaulting to True
More specific exception in GaussianProcess + regression test
(Micro)optimization in DBSCAN
fix DBSCAN bug (oops)
new-style deprecation of load_20newsgroups
ENH set_params method on BaseEstimator, deprecate estimator params to fit
set_params: update according to @GaelVaroquaux's review
Rm k param from KMeans.fit again
DOC improve fbeta docstring
minor fixes in clustering metrics
cosmetic changes to ari_score
rename ari_score adjusted_rand_score
pep8 sklearn/utils/__init__.py
refactor linear models to call as_float_array only from _center_data
unconditionally call as_float_array in LinearModel._center_data
DOC: fix typos
Lucas Wiman (1):
Fix spelling in dosctring.
Mathieu Blondel (120):
Make the author file more consistent.
Merge pull request #167 from bsilverthorn/fix-kernelpca-ncomponents
Add sparse.LogisticRegression to class reference.
Better doc for the dataset loaders.
Make kernels consistent with SVM and add sigmoid kernel.
Fix LDA transform.
Add LDA to the handwritten digit 2d-projection example.
Add TransformerMixin to LDA and RandomizedPCA.
Cosmetics.
Merge pull request #200 from amueller/minor_docs
Merge pull request #193 from ogrisel/preprocessing-simplification
Better PCA docstrings.
Fix LDA.transform's docstring.
Typo.
Add hinge_loss to metrics.
Fast and memory-efficient loader for the svmlight format.
Allow to user to fix n_features.
Docstring.
Important note.
Propagate errors up to the Python level.
Narrative documentation.
Update credits.
Return false when couldn't read the file.
Fix comment.
Merge pull request #6 from larsmans/mblondel-svmlight
Merge branch 'mblondel-svmlight' of git://github.com/larsmans/scikit-learn into svmlight_format
Fix compile issues on Mac OS X.
Fix ref counting bug.
More comments.
Merge pull request #7 from larsmans/mblondel-svmlight
load_svmlight_format -> load_svmlight_file.
Merge branch 'master' into svmlight_format
Merge pull request #209 from mblondel/svmlight_format
Documentation fixes.
Add note to base fit_transform doc.
Raise error if file doesn't exist.
Fix parsing issues.
More tests for the svmlight reader.
Documentation fixes.
Better performance of Ax=b solver when b is 2d and A is sparse, and add
Fix doctest.
Reverse coef_ in Ridge.
Merge pull request #235 from mblondel/fix_ridge
Improve Logistic Regression sparsity example.
Better test and remove old garbage.
Allow CountVectorizer to be fitted twice.
Remove unnecessary submethod.
2011!
squared loss -> squared hinge loss.
Merge pull request #255 from vene/kernel-pca
Merge pull request #260 from glouppe/master
Merge pull request #261 from glouppe/master
Merge branch 'dbscan' of https://github.com/robertlayton/scikit-learn into dbscan
Handle metric="precomputed" in dbscan.
Use euclidean_distances in kmeans.
Cosmit: use dense_output=True.
Sparse matrix support in kernels.
PCA: fix issue #258.
PCA: better doc string for 0 < n_components < 1 case.
Partial support for sparse matrices in kernel PCA.
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Remove unnecessary import.
Merge branch 'dbscan' of git://github.com/robertlayton/scikit-learn into dbscan
calculate_distances -> pairwise_distances + goodies.
Improve DBSCAN doc.
Fix DBSCAN example.
Remove automatically generated auto examples.
Test pickability in DBSCAN.
Test precomputed similarity in pairwise_distances.
Merge branch 'samples_generator' of git://github.com/glouppe/scikit-learn into samples_generator
Doc for sample generator cosmits.
Merge branch 'kmeans_transform2' of https://github.com/robertlayton/scikit-learn into kmeans_transform2
Add tests and fix bug.
Kmeans transform and predict doc improvements.
Merge pull request #296 from bdholt1/fix/feature_extraction
Add TransformerMixin (back?) to preprocessing classes.
Fix plot_kmeans_digits.py.
Typo.
Implement one-vs-the-rest multiclass strategy.
Fix bug in one-vs-rest when underlying estimator uses predict_proba.
Implement one-vs-one multiclass strategy.
Merge pull request #2 from ogrisel/robertlayton-kmeans_transform2
Implement error-correcting output-code multiclass strategy.
Test grid searchability.
Merge pull request #273 from robertlayton/kmeans_transform2
Docstrings!
Add new meta module to setup.py
Merge branch 'master' into multiclass
Check estimator and fix syntax error.
Documentation for the meta learners.
pep8-proof.
Fill missing docstrings.
Allow one-class only in LabelBinarizer.
Rewrite svmlight loader in pure Python for now.
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' into multiclass
Fix mistake and docstring cosmits in SVC.
Moved multiclass module to top-level module.
Fix doc!
Fix setup!
Address @agramfort and @ogrisel's comments.
Merge branch 'master' into multiclass
More informative name for color quantization example.
More explanations and pep8.
Use 256 colors and add title.
Emphasize one-vs-all.
Better documentation.
Fix doctest errors (hopefully!).
Document fit_ecoc.
Typo.
Fix currentmodule.
Fix bad copy-paste.
Merge pull request #320 from mblondel/multiclass
64 colors + random codebook comparison.
Better title + authors.
Welcome to Robert and Gilles.
Sparse matrix support in the `density` util.
Documenting a secret feature and fixing bugs in the process.
Use l1 penalty.
Giving due credit (last minute ChangeLog item).
