X, y, w = sklearn.datasets.make_regression(n_samples=40000, n_features=40,
    n_informative=10, coef=True, random_state=0)
clf1 = sklearn.linear_model.LassoLarsCV(fit_intercept=True, normalize=False,
    max_n_alphas=1000000.0)
clf1.fit(X, y)
clf2 = sklearn.linear_model.OrthogonalMatchingPursuitCV(fit_intercept=True,
    normalize=False)
clf2.fit(X, y)
print(numpy.linalg.norm(y - clf1.predict(X)))
print(numpy.linalg.norm(y - clf2.predict(X)))
Orthogonal matching pursuit regression - am I using it wrong?