def test_dunn_regression(self):
dunn_calculator = DunnCalculator()
expected = [0.238095238095, 0.238095238095]
for i in range(len(self.clusterings)):
self.assertAlmostEqual( dunn_calculator.evaluate( self.clusterings[i], self.matrix), expected[i],6)
def test_dunn_regression(self):
dunn_calculator = DunnCalculator()
expected = [0.238095238095, 0.238095238095]
for i in range(len(self.clusterings)):
self.assertAlmostEqual(
dunn_calculator.evaluate(self.clusterings[i], self.matrix),
expected[i], 6)
def test_max_intercluster_distance(self):
expected = [21, 21]
for i in range(len(self.clusterings)):
self.assertEqual( DunnCalculator.max_intercluster_distance(self.clusterings[i], self.matrix), expected[i])
def test_min_intracluster_distances(self):
expected = [5, 5]
for i in range(len(self.clusterings)):
self.assertEqual(DunnCalculator.min_intracluster_distances(self.clusterings[i], self.matrix), expected[i])
def test_max_intercluster_distance(self):
expected = [21, 21]
for i in range(len(self.clusterings)):
self.assertEqual(
DunnCalculator.max_intercluster_distance(
self.clusterings[i], self.matrix), expected[i])
def test_min_intracluster_distances(self):
expected = [5, 5]
for i in range(len(self.clusterings)):
self.assertEqual(
DunnCalculator.min_intracluster_distances(
self.clusterings[i], self.matrix), expected[i])