Minwoo Jake Lee (2):
Merge remote-tracking branch 'master/master' into sparse-mbkm
moved _gen_even_slices to utils/init
Nelle Varoquaux (38):
First draft of the mini batch KMeans - works, but a lot of cleaning up to do
Refactored: deleted the batch_k_means function, and created an option for the batch_k_means to avoid code duplication - Added some documentation
Added test one the batch k_means
Improve documentation
Batch K-Means
[batch k-means] Changed the algorithm to compute the centroids.
[batch k-means] Fixed the computation of the batch kmeans centroids
[MiniBatchKMeans] Starting refactoring code after the review
[MiniBatchKMeans] Small fixes
Merge branch 'master' into batchKMeans
[MiniBatchKMeans] Small fix in the initialisation for the random initialisation of the centroids
[MiniBatchKMeans] Fixed the tests for the new API
[BatchKMeans] Small fixes following Olivier & Gael's review
Merge remote branch 'scikit/master' into batchKMeans
[MiniBatchKMeans] Removed the unnecessary import in examples/cluster/mini_batch_kmeans.py
[MiniBatchKMeans] Now checks the validity of the data only when initializing the centroids. When the data is empty, return immediately
Merge with Olivier's branch
[MiniBatchKMeans] Documentation fixes
[MiniBatchKMeans] Added a benchmark
[MiniBatchKMeans] Added chart showing the speed and the inertia / total number of points depending on the chunk size and number of iteration
merge with master
[MiniBatchKMeans] PEP8 Compliance
[MiniBatchKMeans] Fixed typo in attribute: cluster_centers_
[MiniBatchKMeans] Added some documentation and example
[MiniBatchKMeans] PEP8 compliance
[MiniBatchKMeans] Added a fit method to the MiniBatchKMeans
Merge branch 'master' into batchKMeans
Merge branch 'master' into batchKMeans
[MiniBatchKMeans] PEP8 compliance and small fixed
Trailing white space
[MiniBatchKMeans] Small fixes
[MiniBatchKMeans] Added an example
[MiniBatchKMeans] Updated the example to compare BatchKMeans and MiniBatchKMeans - added the copy_x option to the BatchKMeans
[MiniBatchKMeans] Minor modifications on the examples
[MiniBatchKMeans] Added labels and scaled the axis properly on the benchmark plot
merge with master
Merge remote branch 'gael/batchKMeans' into batchKMeans
FIX the IRC chan used is scikit-learn, and not learn
Nicolas Pinto (1):
Fix typo in SGDClassifier's docstring (via GitHub).
Olivier Grisel (310):
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'batchKMeans' of https://github.com/NelleV/scikit-learn into NelleV-batchKMeans
Merge branch 'batchKMeans' of https://github.com/NelleV/scikit-learn into NelleV-batchKMeans
ENH: style, pep8, docstrings comments, variable names
ENH: more interesting batch size
ENH: more fixes for variable names
ENH: fix example docstring
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'master' into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
Merge branch 'variational-infinite-gmm' of https://github.com/alextp/scikit-learn into alextp-variational-infinite-gmm
ENH: more informative test error message
typo
merged master
merge master
merge from master, update random_state API + pep8
track changes from master
started work on homogeneity, completeness and V-measure as clustering metrics
working implementation of V-measure, still needs doc and updated clustering examples
use V-measure metrics in K-means example
illustrate clustering metrics on affinity propagation example
100% test coverage for the new clustering metrics
more tests
add more documentation for the new metrics
typo
typo
split some tests to make them more atomic
Merge branch 'master' into clustering-metrics
typos
Merge branch 'master' into clustering-metrics
typo
Merge branch 'batchKMeans' of https://github.com/NelleV/scikit-learn into NelleV-batchKMeans
pep8
Merge branch 'master' of github.com:scikit-learn/scikit-learn
more pep8
better docstring for the LabelBinarizer in the multilabel case
started work on normalizer API simplification
work in progress on package structure
FIX: rounding issues on python 2.6 in clustering metrics doctests
ENH: add a note on the symmetry of the metrics
ENH: simpler import statement in example
ENH: simpler import statement in example + explicit square
ENH: add links to the reference guide
ENH: better docstrings for symmetric considerations
cosmit
ENH: better organization of metrics references
ENH: reorganization of the document to be operational quicker
fix broken test introduced in last checkin
new utility function to generate blobby datasets
Merge branch 'master' of github.com:scikit-learn/scikit-learn
FIX: indexing bug when labels are not consecutive
Merge branch 'master' into clustering-metrics
FIX: broken doctests
Merge branch 'master' of github.com:scikit-learn/scikit-learn
ENH: new utility function to shuffle data in a consistent way
Merge branch 'batchKMeans' of https://github.com/NelleV/scikit-learn into NelleV-batchKMeans
Merge pull request #161 from ogrisel/clustering-metrics
ENH: small fixes in scikits.learn.utils.shuffle
Merge branch 'batchKMeans' of https://github.com/NelleV/scikit-learn into NelleV-batchKMeans
Welcome to Nelle\!
