>> clf = linear_model.Ridge(solver='sag')
>>> clf.fit(X, y)
Ridge(solver='sag')

>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> clf = linear_model.LogisticRegression(solver='sag')
>>> clf.fit(X, y)
LogisticRegression(solver='sag')

References
----------
Schmidt, M., Roux, N. L., & Bach, F. (2013).
Minimizing finite sums with the stochastic average gradient
https://hal.inria.fr/hal-00860051/document

:arxiv:`Defazio, A., Bach F. & Lacoste-Julien S. (2014).
"SAGA: A Fast Incremental Gradient Method With Support
for Non-Strongly Convex Composite Objectives" <1407.0202>`

See Also
--------
Ridge, SGDRegressor, ElasticNet, Lasso, SVR,
LogisticRegression, SGDClassifier, LinearSVC, Perceptron
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