pyriemann.classification.FgMDM¶
- class pyriemann.classification.FgMDM(metric='riemann', tsupdate=False, n_jobs=1)¶
Classification by Minimum Distance to Mean with geodesic filtering.
Apply geodesic filtering described in [1], and classify using MDM. The geodesic filtering is achieved in tangent space with a Linear Discriminant Analysis, then data are projected back to the manifold and classifier with a regular MDM. This is basically a pipeline of FGDA and MDM.
- Parameters
- metricstring | dict, default=’riemann’
The type of metric used for reference matrix estimation (see mean_covariance for the list of supported metric), for distance estimation, and for tangent space map (see tangent_space for the list of supported metric). The metric could be a dict with three keys, mean, dist and map in order to pass different metrics for the reference matrix estimation, the distance 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.
- n_jobsint, default=1
The number of jobs to use for the computation. This works by computing each of the class centroid in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
See also
MDM
FGDA
TangentSpace
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.
- Attributes
- classes_ndarray, shape (n_classes,)
Labels for each class.
- __init__(metric='riemann', tsupdate=False, n_jobs=1)¶
Init.
- fit(X, y, sample_weight=None)¶
Fit FgMDM.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yndarray, shape (n_matrices,)
Labels for each matrix.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- Returns
- selfFgMDM instance
The FgMDM instance.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- 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.
- predict(X)¶
Get the predictions after FGDA filtering.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- predndarray of int, shape (n_matrices,)
Predictions for each matrix according to the closest centroid.
- predict_proba(X)¶
Predict proba using softmax after FGDA filtering.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- probndarray, shape (n_matrices, n_classes)
The softmax probabilities for each class.
- score(X, y, sample_weight=None)¶
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
Mean accuracy of
self.predict(X)
wrt. y.
- 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)¶
Get the distance to each centroid after FGDA filtering.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- distndarray, shape (n_matrices, n_cluster)
The distance to each centroid according to the metric.