def test_fit_rbf_binary_early_stopping():
clf = DualSVC(loss="l1", kernel="rbf", gamma=0.5, random_state=0,
shrinking=True, selection="loss",
termination="n_sv", sv_upper_bound=30)
clf.fit(bin_dense, bin_target)
y_pred = clf.predict(bin_dense)
assert_equal(clf.dual_coef_.shape[1], 30)
def test_fit_rbf_binary():
for shrinking in (True, False):
for selection in ("loss", "permute", "active"):
for loss in ("l1", "l2"):
clf = DualSVC(loss=loss, kernel="rbf", gamma=0.1, random_state=0,
shrinking=shrinking, selection=selection)
clf.fit(bin_dense, bin_target)
y_pred = clf.predict(bin_dense)
assert_equal(np.mean(y_pred == bin_target), 1.0)
def test_fit_rbf_multi():
clf = DualSVC(kernel="rbf", gamma=0.1, random_state=0)
clf.fit(mult_dense, mult_target)
y_pred = clf.predict(mult_dense)
acc = np.mean(y_pred == mult_target)
assert_almost_equal(acc, 1.0)