@ignore_warnings
def test_classifier_results():
    alpha = .1
    n_features = 20
    n_samples = 10
    tol = .01
    max_iter = 200
    rng = np.random.RandomState(0)
    X = rng.normal(size=(n_samples, n_features))
    w = rng.normal(size=n_features)
    y = np.dot(X, w)
    y = np.sign(y)
    clf1 = LogisticRegression(solver='sag', C=1. / alpha / n_samples,
                              max_iter=max_iter, tol=tol, random_state=77)
    clf2 = clone(clf1)

    clf1.fit(X, y)
    clf2.fit(sp.csr_matrix(X), y)
    pred1 = clf1.predict(X)
    pred2 = clf2.predict(X)
    assert_almost_equal(pred1, y, decimal=12)
    assert_almost_equal(pred2, y, decimal=12)
tests if classifier results match target
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/linear_model/tests/test_sag.py/test_classifier_results