sklearn.linear_model.orthogonal_mp_gram()

Gram : array, shape (n_features, n_features)

Gram matrix of the input data: X.T * X

Xy : array, shape (n_features,) or (n_features, n_targets)

Input targets multiplied by X: X.T * y

n_nonzero_coefs : int

Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.

tol : float

Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

norms_squared : array-like, shape (n_targets,)

Squared L2 norms of the lines of y. Required if tol is not None.

copy_Gram : bool, optional

Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway.

copy_Xy : bool, optional

Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten.

return_path : bool, optional. Default: False

Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation.

return_n_iter : bool, optional default False

Whether or not to return the number of iterations.