def test_return_indeces_for_reber_classification(self):
words, _ = make_reber_classification(100, return_indeces=True)
min_index = np.min([np.min(word) for word in words])
max_index = np.max([np.max(word) for word in words])
self.assertEqual(min_index, 0)
self.assertEqual(max_index, len(avaliable_letters) - 1)
def test_reber_classification_data(self):
invalid_data_ratio = 0.5
n_words = 100
words, labels = make_reber_classification(n_words, invalid_size=invalid_data_ratio)
self.assertEqual(n_words, len(labels))
self.assertEqual(math.ceil(n_words * invalid_data_ratio), sum(labels))
for word, label in zip(words, labels):
self.assertEqual(bool(label), is_valid_by_reber(word))
def test_reber_classification_data(self):
invalid_data_ratio = 0.5
n_words = 100
words, labels = make_reber_classification(
n_words, invalid_size=invalid_data_ratio)
self.assertEqual(n_words, len(labels))
self.assertEqual(math.ceil(n_words * invalid_data_ratio), sum(labels))
for word, label in zip(words, labels):
self.assertEqual(bool(label), is_valid_by_reber(word))
def test_reber_classification_exceptions(self):
with self.assertRaisesRegexp(ValueError, "at least 2 samples"):
make_reber_classification(n_samples=1)
with self.assertRaises(ValueError):
make_reber_classification(n_samples=10, invalid_size=-1)
with self.assertRaises(ValueError):
make_reber_classification(n_samples=10, invalid_size=2)
def test_reber_classification_exceptions(self):
with self.assertRaisesRegexp(ValueError, "at least 2 samples"):
make_reber_classification(n_samples=1)
with self.assertRaises(ValueError):
make_reber_classification(n_samples=10, invalid_size=-1)
with self.assertRaises(ValueError):
make_reber_classification(n_samples=10, invalid_size=2)
