9.2.21. sklearn.linear_model.orthogonal_mp¶

sklearn.linear_model.orthogonal_mp(X, y, n_nonzero_coefs=None, tol=None, precompute_gram=False, overwrite_X=False)¶

Orthogonal Matching Pursuit (OMP)

Solves n_targets Orthogonal Matching Pursuit problems. An instance of the problem has the form:

When parametrized by the number of non-zero coefficients using n_nonzero_coefs: argmin ||y - Xgamma||^2 subject to ||gamma||_0 <= n_{nonzero coefs}

When parametrized by error using the parameter tol: argmin ||gamma||_0 subject to ||y - Xgamma||^2 <= tol

Parameters :

X: array, shape = (n_samples, n_features) :

Input data. Columns are assumed to have unit norm.

y: array, shape = (n_samples,) or (n_samples, n_targets) :

Input targets

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.

precompute_gram: {True, False, ‘auto’}, :

Whether to perform precomputations. Improves performance when n_targets or n_samples is very large.

overwrite_X: bool, :

Whether the design matrix X can be overwritten by the algorithm. This is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

Returns :

coef: array, shape = (n_features,) or (n_features, n_targets) :

Coefficients of the OMP solution

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9.2.21. sklearn.linear_model.orthogonal_mp¶