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:
- Gramndarray of shape (n_features, n_features)
Gram matrix of the input data: X.T * X.
- Xyndarray of shape (n_features,) or (n_features, n_targets)
Input targets multiplied by X: X.T * y.
- n_nonzero_coefsint, default=None
Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.
- tolfloat, default=None
Maximum squared norm of the residual. If not
None
, overridesn_nonzero_coefs
.- norms_squaredarray-like of shape (n_targets,), default=None
Squared L2 norms of the lines of y. Required if tol is not None.
- copy_Grambool, default=True
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, default=True
Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten.
- return_pathbool, default=False
Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation.
- return_n_iterbool, default=False
Whether or not to return the number of iterations.
- Returns:
- coefndarray of 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.
See also
OrthogonalMatchingPursuit
Orthogonal Matching Pursuit model (OMP).
orthogonal_mp
Solves n_targets Orthogonal Matching Pursuit problems.
lars_path
Compute Least Angle Regression or Lasso path using LARS algorithm.
sklearn.decomposition.sparse_encode
Generic sparse coding. Each column of the result is the solution to a Lasso problem.
Notes
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf