What’s new in the package

A catalog of new features, improvements, and bug-fixes in each release.

v0.6.dev

• Update pyRiemann from Python 3.7 to 3.8. #254 by @qbarthelemy

• Speedup pairwise distance function pyriemann.utils.distance.pairwise_distance() by adding individual functions for ‘euclid’, ‘harmonic’, ‘logeuclid’ and ‘riemann’ metrics. #256 by @gabelstein

• Add pyriemann.utils.test.is_real_type() to check the type of input arrays and add pyriemann.utils.covariance.covariance_scm() allowing to process complex-valued inputs for ‘scm’ covariance estimator. #251 by @qbarthelemy

• Update to Read the Docs v2. #260 by @qbarthelemy

• Correct pyriemann.utils.distance.distance_wasserstein() and pyriemann.utils.distance.distance_kullback(), keeping only real part. #267 by @qbarthelemy

v0.5 (Jun 2023)

• Fix pyriemann.utils.distance.pairwise_distance() for non-symmetric metrics. #229 by @qbarthelemy

• Fix pyriemann.utils.mean.mean_covariance() used with keyword arguments. #230 by @qbarthelemy

• Add functions to test HPD and HPSD matrices, pyriemann.utils.test.is_herm_pos_def() and pyriemann.utils.test.is_herm_pos_semi_def(). #231 by @qbarthelemy

• Add function pyriemann.datasets.make_matrices() to generate SPD, SPSD, HPD and HPSD matrices. Deprecate function pyriemann.datasets.make_covariances(). #232 by @qbarthelemy

• Add tests for matrix operators and distances for HPD matrices, complete doc and add references. #234 by @qbarthelemy

• Enhance tangent space module to process HPD matrices. #236 by @qbarthelemy

• Fix regression introduced in pyriemann.spatialfilters.Xdawn() by #214. #242 by @qbarthelemy

• Fix pyriemann.utils.kernel.kernel_euclid() applied on non-symmetric matrices. #245 by @qbarthelemy

• Add argument squared to all distances. #246 by @qbarthelemy

• Correct transform and predict_proba of pyriemann.classification.MeanField. #247 by @qbarthelemy

• Enhance mean module to process HPD matrices. #243 by @qbarthelemy

• Correct pyriemann.utils.distance.distance_mahalanobis(), keeping only real part. #249 by @qbarthelemy

• Fix pyriemann.datasets.sample_gaussian_spd() used with sampling_method=rejection on 2D matrices. #250 by @mhurte

v0.4 (Feb 2023)

• Add exponential and logarithmic maps for three main metrics: ‘euclid’, ‘logeuclid’ and ‘riemann’. pyriemann.utils.tangentspace.tangent_space() is splitted in two steps: (i) log_map_*() projecting SPD matrices into tangent space depending on the metric; and (ii) pyriemann.utils.tangentspace.upper() taking the upper triangular part of matrices. Similarly, pyriemann.utils.tangentspace.untangent_space() is splitted into (i) pyriemann.utils.tangentspace.unupper() and (ii) exp_map_*(). The different metrics for tangent space mapping can now be defined into pyriemann.tangentspace.TangentSpace, then used for transform() as well as for inverse_transform(). #195 by @qbarthelemy

• Enhance AJD: add init to pyriemann.utils.ajd.ajd_pham() and pyriemann.utils.ajd.rjd(), add warm_restart to pyriemann.spatialfilters.AJDC. #196 by @qbarthelemy

• Add parameter sampling_method to pyriemann.datasets.sample_gaussian_spd(), with rejection accelerating 2x2 matrices generation. #198 by @Artim436

• Add geometric medians for Euclidean and Riemannian metrics: pyriemann.utils.median_euclid() and pyriemann.utils.median_riemann(), and add an example in gallery to compare means and medians on synthetic datasets. #200 by @qbarthelemy

• Add score() to pyriemann.regression.KNearestNeighborRegressor. #205 by @qbarthelemy

• Add Transfer Learning module and examples, including RPA and MDWM. #189 by @plcrodrigues, @qbarthelemy and @sylvchev

• Add class distinctiveness function to measure the distinctiveness between classes on the manifold, pyriemann.classification.class_distinctiveness(), and complete an example in gallery to show how it works on synthetic datasets. #215 by @MSYamamoto

• Add example on ensemble learning applied to functional connectivity, and add pyriemann.utils.base.nearest_sym_pos_def(). #202 by @mccorsi and @sylvchev

• Add kernel matrices representation pyriemann.estimation.Kernels and complete example comparing estimators. #217 by @qbarthelemy

• Add a new covariance estimator, robust fixed point covariance, and add kwds arguments for all covariance based functions and classes. #220 by @qbarthelemy

• Add example in gallery on frequency band selection using class distinctiveness measure. #219 by @MSYamamoto

• Add pyriemann.utils.covariance.covariance_mest() supporting three robust M-estimators (Huber, Student-t and Tyler) and available for all covariance based functions and classes; and add an example on robust covariance estimation for corrupted data. Add also pyriemann.utils.distance.distance_mahalanobis() between between vectors and a Gaussian distribution. #223 by @qbarthelemy

v0.3 (July 2022)

• Correct spectral estimation in pyriemann.utils.covariance.cross_spectrum() to obtain equivalence with SciPy. #133 by @qbarthelemy

• Add instantaneous, lagged and imaginary coherences in pyriemann.utils.covariance.coherence() and pyriemann.estimation.Coherences. #132 by @qbarthelemy

• Add partial_fit in pyriemann.clustering.Potato, useful for an online update; and update example on artifact detection. #133 by @qbarthelemy

• Deprecate pyriemann.utils.viz.plot_confusion_matrix() as sklearn integrate its own version. #135 by @sylvchev

