skglm.datafits.WeightedQuadratic

class skglm.datafits.WeightedQuadratic(sample_weights)

Weighted Quadratic datafit to handle sample weights.

The datafit reads:

`1 / (2 xx \sum_(i=1)^(n_"samples") weights_i) \sum_(i=1)^(n_"samples") weights_i (y_i - (Xw)_i)^ 2`

Attributes:

Xtwyarray, shape (n_features,)

Pre-computed quantity used during the gradient evaluation. Equal to X.T @ (samples_weights * y).

sample_weightsarray, shape (n_samples,)

Weights for each sample.

__init__(sample_weights)

Methods

__init__(sample_weights)
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(X, y, Xw)
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)
raw_hessian(y, Xw)
value(y, w, Xw)	Value of datafit at vector w.