pep8
ENH: syntactic sugar for the shuffle utility
ENH: better / simpler handling of shuffling in MiniBatchKMeans
ENH: refactored shuffle to address the resampling with replacement case + more tests
FIX: n_samples bug in shuffle, 100% coverage in utils, missing reference doc entries
first shot at a boostrapping cross validator
typos
more typos
ENH: ensure that training and test split do not share any sample
ENH: better input validation + more representative doctest
ooops
cosmit
DOC: cleanup in cross validation doc
Merge branch 'master' into bootstrap
add bootstrap to reference doc
DOC: new section for the Bootstrap cross-validation
cosmit
cosmit
add see also in resample docstring
FIX: make cross_validation_score work with sparse inputs
merge master
cleanup leftover
ENH: add test for the permutation_test_score with sparse data
Merge branch 'master' into bootstrap
more tests
Merge branch 'balltree-wrapper' of https://github.com/jakevdp/scikit-learn into jakevdp-balltree-wrapper
Merge branch 'bootstrap'
FIX: make r2_score and explained_variance_score never return NaNs
Merge branch 'master' of github.com:scikit-learn/scikit-learn
pep8
add a comment explaining the + 10
Merge branch 'mldata' of https://github.com/pberkes/scikit-learn into pberkes-mldata
pep8 / style
fix broken test in MultinomialNB
ENH: more readable datasets definitions
ENH: avoid double HDD copy of mocked datasets + style
merge
merge master
add random projection and PCA to digits manifold example
use scikit-learn QR compat alias
cosmit
ENH: split figures for better reusability and readability
Merge branch 'extended-digits-manifold-example'
ENH: make the LLE random seeding controllable and deterministic by default
Merge branch 'master' of github.com:scikit-learn/scikit-learn
docstring style
FIX: broken doctests and missing max_iter attribute in LassoLARS
FIX: broken doctest in the documentation caused by the last fix
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' into preprocessing-simplification
work in progress on SampleNormalizer unification
enable test for the sparse variant
getting rid of the remaining stuff in the preprocessing.sparse package
more explicit / descriptive low level cython function names
cosmits / pyflakes / pep8
ENH: improve docstring with missing parameters and motivations
factorize a normalize utility function
s/SampleNormalizer/Normalizer/g
Merge branch 'master' into preprocessing-simplification
moar tests
more tests for preprocessing (scaling)
more tests for preprocessing: coverage is now 100%
make centering optional in Scaler / scale + fix broken test
one more test
one more test for preprocessing (no mean centering)
fail early
pep8
ENH: docstrings for Scaler / scale
bugfix: sparse_format can be omitted
typo
better docstring for Scaler
register the preprocessing utilities to the reference documentation
fixes in See also sections
ENH: give motivations for standardazation in the Scaler docstring
ENH: style fixes and better use of the scikit-learn API in ROC example
Merge branch 'master' into preprocessing-simplification
started work on the narrative documentation for the preprocessing package
typo
reorg TODO and notes
DOC: section on normalization
DOC: section on feature binarization
factorize the binarize function + write documentation
format
Merge pull request #194 from jakevdp/balltree-queryrad
Merge pull request #198 from amueller/fastICA_transposed
Merge pull request #207 from pprett/mbkm-fix
Merge branch 'master' into pberkes-mldata
DOC: reorg of dataset page to make it more consistent
FIX: make the dataset doctest fixture modular
typo
track changes from master
FIX: make the dataset doctest fixture modular
typo
PEP8
ENH: make rng of the LLE tests controllable to hunt down potential NaNs
FIX: add tolerance for lack of numerical precision
Merge remote-tracking branch 'lemin/sparse-mbkm'
remove leading _ in _gen_even_slices and duplicate implementation in sparse_pca
remove verbose output from GMMHMM test
Merge pull request #272 from glouppe/master
fixed broken doctest in HMM
Merge remote-tracking branch 'sabba/master'
Merge pull request #289 from sabba/master
ENH: more rng instance instead of singleton in tests
FIX: potential division by zero when normalizing non-pruned CSR matrices
PEP8 in LLE tests + better assertion failure messages
display the eigen solver name in case of LLE reconstruction test failure
ENH: make the file loader keep the filenames information
cosmit on docstring first line
FIX: broken Gram handling in OMP estimator + minor style improvements
Merge branch 'master' into jakevdp-manifold-isomap
FIX: broken dataset generator import + minor styling issues
fix comment
Merge pull request #303 from glouppe/master
FIX: avoid the dependency on pylab in the doctests
Merge remote-tracking branch 'vene/patch-extraction' into vene-patch-extraction
fix broken doctests
ENH: remove references to digits + format
plot the original centered sample + make sparse pca a little less sparse + kmean a little less like init
DOC: make the decomposition doc more consistent with running faces example
cosmit
ENH: use introspection to find the cluster components
DOC: group SparsePCA and MiniBatchSparsePCA chapter to reduce redundancy
cosmit
ENH: minor style fixes in docstrings and comments
cosmit
cosmit
FIX: removed recently introduced mistake from dict_learning_online docstring
Carve the emmerging consensus on __init__ vs fit parameters in the contributors documentation
cosmit
DOC: give some motivation for the return of self in fit
DOC: formatting mistake
DOC: more fitting doc improvements
typo
DOC: more formatting
yet another typo
Merge pull request #311 from glouppe/test-coverage
Merge pull request #302 from jakevdp/manifold-doc
DOC: section level fix in clustering doc
Merge remote-tracking branch 'robertlayton/kmeans_transform2' into robertlayton-kmeans_transform2
checkpoint style improvements for the KMeans predict
track changes from upstream/master
time the main operations
add warning utils and use it in KMeans when data matrix is integers, boolean, complex...