• Add Ando-Li-Mathias mean estimation in pyriemann.utils.mean.mean_covariance(). #56 by @sylvchev

• Add Schaefer-Strimmer covariance estimator in pyriemann.utils.covariance.covariances(), and an example to compare estimators #59 by @sylvchev

• Refactor tests + fix refit of pyriemann.tangentspace.TangentSpace. #136 by @sylvchev

• Add pyriemann.clustering.PotatoField, and an example on artifact detection. #142 by @qbarthelemy

• Add sampling SPD matrices from a Riemannian Gaussian distribution in pyriemann.datasets.sample_gaussian_spd(). #140 by @plcrodrigues

• Add new function pyriemann.datasets.make_gaussian_blobs() for generating random datasets with SPD matrices. #140 by @plcrodrigues

• Add module pyriemann.utils.viz in API, add pyriemann.utils.viz.plot_waveforms(), and add an example on ERP visualization. #144 by @qbarthelemy

• Add a special form covariance matrix pyriemann.utils.covariance.covariances_X(). #147 by @qbarthelemy

• Add masked and NaN means with Riemannian metric: pyriemann.utils.mean.maskedmean_riemann() and pyriemann.utils.mean.nanmean_riemann(). #149 by @qbarthelemy and @sylvchev

• Add corr option in pyriemann.utils.covariance.normalize(), to normalize covariance into correlation matrices. #153 by @qbarthelemy

• Add block covariance matrix: pyriemann.estimation.BlockCovariances and pyriemann.utils.covariance.block_covariances(). #154 by @gabelstein

• Add Riemannian Locally Linear Embedding: pyriemann.embedding.LocallyLinearEmbedding and pyriemann.embedding.locally_linear_embedding(). #159 by @gabelstein

• Add Riemannian Kernel Function: pyriemann.utils.kernel.kernel_riemann(). #159 by @gabelstein

• Fix fit in pyriemann.channelselection.ElectrodeSelection. #166 by @qbarthelemy

• Add power mean estimation in pyriemann.utils.mean.mean_power(). #170 by @qbarthelemy and @plcrodrigues

• Add example in gallery to compare classifiers on synthetic datasets. #175 by @qbarthelemy

• Add predict_proba in pyriemann.classification.KNearestNeighbor, and correct attribute classes_. #171 by @qbarthelemy

• Add Riemannian Support Vector Machine classifier: pyriemann.classification.SVC. #175 by @gabelstein and @qbarthelemy

• Add Riemannian Support Vector Machine regressor: pyriemann.regression.SVR. #175 by @gabelstein and @qbarthelemy

• Add K-Nearest-Neighbor regressor: pyriemann.regression.KNearestNeighborRegressor. #164 by @gabelstein, @qbarthelemy and @agramfort

• Add Minimum Distance to Mean Field classifier: pyriemann.classification.MeanField. #172 by @qbarthelemy and @plcrodrigues

• Add example on principal geodesic analysis (PGA) for SSVEP classification. #169 by @qbarthelemy

• Add pyriemann.utils.distance.distance_harmonic(), and sort functions by their names in code, doc and tests. #183 by @qbarthelemy

• Parallelize functions for dataset generation: pyriemann.datasets.make_gaussian_blobs(). #179 by @sylvchev

• Fix dispersion when generating datasets: pyriemann.datasets.sample_gaussian_spd(). #179 by @sylvchev

• Enhance base and distance functions, to process ndarrays of SPD matrices. #186 and #187 by @qbarthelemy

• Enhance utils functions, to process ndarrays of SPD matrices. #190 by @qbarthelemy

• Enhance means functions, with faster implementations and warning when convergence is not reached. #188 by @qbarthelemy

v0.2.7 (June 2021)

• Add example on SSVEP classification

• Fix compatibility with scikit-learn v0.24

• Correct probas of pyriemann.classification.MDM

• Add predict_proba for pyriemann.clustering.Potato, and an example on artifact detection

• Add weights to Pham’s AJD algorithm pyriemann.utils.ajd.ajd_pham()

• Add pyriemann.utils.covariance.cross_spectrum(), fix pyriemann.utils.covariance.cospectrum(); pyriemann.utils.covariance.coherence() output is kept unchanged

• Add pyriemann.spatialfilters.AJDC for BSS and gBSS, with an example on artifact correction

• Add pyriemann.preprocessing.Whitening, with optional dimension reduction

v0.2.6 (March 2020)

• Updated for better Scikit-Learn v0.22 support

v0.2.5 (January 2018)

• Added BilinearFilter

• Added a permutation test for generic scikit-learn estimator

• Stats module refactoring, with distance based t-test and f-test

• Removed two way permutation test

• Added FlatChannelRemover

• Support for python 3.5 in travis

• Added Shrinkage transformer

• Added Coherences transformer

• Added Embedding class.

v0.2.4 (June 2016)

• Improved documentation

• Added TSclassifier for out-of the box tangent space classification.

• Added Wasserstein distance and mean.

• Added NearestNeighbor classifier.

• Added Softmax probabilities for MDM.

• Added CSP for covariance matrices.

• Added Approximate Joint diagonalization algorithms (JADE, PHAM, UWEDGE).

• Added ALE mean.

• Added Multiclass CSP.

• API: param name changes in CospCovariances to comply to Scikit-Learn.

• API: attributes name changes in most modules to comply to the Scikit-Learn naming convention.

• Added HankelCovariances estimation

• Added SPoC spatial filtering

• Added Harmonic mean

• Added Kullback leibler mean

v0.2.3 (November 2015)

• Added multiprocessing for MDM with joblib.

• Added kullback-leibler divergence.

• Added Riemannian Potato.

• Added sample_weight for mean estimation and MDM.