class ClusteringTest(TestCase):
def setUp(self):
self.reader = Reader(filename)
self.clusterer = Kmeans(3)
def test_01_courses(self):
courses = self.reader.courses #returns list of courses
self.assertEqual(courses[:3],
['Bioinformatik', 'Informatik', 'Mathematik'])
def test_02_normalize(self):
word = "(Studienrichtung"
normalized_word = self.reader.normalize_word(
word) #returns list of courses
self.assertEqual(normalized_word, "studienrichtung")
def test_03_vocabulary(self):
words = self.reader.vocabulary
self.assertEqual(words[:3], ['albanologie', 'allgemeine', 'als'])
def test_04_vectorspaced(self):
word_to_vectorspace = self.reader.vectorspaced("Slavische Philologie")
vocab_size = len(self.reader.vocabulary)
self.assertEqual(vocab_size, len(word_to_vectorspace))
self.assertEqual(word_to_vectorspace, [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
])
def test_05_distance(self):
a = [1, 2, 3]
b = [4, 5, 6]
euclidean_dist = self.clusterer.distance(a, b)
self.assertEqual(int(euclidean_dist), 5)
def test_06_vector_mean(self):
vectors = [[1, 2, 3], [4, 5, 6]]
mean = self.clusterer.vector_mean(vectors)
self.assertEqual(mean, [2.5, 3.5, 4.5])
def test_07_classify(self):
vectorspaced_data = self.reader.vector_spaced_data
#clusters are always differrent
self.clusterer.train(vectorspaced_data)
clusters = [self.clusterer.classify(vec) for vec in vectorspaced_data]
self.assertEqual(len(clusters), len(vectorspaced_data))
