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__(self, /)

Return hash(self).

fit(self, X[, y])

Store the shape of the features of X.

fit_transform(self, X[, y])

Fit the data, then transform in one step.

inverse_transform(self, X)

Transform 2D data back to its original feature shape.

transform(self, X)

Convert given array into two dimensions.

__hash__(self, /)

Return hash(self).

fit(self, X, y=None)[source]

Store the shape of the features of X.

Parameters
Xarray_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).

yNone | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns
selfinstance of Vectorizer

Return the modified instance.

fit_transform(self, X, y=None)[source]

Fit the data, then transform in one step.

Parameters
Xarray_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).

yNone | array, shape (n_samples,)

Used for scikit-learn compatibility.

Returns
Xarray, shape (n_samples, -1)

The transformed data.

inverse_transform(self, X)[source]

Transform 2D data back to its original feature shape.

Parameters
Xarray_like, shape (n_samples, n_features)

Data to be transformed back to original shape.

Returns
Xarray

The data transformed into shape as used in fit. The first dimension is of length n_samples.

transform(self, X)[source]

Convert given array into two dimensions.

Parameters
Xarray_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
Xarray, shape (n_samples, n_features)

The transformed data.