skglm.datafits.Gamma#

class skglm.datafits.Gamma[source]#

Gamma datafit.

The datafit reads:

`1 / n_"samples" \sum_(i=1)^(n_"samples") ((Xw)_i + y_i exp(-(Xw)_i) - 1 - log(y_i))`

Notes

The class is jit compiled at fit time using Numba compiler. This allows for faster computations.

__init__()[source]#

Methods

__init__()

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.

raw_grad(y, Xw)

Compute gradient of datafit w.r.t.

raw_hessian(y, Xw)

Compute Hessian of datafit w.r.t.

value(y, w, Xw)

Value of datafit at vector w.