odel a.k.a. LAR.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
sklearn.decomposition.sparse_encode : Generic sparse coding.
    Each column of the result is the solution to a Lasso problem.
OrthogonalMatchingPursuitCV : Cross-validated
    Orthogonal Matching Pursuit model (OMP).

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

Examples
--------
>>> from sklearn.linear_model import OrthogonalMatchingPursuit
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = OrthogonalMatchingPursuit().fit(X, y)
>>> reg.score(X, y)
0.9991...
>>> reg.predict(X[:1,])
array([-78.3854...])
r