pyriemann.regression.KNearestNeighborRegressor¶
- class pyriemann.regression.KNearestNeighborRegressor(n_neighbors=5, metric='riemann')¶
Regression by k-nearest-neighbors.
Regression by k-nearest neighbors (k-NN). For each point of the test set, the pairwise distance to each element of the training set is estimated. The value is calculated according to the softmax average w.r.t. distance of the k-nearest neighbors.
DISCLAIMER: This is an unpublished algorithm.
- Parameters
- n_neighborsint, default=5
Number of neighbors.
- metricstring | dict, default=’riemann’
The type of metric used for distance estimation. See distance for the list of supported metric.
Notes
New in version 0.3.
- Attributes
- values_ndarray, shape (n_matrices,)
Training target values.
- covmeans_ndarray, shape (n_matrices, n_channels, n_channels)
Training set of SPD matrices.
- __init__(n_neighbors=5, metric='riemann')¶
Init.
- fit(X, y, sample_weight=None)¶
Fit (store the training data).
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yndarray, shape (n_matrices,)
Target values for each matrix.
- sample_weightNone
Not used, here for compatibility with sklearn API.
- Returns
- selfKNearestNeighborRegressor instance
The KNearestNeighborRegressor 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, shape (n_matrices,)
Predictions for each matrix according to the closest neighbors.
- 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)¶
Return the coefficient of determination of the prediction.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Test set of SPD matrices.
- yndarray, shape (n_matrices,)
True values for each matrix.
- Returns
- scorefloat
R2 of self.predict(X) wrt. y.
Notes
New in version 0.4.
- 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.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- distndarray, shape (n_matrices, n_classes)
The distance to each centroid according to the metric.