`sklearn.linear_model`.orthogonal_mp_gram¶

sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True, return_path=False, return_n_iter=False)[source]¶

Gram Orthogonal Matching Pursuit (OMP)

Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y.

Read more in the User Guide.

Parameters

Gramarray, shape (n_features, n_features): Gram matrix of the input data: X.T * X
Xyarray, shape (n_features,) or (n_features, n_targets): Input targets multiplied by X: X.T * y
n_nonzero_coefsint: Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.
tolfloat: Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
norms_squaredarray-like, shape (n_targets,): Squared L2 norms of the lines of y. Required if tol is not None.
copy_Grambool, 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_Xybool, optional: Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten.
return_pathbool, optional. Default: False: Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation.
return_n_iterbool, optional default False: Whether or not to return the number of iterations.

Returns

coefarray, shape (n_features,) or (n_features, n_targets): Coefficients of the OMP solution. If return_path=True, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features.
n_itersarray-like or int: Number of active features across every target. Returned only if return_n_iter is set to True.

sklearn.linear_model.orthogonal_mp_gram¶

`sklearn.linear_model`.orthogonal_mp_gram¶