class TestMainClassifier(TestCase): def setUp(self): self.main_classifier = TieredLayeredNeuralNetwork(feature_count) def test_activate(self): self.fail() def test_fit(self): self.main_classifier.fit(X, y, 'test main classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 2) def test_fit_unequal_width(self): self.main_classifier.fit(X, y, 'test main classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 2) new_X = np.random.randn(samples_count,42) self.main_classifier.fit(X, y, 'unequal width classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 3) def test_predict(self): self.fail() def test_find_label_position(self): self.fail() def test_score(self): self.fail() def test_status(self): self.fail()
class TestMainClassifier(TestCase): def setUp(self): self.main_classifier = TieredLayeredNeuralNetwork(feature_count) def test_activate(self): self.fail() def test_fit(self): self.main_classifier.fit(X, y, 'test main classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 2) def test_fit_unequal_width(self): self.main_classifier.fit(X, y, 'test main classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 2) new_X = np.random.randn(samples_count, 42) self.main_classifier.fit(X, y, 'unequal width classifier') self.assertEqual(len(self.main_classifier.get_labels_list()), 3) def test_predict(self): self.fail() def test_find_label_position(self): self.fail() def test_score(self): self.fail() def test_status(self): self.fail()
def setUp(self): self.main_classifier = TieredLayeredNeuralNetwork(feature_count)