pyriemann.classification.MeanField¶
- class pyriemann.classification.MeanField(power_list=[-1, 0, 1], method_label='sum_means', metric='riemann', n_jobs=1)¶
Classification by Minimum Distance to Mean Field.
Classification by Minimum Distance to Mean Field [1], defining several power means for each class.
- 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 covariance to the class whom the sum of distances to means of the field is the lowest;
inf_means: it assigns the covariance to the class of the closest mean of the field.
- metricstring | dict, default=’riemann’
The type of metric used for distance estimation during prediction. See distance for the list of supported metric.
See also
Notes
New 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.
- Attributes
- classes_ndarray, shape (n_classes,)
Labels for each class.
- covmeans_dict of
n_powerslists ofn_classesndarrays of shape (n_channels, n_channels) Centroids for each power and each class.
- __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 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(X, y)¶
Fit and predict in one function.
- 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.
- 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 means field.
- predict_proba(X)¶
Predict proba using softmax.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD 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.
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 means field.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- distndarray, shape (n_matrices, n_classes)
Distance to each means field according to the metric.