def test_minmax_condensed(self):
condensed_matrix = CondensedDistanceMatrix([ 1.,
4.5, 8.5,
7.2, 4.5, 7.8,
6.7, 3.6,2.2, 2.0])
expected = (1,8.5)
self.assertEqual(condensed_matrix.get_minimum_and_maximum(),expected)
def test_normalize_condensed_matrix(self):
condensed = CondensedDistanceMatrix([ 1., 4.5, 8.5, 7.2, 4.5, 7.8, 6.7, 3.6,2.2, 2.])
expected = CondensedDistanceMatrix([0.0, 0.47, 1.0, 0.83, 0.47, 0.91, 0.76, 0.35, 0.16, 0.13])
minmax = condensed.get_minimum_and_maximum()
condensed.normalize(minmax[0], minmax[1])
for i in range(len(condensed.get_data())):
self.assertAlmostEqual(condensed.get_data()[i],expected.get_data()[i],2)
def test_normalize_condensed_matrix(self):
condensed = CondensedDistanceMatrix(
[1., 4.5, 8.5, 7.2, 4.5, 7.8, 6.7, 3.6, 2.2, 2.])
expected = CondensedDistanceMatrix(
[0.0, 0.47, 1.0, 0.83, 0.47, 0.91, 0.76, 0.35, 0.16, 0.13])
minmax = condensed.get_minimum_and_maximum()
condensed.normalize(minmax[0], minmax[1])
for i in range(len(condensed.get_data())):
self.assertAlmostEqual(condensed.get_data()[i],
expected.get_data()[i], 2)
def test_minmax_condensed(self):
condensed_matrix = CondensedDistanceMatrix(
[1., 4.5, 8.5, 7.2, 4.5, 7.8, 6.7, 3.6, 2.2, 2.0])
expected = (1, 8.5)
self.assertEqual(condensed_matrix.get_minimum_and_maximum(), expected)
