mne.decoding.
SlidingEstimator
(base_estimator, scoring=None, n_jobs=1, verbose=None)[source]¶Search Light.
Fit, predict and score a series of models to each subset of the dataset along the last dimension. Each entry in the last dimension is referred to as a task.
Parameters: |
|
---|---|
Attributes: |
|
Methods
__hash__ ($self, /) |
Return hash(self). |
decision_function (X) |
Estimate distances of each data slice to the hyperplanes. |
fit (X, y, **fit_params) |
Fit a series of independent estimators to the dataset. |
fit_transform (X, y, **fit_params) |
Fit and transform a series of independent estimators to the dataset. |
get_params ([deep]) |
Get parameters for this estimator. |
predict (X) |
Predict each data slice/task with a series of independent estimators. |
predict_proba (X) |
Predict each data slice with a series of independent estimators. |
score (X, y) |
Score each estimator on each task. |
set_params (**params) |
Set the parameters of this estimator. |
transform (X) |
Transform each data slice/task with a series of independent estimators. |
__hash__
($self, /)¶Return hash(self).
decision_function
(X)[source]¶Estimate distances of each data slice to the hyperplanes.
Parameters: |
|
---|---|
Returns: |
|
Notes
This requires base_estimator to have a decision_function
method.
fit
(X, y, **fit_params)[source]¶Fit a series of independent estimators to the dataset.
Parameters: |
|
---|---|
Returns: |
|
fit_transform
(X, y, **fit_params)[source]¶Fit and transform a series of independent estimators to the dataset.
Parameters: |
|
---|---|
Returns: |
|
get_params
(deep=True)[source]¶Get parameters for this estimator.
Parameters: |
|
---|---|
Returns: |
|
predict
(X)[source]¶Predict each data slice/task with a series of independent estimators.
The number of tasks in X should match the number of tasks/estimators given at fit time.
Parameters: |
|
---|---|
Returns: |
|
predict_proba
(X)[source]¶Predict each data slice with a series of independent estimators.
The number of tasks in X should match the number of tasks/estimators given at fit time.
Parameters: |
|
---|---|
Returns: |
|
score
(X, y)[source]¶Score each estimator on each task.
The number of tasks in X should match the number of tasks/estimators
given at fit time, i.e. we need
X.shape[-1] == len(self.estimators_)
.
Parameters: |
|
---|---|
Returns: |
|
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
transform
(X)[source]¶Transform each data slice/task with a series of independent estimators.
The number of tasks in X should match the number of tasks/estimators given at fit time.
Parameters: |
|
---|---|
Returns: |
|