mne.decoding.
EpochsVectorizer
(*args, **kwargs)¶EpochsVectorizer transforms epoch data to fit into a scikit-learn pipeline.
Parameters: | info : instance of Info
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Attributes
n_channels | (int) The number of channels. |
n_times | (int) The number of time points. |
Methods
__hash__ () <==> hash(x) |
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fit (epochs_data, y) |
For each epoch, concatenate data from different channels into a single feature vector. |
fit_transform (X[, y]) |
Fit to data, then transform it |
inverse_transform (X[, y]) |
For each epoch, reshape a feature vector into the original data shape |
transform (epochs_data[, y]) |
For each epoch, concatenate data from different channels into a single feature vector. |
__hash__
() <==> hash(x)¶fit
(epochs_data, y)¶For each epoch, concatenate data from different channels into a single feature vector.
Parameters: | epochs_data : array, shape (n_epochs, n_channels, n_times)
y : array, shape (n_epochs,)
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Returns: | self : instance of EpochsVectorizer
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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: | X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]
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Returns: | X_new : numpy array of shape [n_samples, n_features_new]
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inverse_transform
(X, y=None)¶For each epoch, reshape a feature vector into the original data shape
Parameters: | X : array, shape (n_epochs, n_channels * n_times)
y : None | array, shape (n_epochs,)
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Returns: | epochs_data : array, shape (n_epochs, n_channels, n_times)
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transform
(epochs_data, y=None)¶For each epoch, concatenate data from different channels into a single feature vector.
Parameters: | epochs_data : array, shape (n_epochs, n_channels, n_times)
y : None | array, shape (n_epochs,)
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Returns: | X : array, shape (n_epochs, n_channels * n_times)
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