def test_multiclass_sgd_equivalence():
clf = KernelSGDClassifier(kernel="linear",
random_state=0)
clf.fit(mult_dense, mult_target)
decisions = clf.decision_function(mult_dense)
predictions = clf.predict(mult_dense)
clf = SGDClassifier(random_state=0)
clf.fit(mult_dense, mult_target)
decisions2 = clf.decision_function(mult_dense)
predictions2 = clf.predict(mult_dense)
assert_array_almost_equal(decisions, decisions2)
assert_array_almost_equal(predictions, predictions2)
def test_multiclass_natural_kernel_sgd_equivalence():
for loss in ("hinge", "log"):
clf = KernelSGDClassifier(kernel="linear",
loss=loss, multiclass="natural",
random_state=0)
clf.fit(mult_dense, mult_target)
decisions = clf.decision_function(mult_dense)
predictions = clf.predict(mult_dense)
clf = SGDClassifier(random_state=0, loss=loss, multiclass="natural")
clf.fit(mult_dense, mult_target)
decisions2 = clf.decision_function(mult_dense)
predictions2 = clf.predict(mult_dense)
assert_array_almost_equal(decisions, decisions2)
assert_array_almost_equal(predictions, predictions2)
