generalized_additive_models.Log#

class generalized_additive_models.Log#

Log link: \(g(\mu) = \log(\mu)\)

__init__(*args, **kwargs)#

Methods

`__init__`(args, *kwargs)
`derivative`(mu)	Elementwise first derivative of the link function.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_link`(linear_prediction)	Map from the linear space to the expected value \(\mu\).
`link`(mu)	Map from the expected value \(\mu\) to the unbounded linear space.
`second_derivative`(mu)	Elementwise second derivative of the link function.
`set_params`(**params)	Set the parameters of this estimator.

Attributes

`domain`	Domain of the link function
`name`	Name of the link function

derivative(mu)#: Elementwise first derivative of the link function.

domain = (0, inf)#: Domain of the link function

inverse_link(linear_prediction)#: Map from the linear space to the expected value \(\mu\).

link(mu)#

Map from the expected value \(\mu\) to the unbounded linear space.

Examples

>>> mu = np.array([0.1, 0.5, 0.9, 0.99, 0.999])
>>> Log().link(mu)
array([-2.30258509e+00, -6.93147181e-01, -1.05360516e-01, -1.00503359e-02,
       -1.00050033e-03])

name = 'log'#: Name of the link function

second_derivative(mu)#: Elementwise second derivative of the link function.