mne.decoding.
TimeDelayingRidge
(tmin, tmax, sfreq, alpha=0.0, reg_type='ridge', fit_intercept=True)[source]¶Ridge regression of data with time delays.
Parameters: | tmin : int | float
tmax : int | float
sfreq : float
alpha : float
reg_type : str | list
fit_intercept : bool
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See also
Notes
This class is meant to be used with mne.decoding.ReceptiveField
by only implicitly doing the time delaying. For reasonable receptive
field and input signal sizes, it should be more CPU and memory
efficient by using frequency-domain methods (FFTs) to compute the
auto- and cross-correlations.
Methods
__hash__ () <==> hash(x) |
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fit (X, y) |
Estimate the coefficients of the linear model. |
get_params ([deep]) |
Get parameters for this estimator. |
predict (X) |
Predict the output. |
set_params (**params) |
Set the parameters of this estimator. |
__hash__
() <==> hash(x)¶fit
(X, y)[source]¶Estimate the coefficients of the linear model.
Parameters: | X : array, shape (n_samples[, n_epochs], n_features)
y : array, shape (n_samples[, n_epochs], n_outputs)
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Returns: | self : instance of TimeDelayingRidge
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get_params
(deep=True)[source]¶Get parameters for this estimator.
Parameters: | deep : boolean, optional
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Returns: | params : mapping of string to any
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predict
(X)[source]¶Predict the output.
Parameters: | X : array, shape (n_samples[, n_epochs], n_features)
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Returns: | X : ndarray
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set_params
(**params)[source]¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
Returns
——-
self