Bump pyriemann from 0.3 to 0.4

[dependabot]

Bumps pyriemann from 0.3 to 0.4.

Changelog

Sourced from pyriemann's changelog.

v0.4 (Feb 2023)

• Add exponential and logarithmic maps for three main metrics: 'euclid', 'logeuclid' and 'riemann'. :func: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) :func:pyriemann.utils.tangentspace.upper taking the upper triangular part of matrices. Similarly, :func:pyriemann.utils.tangentspace.untangent_space is splitted into (i) :func:pyriemann.utils.tangentspace.unupper and (ii) exp_map_*(). The different metrics for tangent space mapping can now be defined into :class:pyriemann.tangentspace.TangentSpace, then used for transform() as well as for inverse_transform(). :pr:195 by :user:qbarthelemy

• Enhance AJD: add init to :func:pyriemann.utils.ajd.ajd_pham and :func:pyriemann.utils.ajd.rjd, add warm_restart to :class:pyriemann.spatialfilters.AJDC. :pr:196 by :user:qbarthelemy

• Add parameter sampling_method to :func:pyriemann.datasets.sample_gaussian_spd, with rejection accelerating 2x2 matrices generation. :pr:198 by :user:Artim436

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

• Add score() to :class:pyriemann.regression.KNearestNeighborRegressor. :pr:205 by :user:qbarthelemy

• Add Transfer Learning module and examples, including RPA and MDWM. :pr:189 by :user:plcrodrigues, :user:qbarthelemy and :user:sylvchev

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

• Add example on ensemble learning applied to functional connectivity, and add :func:pyriemann.utils.base.nearest_sym_pos_def. :pr:202 by :user:mccorsi and :user:sylvchev

• Add kernel matrices representation :class:pyriemann.estimation.Kernels and complete example comparing estimators. :pr:217 by :user:qbarthelemy

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

• Add example in gallery on frequency band selection using class distinctiveness measure. :pr:219 by :user:MSYamamoto

• Add :func: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 :func:pyriemann.utils.distance.distance_mahalanobis between between vectors and a Gaussian distribution. :pr:223 by :user:qbarthelemy

Commits

• 8747462 bump verstion to 0.4
• 9d73f39 use relative error as stopping criterion for m estimator (#225)
• 527375a Add robust M-estimators for covariance matrix (#223)
• 8cd0eab update readme (#222)
• 29f41f8 add a new function for frequency band selection (#219)
• fb53182 Add fixed point covariance estimator and add **kwds arguments in Covariances ...
• ae7c40a replace while loop by expand_dims (#221)
• fc013b7 Update README.md
• c05762a Update README.md
• 6fd8ee1 Update README.md
• Additional commits viewable in compare view

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