def test_nonseeded_clustering(self):
ngrams = NGramSpace(1)
docs = [ngrams.parse(raw) for raw in test_docs]
c = Clustering(docs)
self.assertEqual((1, 0), c.min_link())
c.merge(1, 0)
self.assertEqual((2, 1), c.min_link())
c.merge(2, 1)
self.assertTrue(c.min_link() in [(4, 3), (5, 3)])
c.merge(3, 4)
c.merge(3, 5)
self.assertEqual((7, 6), c.min_link())
def test_nonseeded_clustering(self):
ngrams = NGramSpace(1)
docs = [ngrams.parse(raw) for raw in test_docs]
c = Clustering(docs)
self.assertEqual((1, 0), c.min_link())
c.merge(1, 0)
self.assertEqual((2, 1), c.min_link())
c.merge(2, 1)
self.assertTrue(c.min_link() in [(4, 3), (5, 3)])
c.merge(3, 4)
c.merge(3, 5)
self.assertEqual((7, 6), c.min_link())
def test_clustering(self):
raw_docs = ["a b c", "b c d", "d e f"]
ngrams = NGramSpace(1)
docs = [ngrams.parse(raw) for raw in raw_docs]
c = Clustering(docs)
self.assertEqual((1, 0), c.min_link())
c.merge(1, 0)
self.assertEqual([1, 1, 2], c.assignments)
self.assertEqual((2, 1), c.min_link())
c.merge(2, 0)
self.assertEqual([2, 2, 2], c.assignments)
def test_clustering(self):
raw_docs = ['a b c', 'b c d', 'd e f']
ngrams = NGramSpace(1)
docs = [ngrams.parse(raw) for raw in raw_docs]
c = Clustering(docs)
self.assertEqual((1, 0), c.min_link())
c.merge(1, 0)
self.assertEqual([1, 1, 2], c.assignments)
self.assertEqual((2, 1), c.min_link())
c.merge(2, 0)
self.assertEqual([2, 2, 2], c.assignments)
