mne.decoding.Vectorizer¶

class mne.decoding.Vectorizer[source]¶

Transform n-dimensional array into 2D array of n_samples by n_features.

This class reshapes an n-dimensional array into an n_samples * n_features array, usable by the estimators and transformers of scikit-learn.

Examples

clf = make_pipeline(SpatialFilter(), _XdawnTransformer(), Vectorizer(),

LogisticRegression())

Attributes

features_shape_

(tuple) Stores the original shape of data.

Methods

__hash__() <==> hash(x)
fit(X[, y])	Store the shape of the features of X.
fit_transform(X[, y])	Fit the data, then transform in one step.
inverse_transform(X)	Transform 2D data back to its original feature shape.
transform(X)	Convert given array into two dimensions.

__hash__() <==> hash(x)¶

fit(X, y=None)[source]¶

Store the shape of the features of X.

Parameters:	X : array-like The data to fit. Can be, for example a list, or an array of at least 2d. The first dimension must be of length n_samples, where samples are the independent samples used by the estimator (e.g. n_epochs for epoched data). y : None | array, shape (n_samples,) Used for scikit-learn compatibility.
Returns:	self : Instance of Vectorizer Return the modified instance.

fit_transform(X, y=None)[source]¶

Fit the data, then transform in one step.

Parameters:	X : array-like The data to fit. Can be, for example a list, or an array of at least 2d. The first dimension must be of length n_samples, where samples are the independent samples used by the estimator (e.g. n_epochs for epoched data). y : None | array, shape (n_samples,) Used for scikit-learn compatibility.
Returns:	X : array, shape (n_samples, -1) The transformed data.

inverse_transform(X)[source]¶

Transform 2D data back to its original feature shape.

Parameters:	X : array-like, shape (n_samples, n_features) Data to be transformed back to original shape.
Returns:	X : array The data transformed into shape as used in fit. The first dimension is of length n_samples.

transform(X)[source]¶

Convert given array into two dimensions.

Parameters:	X : array-like The data to fit. Can be, for example a list, or an array of at least 2d. The first dimension must be of length n_samples, where samples are the independent samples used by the estimator (e.g. n_epochs for epoched data).
Returns:	X : array, shape (n_samples, n_features) The transformed data.