pyriemann.classification.MeanField

class pyriemann.classification.MeanField(power_list=[-1, 0, 1], method_label='sum_means', metric='riemann', n_jobs=1)

Classification by Mean Field.

The Mean Field estimates several power means for each class. Then, it can be used as a classifier, which computes the minimum distance to the mean field [1]; or as a feature extractor, which must be pipelined with another classifier [2].

Parameters:
power_listlist of float, default=[-1,0,+1]

Exponents of power means.

method_label{“sum_means”, “inf_means”}, default=”sum_means”

Method to combine labels:

  • sum_means: it assigns the matrix to the class whom the sum of distances to means of the field is the lowest;

  • inf_means: it assigns the matrix to the class of the nearest mean of the field.

metricstring, default=”riemann”

Metric used for distance estimation during prediction. For the list of supported metrics, see pyriemann.utils.distance.distance().

Attributes:
classes_ndarray, shape (n_classes,)

Labels for each class.

covmeans_dict of n_powers dicts of n_classes ndarrays of shape (n_channels, n_channels)

Centroids for each power and each class.

See also

MDM

Notes

Added in version 0.3.

References

[1]

The Riemannian Minimum Distance to Means Field Classifier M Congedo, PLC Rodrigues, C Jutten. BCI 2019 - 8th International Brain-Computer Interface Conference, Sep 2019, Graz, Austria.

[2]

The Riemannian Means Field Classifier for EEG-Based BCI Data A Anndreev, G Cattan, M Congedo. MDPI Sensors journal, April 2025

__init__(power_list=[-1, 0, 1], method_label='sum_means', metric='riemann', n_jobs=1)

Init.

fit(X, y, sample_weight=None)

Fit (estimates) the centroids.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD 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:
selfMeanField instance

The MeanField instance.

fit_predict(**kwargs)

Warning

DEPRECATED: fit_predict() is deprecated and will be removed in 0.10.0; please use fit().predict().

fit_transform(X, y, sample_weight=None)

Fit and transform in a single function.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD 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:
distndarray, shape (n_matrices, n_classes)

Distance to each means field according to the metric.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

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.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
predndarray of int, shape (n_matrices,)

Predictions for each matrix according to the nearest means field.

predict_proba(X)

Predict proba using softmax of negative squared distances.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
probndarray, shape (n_matrices, n_classes)

Probabilities for each class.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Test set of SPD matrices.

yndarray, shape (n_matrices,)

True labels for each matrix.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix.

Returns:
scorefloat

Mean accuracy of clf.predict(X) wrt. y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MeanField

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MeanField

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

transform(X)

Get the distance to each means field.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:
distndarray, shape (n_matrices, n_classes)

Distance to each means field according to the metric.