skelm
.HiddenLayer¶
- class skelm.HiddenLayer(n_neurons=None, density=None, ufunc='tanh', pairwise_metric=None, random_state=None)[source]¶
Scikit-Learn compatible interface for SLFN.
Handles parameter transformation and input checks. Not a part of ELM; for stand-alone usage.
- __init__(n_neurons=None, density=None, ufunc='tanh', pairwise_metric=None, random_state=None)[source]¶
- 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 (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns:
X_new – Transformed array.
- Return type:
ndarray array of shape (n_samples, n_features_new)
- get_params(deep=True)¶
Get parameters for this estimator.
- set_output(*, transform=None)¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
self – Estimator instance.
- Return type:
estimator instance
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance