pyriemann.tangentspace.FGDA¶
- class pyriemann.tangentspace.FGDA(metric='riemann', tsupdate=False)¶
Fisher Geodesic Discriminant analysis.
Project data in Tangent space, apply a FLDA to reduce dimention, and project filtered data back in the manifold. For a complete description of the algorithm, see [1].
- Parameters
- metricstring | dict, default=’riemann’
The type of metric used for reference matrix estimation (see mean_covariance for the list of supported metric) and for tangent space map (see tangent_space for the list of supported metric). The metric could be a dict with two keys, mean and map in order to pass different metrics for the reference matrix estimation and the tangent space mapping.
- tsupdatebool, default=False
Activate tangent space update for covariante shift correction between training and test, as described in [2]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.
See also
FgMDMTangentSpace
References
- 1
Riemannian geometry applied to BCI classification A. Barachant, S. Bonnet, M. Congedo and C. Jutten. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), LNCS vol. 6365, 2010, p. 629-636.
- 2
Classification of covariance matrices using a Riemannian-based kernel for BCI applications A. Barachant, S. Bonnet, M. Congedo and C. Jutten. Neurocomputing, Elsevier, 2013, 112, pp.172-178.
- __init__(metric='riemann', tsupdate=False)¶
Init.
- fit(X, y=None, sample_weight=None)¶
Fit (estimates) the reference point and the FLDA.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns
- selfFGDA instance
The FGDA instance.
- fit_transform(X, y=None, sample_weight=None)¶
Fit and transform in a single function.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns
- covsndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices after filtering.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
- transform(X)¶
Filtering operation.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- covsndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices after filtering.