checkpointing work in progress on VQ example
ENH: add missing inverse_transform method for Scaler
Merge branch 'master' into robertlayton-kmeans_transform2
fix the VQ example by switching to floats in range 0 - 1
Merge branch 'master' into robertlayton-kmeans_transform2
cosmit
use the scipy public API rather than PIL
update the documentation
ENH: 'make test' now runs the doc doctests as well
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge remote-tracking branch 'JeanKossaifi/master'
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge remote-tracking branch 'JeanKossaifi/sorted_repr' into JeanKossaifi-sorted_repr
FIX NMF doctests
ENH: shorter doctest output
ENH: pipeline doctest style improvements
FIX: updating doctests in gaussian_process.rst and linear_model.rst
FIX: remaining broken doctests
FIX: doctests on buildbot
cosmit
ENH: new example: NMF topic extraction on 20 newsgroups
FIX: useless arg to argsort in NMF example
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge remote-tracking branch 'glouppe/master' into glouppe-master
Merge pull request #328 from bdholt1/crossval
more scikits.learn => sklearn updates
ENH: new Makefile target to cythonize everything
Merge branch 'master' of github.com:scikit-learn/scikit-learn
batch re-cythonization with version 0.15 and new package names
fix typo in scikits.learn.qda
update the Makefile test-coverage target to work with the new package layout
ENH: cross_val module docstring and style improvements
ENH: more randomized cross val docstring & var naming improvements
He who seeks only vanity and no love for humanity shall fade away
style
Better exception messages in SVM
ENH: make the cross_val_score able to use functions from the metrics module
ENH: better docstrings for SVMs
Merge branch 'master' into cross-validation-improvements
DOC: improvements to the cross validation doc layout + missing ref to ShuffleSplit and cross_val_score
Merge remote-tracking branch 'glouppe/master' into glouppe-master
Merge branch 'master' into glouppe-master
Add missing authorship + license info to NMF topics example
Merge branch 'master' into cross-validation-improvements
ENH: more cross_val doc for LOLO and LPLO
DOC: add info about smart CV and IC estimators
cosmit
ENH: s/n_labels/n_unique_labels/g in cross_val
Merge pull request #341 from ogrisel/cross-validation-improvements
s/\bcross_val\b/cross_validation/g
backward compat for cross_val namespace
cosmit
API: start 'API changes summary' section in doc/whats_new.rst
API: removal of fit parameters
FIX: fix broken tests on ElasticNetCV
batch trailing spaces cleanup
ENH: docstring cleanup
Mark sklearn.hmm as orphaned
FIX: make the @deprecated class decorator not break the __repr__ of estimators
ENH: implementation Adjusted Rand Index for clustering evaluation
cosmit
removing the undocument implementation of the unadjusted Rand index in kmeans_
cosmit
missing import in the metrics namespace
DOC: narrative documentation for the ARI
DOC: typos
FIX: fix broken document clustering example and add ARI to examples
add doctest for combinations (to document the n < k case)
more tests for ARI and clustering metrics
test non consecutive integers in perfect match
FIX: use scipy's fast implementation of comb + fix tests + limit cases + faster adjustment test
cosmit
OPTIM: use exact comb evaluation since it's faster for the ARI case
cosmit
cosmit
DOC: add example to illustrate the concept of adjustment for chance
more details about ARI value range
make example script filename more explicit
typo
Merge branch 'master' into cluster-metrics-2
Merge remote-tracking branch 'jakevdp/neighbors-refactor' into jakevdp-neighbors-refactor
cosmit + docstest
DOC: reorg, bold important points, include adjustment plot as figure
typo
Merge pull request #347 from ogrisel/cluster-metrics-2
Merge remote-tracking branch 'jakevdp/neighbors-refactor'
Added items for cross validation and clustering metrics
trailing spaces
Merge remote-tracking branch 'vene/sc' into vene-sc
cosmit
DOC: howto register the %lprun line_profiler magic on IPython 0.11+
Merge pull request #313 from robertlayton/pairwise_distance
Merge branch 'master' into vene-sc
Merge remote-tracking branch 'vene/sc' into vene-sc
Merge branch 'sc' of https://github.com/vene/scikit-learn into vene-sc
Merge branch 'sc' of https://github.com/vene/scikit-learn into vene-sc
Merge branch 'sc' of https://github.com/vene/scikit-learn into vene-sc
Merge branch 'sc' of https://github.com/vene/scikit-learn into vene-sc
Merge branch 'vene-sc'
LassoLarsIC/CV and metrics.roc_curve in whats_new
Cosmit.
Olivier Hervieu (6):
Refactor roc_curve method.
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
fixes typo in roc_curve method
[refs #350] - variable renaming regarding reviewer comments
Removes useless (and time consuming) statement.
Improves signal sorting method (using numpy primitives).
Paolo Losi (2):
FIX: for SGD log loss
FIX: partial revert of the SGD log loss fix
Peter Prettenhofer (42):
changed semantics of LossFunction.dloss.
cosmit: pep8 + doc
cosmit: changed docstring of svm_gui.py.
cosmit: removed requirements in svm_gui doc.
Merge remote branch 'upstream/master'
bugfix: bad Scaler example.
fixed LARS doctest.
Initial checkin of sparse.MiniBatchKmeans clustering + document clustering example on 20 news.
enh: compute cache only on samples in current batch.
Added different compute_cache functions: dot and euclidean distance.
added SpectralClustering to document_clustering example.
fix: random_state was set to None.
use provided x_squared_norms instead of recompute (if none euclidean_distances will recompute).
reuse squared sample norms if possible (_calculate_labels_inertia).
Use euclidean distance.
Merge remote branch 'upstream/master' into sparse-mbkm
dense and sparse seed differences: change order of shuffling X and init centroids to ensure consistant results.
changed mini batch representation in dense MiniBatchKMeans - saves mem but increases runtime.
deleted sparse clustering package.
Merged dense and sparse MiniBatchKMeans implementations.
Document clustering example updated.
cosmit: pep8
fast function to compute l2 norm of rows in CSR matrix.
set max_terms to 10k. Added spectral clustering.
more tests for (mini-batch) k-means (99% coverage).
