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)
def setUp(self):
self.main_classifier = TieredLayeredNeuralNetwork(feature_count)
