mne.decoding.Vectorizer¶

class
mne.decoding.
Vectorizer
[source]¶ Transform ndimensional array into 2D array of n_samples by n_features.
This class reshapes an ndimensional array into an n_samples * n_features array, usable by the estimators and transformers of scikitlearn.
Examples
 clf = make_pipeline(SpatialFilter(), _XdawnTransformer(), Vectorizer(),
LogisticRegression())
 Attributes
 features_shape_
tuple
Stores the original shape of data.
 features_shape_
Methods
__hash__
(/)Return hash(self).
fit
(X[, y])Store the shape of the features of X.
fit_transform
(X[, y])Fit the data, then transform in one step.
Transform 2D data back to its original feature shape.
transform
(X)Convert given array into two dimensions.

fit
(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).
 y
None
array
, shape (n_samples,) Used for scikitlearn compatibility.
 Returns
 selfinstance of
Vectorizer
Return the modified instance.
 selfinstance of

fit_transform
(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).
 y
None
array
, shape (n_samples,) Used for scikitlearn compatibility.
 Returns
 X
array
, shape (n_samples, 1) The transformed data.
 X

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

transform
(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
 X
array
, shape (n_samples, n_features) The transformed data.
 X