Merge branch 'master' into sparse-mbkm
changed batch representation from indices to slices.
remove assert_warns from test case (not supported by build bots numpy version).
cosmit: docstring of MiniBatchKKeans
remove n_init arg from MiniBatchKMeans signature
fix: doctest formatting.
fix: remove n_init from mbkm tests.
fix: call set_params in mbkm fit.
Merge remote branch 'upstream/master'
cosmit: docstring + raise ValueError if kmeans input is sparse.
added document clustering example to KMeans clustering section.
Merge pull request #305 from vincentschut/mini-batch-kmeans-batch-labeling
fix: if n_samples < chunksize n_batches was 0 and no iterations are performed.
cosmit: rm debug output
add smoke test for MiniBatchKMeans
Merge remote branch 'upstream/master'
added NavigationToolbar to SVM gui
Pietro Berkes (22):
NEW: Function to automatically download any mldata dataset given its name
ERF: load files in "mldata" subdir; some documentation improvement
ERF: Error checking in fetch_mldata
ERF: fetch_mldata allows to use natural mldata.org names for datasets
FIX: trying to reverse-engineer mldata.org conventions
FIX: fetch_mldata fixed to support non-standard data sets in mldata.org
NEW: mldata tests
ERF: Simplify conversion of mldata.org data set name to filename
Merge pull request #1 from ogrisel/pberkes-mldata
FIX: Remove column name when renaming in fetch_mldata
ERF: Improved coverage of mldata, taking into account network availability
DOC: documentation for fetch_mldata
ERF: Test mldata download using mock urllib2
FIX: fix pep8 and pyflakes issues
ERF: refactor object mocking urllib2 for general use (to be used in doctests)
ERF: Refactor utility function to test that list of names are (not) in an object
ERF: Move testing utilities to make them accessible from doctests
FIX: Doctests use mock mldata.org and do not download
DOC: small fix in datasets.rst docs
Merge pull request #2 from larsmans/pberkes-mldata
Merge pull request #3 from ogrisel/pberkes-mldata
FIX: update mldata tests to match recent updates; mock_urllib2 now accepts ordering parameter
Robert (11):
Twenty newsgroups will not create folder if the folder doesn't exist and the files won't be downloaded anyway
Example file based on Affinity Propogation example.
Fixed noted issues with previous version
params in DBSCAN.fit description
DBSCAN now takes either a similarity matrix, OR a feature matrix.
label_num is now only calculated once. This corrects a previous patch, which I incorrected half finished a refactoring, breaking the code badly :(
dbscan_.py file reinstated after accidental deletion
Function to calculate similarity matrix given either a feature matrix or a similarity matrix
Fixed documentation, and the input matrix is now consistently called 'X'.
NOW X is used consistently everywhere
pep8'd and pyflakes'd
Robert Layton (92):
DBSCAN clustering algorithm. A density based cluster analysis algorithm that looks for core points in dense neighbourhoods.
DBSCAN density based clustering algorithm (Ester et al. 1996)
Merge pull request #1 from larsmans/dbscan
labels_ doc updated
Added a paragraph in the documentation.
K-means with transform method.
pep8 fix for k_means_.py
Fixed documentation in example
Examples for dbscan in documentation
Much better example with pyplot, thanks to suggestions by GaelVaroquaux.
vq now the default in KMeans.transform
n_samples used instead of n_points in transform()
American spelling
Example now much more likely to return 3 clusters.
calculate_similarity changed to calculate_distance, moved to metrics.pairwise.py
Import of calculate_distance in metrics.__init__.py.
Merge branch 'master' of https://github.com/robertlayton/scikit-learn
Tests updated to work with the new distance based method.
Test using a callable function as the metric
Multiple small changes
pep8'd
kmeans example renamed
Digits example has plot.
Merge branch 'origin/master' into dbscan
Small changes, mostly to wording
Reference to calculate_distances fixed
Returned line I removed for some reason
Deleted line I returned that I really didn't delete.
K-means documentation updated to include information based on this PR
Extra example removed
Small fixes as per ogrisel's comments.
Merge remote-tracking branch 'remotes/origin/master' into kmeans_transform2
Small changes based on mblondel's comments. Nothing overly noticable
Replace points with samples everywhere
random_state used instead of giving index_order as argument
Description for components_ attribute. Renamed core_samples_ attribute to core_samples_indices_ to remove confusion
Split the transform method into a predict and a transform.
Merge remote-tracking branch 'upstream/master'
Merge branch 'master' into kmeans_transform2
Merge remote-tracking branch 'mblondel/kmeans_transform2' into kmeans_transform2
Merge remote branch 'upstream/master' into pairwise_distance
Initial changes to improve this module. pairwise_distance now uses a dict for functions.
Working through some of the errors in testing
Fixing twenty_newsgroups
Fixed a few import errors
Example images
VQ example. Not working yet - clusters aren't well formed I think.
Fixed loader problems
X -> XA, Y -> XB. pairwise_distance back to metrics
check_set_Y -> check_arrays
Ran tests and fixed a few bugs. Unit tests added.
Less verbose name
Test for tuple input. Tests now run in suite (forgot to have test_ at start of func name!)
XA -> X, XB -> Y
Merge branch 'master' into pairwise_distance
Moved metrics file to sklearn
pairwise_kernel function (untested, for comment)
PEP8 of metrics.py
import to metrics namespace for pairwise_kernels
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into pairwise_distance
Merge branch 'master' into pairwise_distance
Merge branch 'master' into pairwise_distance
Tests working, mostly pass
Merged PR263 into this PR
Fixed merge conflict
Fixes based on ogrisel's comments
l1_distances -> manhattan distance
pep8'd and pyflakes'd
Remove l1_distances completely, updated gaussian_process
Actually removed l1_distances this time
test_checks merged into test_pairwise. test_checks is empty for now.
