skglm.datafits.Huber#

class skglm.datafits.Huber(delta)[source]#

Huber datafit.

The datafit reads:

`1 / n_"samples" sum_(i=1)^(n_"samples") f(y_i - (Xw)_i)`

where `f` is the Huber function:

`f(x) = {(1/2 x^2 , if x <= delta), (delta abs(x) - 1/2 delta^2, if x > delta):}`
Attributes:
deltafloat

Threshold hyperparameter.

__init__(delta)[source]#

Methods

__init__(delta)

full_grad_sparse(X_data, X_indptr, ...)

get_global_lipschitz(X, y)

get_global_lipschitz_sparse(X_data, ...)

get_lipschitz(X, y)

get_lipschitz_sparse(X_data, X_indptr, ...)

get_spec()

Specify the numba types of the class attributes.

gradient_scalar(X, y, w, Xw, j)

gradient_scalar_sparse(X_data, X_indptr, ...)

initialize(X, y)

Pre-computations before fitting on X and y.

initialize_sparse(X_data, X_indptr, X_indices, y)

Pre-computations before fitting on X and y when X is a sparse matrix.

intercept_update_step(y, Xw)

params_to_dict()

Get the parameters to initialize an instance of the class.

value(y, w, Xw)

Value of datafit at vector w.