gfdl.model.GFDL#

class GFDL(hidden_layer_sizes: ArrayLike = (100,), activation: str = 'identity', weight_scheme: str = 'uniform', direct_links: bool = True, seed: int = None, reg_alpha: float = None, rtol: float | None = None)[source]#

Bases: BaseEstimator

Base class for GFDL for classification and regression.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

fit
get_generator
predict

__init__(hidden_layer_sizes: ArrayLike = (100,), activation: str = 'identity', weight_scheme: str = 'uniform', direct_links: bool = True, seed: int = None, reg_alpha: float = None, rtol: float | None = None)[source]#

Methods

`__init__`([hidden_layer_sizes, activation, ...])
`fit`(X, Y)
`get_generator`(seed)
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)
`set_params`(**params)	Set the parameters of this estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.