Removed test_checks
Fixed doctest and checked tests working - most are;
pairwise callable metrics fixed
Now tests if tuples given as input
check_pairwise_arrays now ensures at least two dimensional arrays are returned.
pep8'd and pyflakes'd
metrics listed in pairwise_distances and pairwise_kernels
kwds ws being passed to squareform, instead of pdist. this has been fixed, with a test added
pairwise helper functions to give verbose knowledge of which metrics
Fix commenting in pairwise_distance
check for sparse matrices for scipy metrics, and throw error. test included
Brief description of kernels and distance metrics in doc
Added a list
Little more description
Fixed typos
manhattan_distances now returns [n_samples_X * n_samples_Y, n_features_X] shape array
Doc update for manhattan_distance
Fixed doctest error
Edited sklearn/metrics/pairwise.py via GitHub
Included distance helper functions line for 0.9 release
API changes in metrics/pairwise.py
Salvatore Masecchia (6):
FIX: coordinate descent stopping rule
added missing _set_params call in LineadModelCV
unified and simplified path params creation in LinearModelCV
fixed parameters passing of LinearModelCV.fit, with test
thread safe tests for coordinate descent
pyflakes/pep8 on coordinate descent
Tim Sheerman-Chase (1):
FIX: Corrected NuSVR impl type and set epsilon to None
Vincent Schut (4):
some optimizations for GaussianProcess
pep8 improvements
remove unnecessary parens
batch k-means: calculate labels and intertia in chunks to prevent memory errors
Virgile Fritsch (8):
Compatibility Ubuntu 11.04 (with matplotlib 0.99.3)
Modify the method computing errors on covariances (<cov_object>.error)
Bug fix: turn <covariance_object>.mse into <covariance_object>.error
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Covariance errors computation API changes.
Docstrings about labels + cosmit in the metrics module.
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Add a void cython module affording to check that `make` has been run.
Vlad Niculae (387):
Copied the Sparse PCA file from the gist
Fixed Lasso call, all is still not right
LARS _update_V fixed by Gael
PEP-8
Initial factoring into SparsePCA class
Implemented transform, fixed confusion
DOC: clarified the default for NMF initialization
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' into sparsepca
Updated transform function, began tests
Merged Gael's gist newest update
Merge branch 'master' into sparsepca
A couple of passing tests
factored out the example code
DOC: a little commenting
renaming, included tests
Merge branch 'master' of github.com:scikit-learn/scikit-learn into sparsepca
Updated init.py
one more test and a quick example
pep8
DOC: foundations, prettified example
Doc enhancement, added alpha in transform
Merge branch 'master' into sparsepca
Added ridge in transform (factored here for now)
Removed print statement from test. Whoopsie!
Merge pull request #2 from agramfort/sparsepca
Initial integration of Orthogonal MP
Renaming, some transposing
Tests and the refactoring they induce
PEP8
Added signal recovery test
rigurous pep8
Added the example
Cosmetized the example
s/nonzero/non-zero
Added Olivier's patch extractor with enhancements
cleanup
Tests for various cases
PEP8, renaming, removed image size from params
Merged Gael's latest update to sparse_pca.py
Merge branch 'sparsepca' of github.com:vene/scikit-learn into sparsepca
Merge branch 'sparsepca' into sc
FIX: update_V without warm restart
FIX: weird branching accident
Merge branch 'sparsepca' into sc
Revert "FIX: update_V without warm restart"
Revert "FIX: update_V without warm restart"
Revert "Revert "FIX: update_V without warm restart""
Merge branch 'sparsepca' into sc
Initial integration of Orthogonal MP
Renaming, some transposing
Tests and the refactoring they induce
PEP8
Added signal recovery test
rigurous pep8
Added the example
Cosmetized the example
Added Olivier's patch extractor with enhancements
cleanup
Tests for various cases
PEP8, renaming, removed image size from params
FIX: weird branching accident
Revert "FIX: update_V without warm restart"
Revert "Revert "FIX: update_V without warm restart""
Merge branch 'sc' of github.com:vene/scikit-learn into sc
FIX: update_V without warm restart
Added dictionary learning example
Merge pull request #3 from agramfort/sc
renaming for consistency, tests for PatchExtractor
Initial shape of dictionary learning object
Added DictionaryLearning to __init__.py
FIX: silly bugs so that the example runs
ENH: Tweaked the example a bit
PEP8
Copied the Sparse PCA file from the gist
Fixed Lasso call, all is still not right
LARS _update_V fixed by Gael
PEP-8
Initial factoring into SparsePCA class
Implemented transform, fixed confusion
Updated transform function, began tests
Merged Gael's gist newest update
A couple of passing tests
factored out the example code
DOC: a little commenting
renaming, included tests
Updated init.py
one more test and a quick example
pep8
DOC: foundations, prettified example
Doc enhancement, added alpha in transform
Added ridge in transform (factored here for now)
Removed print statement from test. Whoopsie!
s/nonzero/non-zero
Merged Gael's latest update to sparse_pca.py
FIX: update_V without warm restart
FIX: weird branching accident
Revert "FIX: update_V without warm restart"
Revert "Revert "FIX: update_V without warm restart""
Merge pull request #5 from agramfort/sc
Merge branch 'sparse_pca' of git://github.com/GaelVaroquaux/scikit-learn into sparsepca
Finished merging Gael's pull request
Merge branch 'master' into sparsepca
Merge branch 'master' into sc
Merge branch 'sparsepca' into sc
Merge branch 'sc' of git://github.com/larsmans/scikit-learn into sc
Renaming, part one
Renaming, part two
Renamed online dict_learning appropriately
Merge branch 'sparsepca' into sc
Renaming part three
Fixed dico learning example
Used @fabianp's ridge refactoring
Exposed ridge_regression in linear_model init.py
Merge branch 'master' into sparsepca
Updated ridge import
Merge branch 'sparsepca' into sc
FIX: checks in orthogonal_mp
Cleanup orthogonal_mp docstrings
OMP docs, a little broken for now
DOC: omp documentation improved
DOC: omp documentation fixes
DOC: dict_learning docs
dictionary learning tests
Fixed overcomplete case and updated dl example
fixed overcomplete case
online dictionary learning object
factored base dico object
pep8
Merge branch 'sparsepca' into sc
pep8
more transform methods, split_sign
OMP dictionary must have normalized columns.
Merge branch 'master' into sparsepca
Merge branch 'master' into sc
DOC: improved dict learning docs
Tweaked the dico example
exposed dict learning online in init
working on partial fit
denoising example
Annotate the example
partial fit iteration tracking, test still fails
FIX: typo, s/treshold/threshold
Merge branch 'sparsepca' into mblondel-fix_ridge
simplify sparse pca
Tweak denoise example spacing
pep8 examples
pep8
Merge branch 'master' into sparsepca
Merge branch 'mblondel-fix_ridge' into sparsepca
Merge branch 'sparsepca' into sc
random state control, comment fixes
Merge branch 'sparsepca' into sc
random state control
clarify lasso method param
Merge branch 'sparsepca' into sc
clarify lasso method param in sc too
s/seed/random_state in patch extractor
DOC: fixed patch extraction comments
ENH: PatchExtractor transform
d:/progs/Git/s/seed/random_state in dico learning example
d:/progs/Git/s/seed/random_state in denoising example
FIX: s/V_views/code_views and pickling
Merge branch 'sparsepca' into sc
DOC: more sparse pca narrative documentation
FIX: gram when method=cd
Merge branch 'master' into sparsepca
removed fit_transform overload
Merge branch 'sparsepca' into sc
DOC: consistent punctuation, minor enh
DOC: missed a couple of dots
ENH: verbose and title in sparse pca example
DOC: fixed typo in sparse pca narratives
Merge branch 'dwf_sparse_pca' of git://github.com/GaelVaroquaux/scikit-learn into dwf_sparse_pca
TEST: fake parallelism
TEST: fake only on win32
TEST: no meddling with joblib outside of win32
Merge branch 'master' into sparsepca
Lower tolerance in sparse pca example
DOC: sparse pca transform rephrasing
DOC: more sparse pca transform rephrasing
One big decomposition example
DOC: consistent coding method in docstrings
Merge pull request #7 from GaelVaroquaux/dwf_sparse_pca
TEST: more coverage
FIX: sparse pca ignored initialization
Merge pull request #8 from GaelVaroquaux/dwf_sparse_pca
Merge branch 'sparsepca' of github.com:vene/scikit-learn into sparsepca
FIX: typo in example s/cluter/cluster
pep8
pep8 in example
FIX: messed up images in narrative doc
FIX: example image order is consistent (for now)
ENH: predictable ordering in example, included kmeans
kernel pca gets its own module
Merge branch 'master' into sc
DOC: fixed SparsePCA docstring issue
Brought in OMP from the larger branch
added functions to classes.rst
Remove useless prints in example
Merge branch 'master' into omp
consistency with lasso: s/n_atoms/n_features
DOC: some fixes
failing test for expected behaviour
FIX: LARS and LassoLARS did not accept n_features
PEP8
FIX: doctests
Merge branch 'master' into lars_n_features
FIX: broken doctest in Lars
cleared n_features naming confusion
s/n_nonzero_features/n_nonzero_coefs
Factored out sparse samples generator
pep8
OrthogonalMatchingPursuit estimator
pep8
Merge branch 'master' into omp
cosmit in example
unified notation
made code consistent with docstring
cleaned up tests, added count_nonzero to fixes
Added OMP bench
better cholesky management
pep8
arrayfuncs solve_triangular and EPIC creeping bugfix
fixed check for None
set random seed to hide odd random test failures
fix more None checks
more clarity
Added early stopping as in reference implementation
n_nonzero_coefs defaults to 10% if eps not passed
began rewriting the tests
transposed generator, updated tests
fixed stupid mistake causing the sample generator to be inconsistent
warn when omp stops early
no need for min, it would break on the previous line
change matrix order, gram looks ok now
use np.asfortranarray
tests robust to warnings
do not overwrite global warn filters in test
use np.argmax instead of x.argmax()
while 1 instead of while True
use nrm2 from BLAS
It's official: omp is faster than lars (w/o Gram)
API changes, part I
API changes, part II: Return of the Estimator
FIX: precompute_gram=auto
DOC: docstrings fixes
pep8
don't use gram in example, useless slowdown
FIX: benchmark was broken
DOC: docstrings
Convert to F-order as soon as possible
F-order asap, don't assume any overwriting
that was unneeded
clearer benchmark
Merge pull request #11 from agramfort/omp
DOC: referenced OrthogonalMatchingPursuit in doc
Merge branch 'omp' of github.com:vene/scikit-learn into omp
updated samples generator according to @glouppe's refactoring
typo s/dictionnary/dictionary
PEP8
Merge branch 'master' into omp
FIX: broken samples generator test
FIX: cruel bug in OMP, no more unneeded warnings now.
