pyriemann.classification.KNearestNeighbor¶
- class pyriemann.classification.KNearestNeighbor(n_neighbors=5, metric='riemann', n_jobs=1)¶
Classification by k-nearest neighbors.
Classification 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 class is affected according to the majority class of the k-nearest neighbors.
- 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.
- n_jobsint, default=1
The number of jobs to use for the computation. This works by computing each of the distance to the training set 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
Kmeans
MDM
- Attributes
- classes_ndarray, shape (n_classes,)
Labels for each class.
- covmeans_ndarray, shape (n_matrices, n_channels, n_channels)
Matrices of training set.
- classmeans_ndarray, shape (n_matrices,)
Labels of training set.
- __init__(n_neighbors=5, metric='riemann', n_jobs=1)¶
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,)
Labels for each matrix.
- sample_weightNone
Not used, here for compatibility with sklearn API.
- Returns
- selfNearestNeighbor instance
The NearestNeighbor 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(covtest)¶
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 centroid.
- 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 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.