Note: partial_fit is not ‘partial’. It fully resets the model using the entirety of the SampleMessage provided. If you require adaptive fitting, try using the adaptive_linear_regressor module.

__init__(*args, **kwargs)[source]#

partial_fit(message)[source]#

Return type:: None
Parameters:: message (SampleMessage)

class LinearRegressorSettings(model_type=StaticLinearRegressor.LINEAR, settings_path=None, model_kwargs=<factory>)[source]#

Bases: Settings

Parameters:

model_type (StaticLinearRegressor)
settings_path (str | None)
model_kwargs (dict)

model_type: StaticLinearRegressor = 'linear'#

settings_path: str | None = None#

model_kwargs: dict#

__init__(model_type=StaticLinearRegressor.LINEAR, settings_path=None, model_kwargs=<factory>)#

Parameters:

model_type (StaticLinearRegressor)
settings_path (str | None)
model_kwargs (dict)

Return type:

None

class LinearRegressorState[source]#

Bases: object

template: AxisArray | None = None#

model: LinearModel | None = None#

class LinearRegressorTransformer(*args, **kwargs)[source]#

Bases: BaseAdaptiveTransformer[LinearRegressorSettings, AxisArray, AxisArray, LinearRegressorState]

Linear regressor.

Note: partial_fit is not ‘partial’. It fully resets the model using the entirety of the SampleMessage provided. If you require adaptive fitting, try using the adaptive_linear_regressor module.

__init__(*args, **kwargs)[source]#

partial_fit(message)[source]#

Return type:: None
Parameters:: message (SampleMessage)

class AdaptiveLinearRegressorUnit(*args, settings=None, **kwargs)[source]#

Bases: BaseAdaptiveTransformerUnit[LinearRegressorSettings, AxisArray, AxisArray, LinearRegressorTransformer]

Parameters:: settings (Settings | None)

SETTINGS#: alias of LinearRegressorSettings

ezmsg.learn.process.linear_regressor#

This Page