Merge branch 'master' into sc
Added Olivier's patch extractor with enhancements
Tests for various cases
PEP8, renaming, removed image size from params
s/seed/random_state in patch extractor
DOC: fixed patch extraction comments
ENH: PatchExtractor transform
extra blank line
pep8 in test file
image.py authors
speed up tests
improved warning for invalid max_patches
New file: Feature extraction documentation
Added feature extraction as a chapter
fix copy paste error in docstring
DOC: improved docstrings
Updated documentation, fixed bug in the process
DOC: clarified docstrings even more
Merge branch 'master' into sc
Accidentally removed a line in a test
pep8 in doc
rename coding_method, transform_method to fit/transform_algorithm
fix broken test
changed digits to faces decomposition example
added dict_learning_online function
MiniBatchSparsePCA is born
Removed dict_init in MiniBatchSparsePCA, docstrings
code reuse by inheritance, more tests
Fast-running face decomposition example
DOC: updated narrative docs for MiniBatchSparsePCA and example
DOC: fixes and updates
DOC: minor errors
FIX: broken test
Added MiniBatchSparsePCA and dict_learning_online to classes.rst
DOC: fixed issue in MiniBatchSparsePCA docstring
ENH: cleaner random number handling in tests
Removed default value of n_components=None in SparsePCA
Fixed inappropriate checks for None
Switched dict_learning_online returns order for consistency
ridge_alpha as instance parameter
prettify face decomposition example (ft. GaelVaroquaux)
add refs to example
Merge branch 'master' into sc
duplicated import
FIX: denoise example was broken
FIX: reconstruction test
make tests share data
clarify docstrings
added init test
partial_fit passes the test
added least-angle regression to dictionary learning transform
plugged in lars instead of lasso_lars in denoising example
Merge branch 'master' into sc
redesign the denoising example
FIX: BayesianRidge doctest
tweaked the example a little more
removed thresholding from denoising example
completely removed thresholding from denoising example
Prettify example
More work on example
tweaking example
DOC: clarified and enhanced dictionary learning narratives
added dictionary learning to classes.rst
corrected reference to omp
DOC: fixed link to decomposition example
DOC: fix See also
DOC: fix See also in both places
DOC: cleaner see also section
DOC: improved dict learning narratives some more
Data centering in denoising example
Removed fit_params from dictionary learning objects
plot the dictionary in denoising example, other one will disappear
completely removed the duplicated example
Prettify the example
Rehauled example to show the difference
Renamed the example, bounded the difference range
Lower the range of the difference in example for better contrast
Added norm to titles
More explicit docstring in the example
Removed verbosity (example now 4s faster!), prettier output
fix output bug
PEP8 and style
Merge branch 'master' into sc
style
Merge branch 'master' into sc
Use fit_params in Pipeline
Moved dict_learning stuff out of sparse_pca.py
rename eps to tol in omp code
Exposed sparse_encoding, docs not updated
Consistent defaults
Updated the first part of the docs
Updated the docs
removed fit_transform for dict learning
Updated the narrative doc
Tweaking the example
Improved the example clarity
Merge branch 'master' into sc
removed unused imports
fixed all pyflakes warnings
Merge branch 'master' into sc
Copied tests, fixed examples imports, enhanced see alsos
Merge branch 'master' into sc
Merge branch 'sc' of git://github.com/agramfort/scikit-learn into sc
Merge branch 'master' into sc
Included dictionary learning online in decomp example
Added missing dashes in doc
Merge branch 'master' into sc
Merge branch 'vene-sc' of git://github.com/ogrisel/scikit-learn into sc
Merge branch 'master' into sc
Merge branch 'dictionary_learning' of git://github.com/GaelVaroquaux/scikit-learn into sc
renamed MiniBatchDictionaryLearning
layout
Reordered dictionary learning docs
tweaked faces decomposition and added to dict learning docs
added dict learning face decomposition to docs
Fixed image display in docs
simplified fit_algorithm keyword
s/img_denoising/image_denoising
made sparse_encode functions visible
added see also refs to sparse_encode functions
Reordered dictionary learning docs
Stabilized and improved face decomposition example
explicit seeding of olivetti faces loader
X006 (1):
Dataset loader moved to datasets.base, but not being installed
Yaroslav Halchenko (23):
FIX: reflect SVC API change (eps -> tol) in doc/tutorial.rst
FIX: lars_path -- assure that at least some features get added if necessary
test case for previous commit
minor -- pass verbose into LARS in the test case
FIX: strings are not necessarily singletones + catch mistakes earlier
DOC: minor spellings fixes in pls.py
Merge commit '0.9' (theirs) into releases
Merge branch 'releases' into dfsg
Merge branch 'dfsg' into debian
removed deprecated up_workaround_numpy_cython_issue589652
Refreshed deb_use_system_joblib patch to reflect renaming of the module
added patch up_release_sv_coef_memory to "cherry-pick" 59006f248f24b22f7a8a21ada85c8558b5a1d1b6 (release sv_coef memory)
First wave of changes for scikits.learn -> sklearn transition
Initial changelog for 0.9.0-1
updating deb_use_system_joblib again
more on scikits.learn -> sklearn -- running tests
Enabled all unittests and ignore failures on doctests: https://github.com/scikit-learn/scikit-learn/issues/401
to please recent gbp -- we are building from the branch, not tags
BF: fixing movement of scikits.learn into compat package
added Enhances field to reference upcoming mvpa2 and mdp
Adjusted debian/copyright to be fresh DEP5-compliant (+minor reformatting of changelog)
Removed ipython from build-depends and suggested it for the binary
fixing bashism in custom 'move around' loop
unknown (3):
Added documentation for the Naive Bayes classifiers.
Added sparse MNNB and modified the textual examples to benchmark it.
Modified the Naive Bayes nose tests to the new location of the module and added sparse test.
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