What’s new in the package¶
A catalog of new features, improvements, and bug-fixes in each release.
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
v0.5 (Jun 2023)¶
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)
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
Add geometric medians for Euclidean and Riemannian metrics:
pyriemann.utils.median_riemann(), and add an example in gallery to compare means and medians on synthetic datasets. #200 by @qbarthelemy
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
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)¶
v0.2.7 (June 2021)¶
Add example on SSVEP classification
Fix compatibility with scikit-learn v0.24
Correct probas of
pyriemann.clustering.Potato, and an example on artifact detection
Add weights to Pham’s AJD algorithm
pyriemann.spatialfilters.AJDCfor BSS and gBSS, with an example on artifact correction
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 a permutation test for generic scikit-learn estimator
Stats module refactoring, with distance based t-test and f-test
Removed two way permutation test
Support for python 3.5 in travis
Added Shrinkage transformer
Added Coherences transformer
Added Embedding class.
v0.2.4 (June 2016)¶
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.