def test_gromos_seeding(self):
points = [(0, 0), (0, 1), (0, -1), (1, 0), (3, 0), (3, 1), (6, 0),
(7, 0), (7, 1), (7, -1)]
# Maximum distance of connected components is 1, for 1.5 it may discover 3 clusters.
# For 3.2 it may discover only 2
#
# 1 5 8
# | | |
# 0 - 3 4 6 - 7
# | |
# 2 9
#
matrix = CondensedMatrix(pdist(points))
kmed_alg = KMedoidsAlgorithm(matrix, rand_seed=10)
# With a small cutoff we get all 3 connected components
numpy.testing.assert_array_equal(
kmed_alg.gromos_seeding(3, 1.4), [0, 7, 4]
) # if it's 1.5 we'll have 6 instead of 7 as medoid (as it is the first one to have 3 neighbours)
# With a bigger cutoff it has to try to find the minimum cutoff for 3 clusters, then 6 is returned instead of 7
numpy.testing.assert_array_equal(kmed_alg.gromos_seeding(3, 3.2),
[3, 6, 5])
# This one is regression
numpy.testing.assert_array_equal(kmed_alg.gromos_seeding(2, 3.2),
[4, 7])
# This one should return a random sequence, so is only testable because of the rand_seed
numpy.testing.assert_array_equal(kmed_alg.gromos_seeding(2, 0), [5, 3])
def test_gromos_seeding(self):
points = [(0,0),(0,1),(0,-1),(1,0),
(3,0),(3,1),
(6,0),(7,0),(7,1),(7,-1)]
# Maximum distance of connected components is 1, for 1.5 it may discover 3 clusters.
# For 3.2 it may discover only 2
#
# 1 5 8
# | | |
# 0 - 3 4 6 - 7
# | |
# 2 9
#
matrix = CondensedMatrix(pdist(points))
kmed_alg = KMedoidsAlgorithm(matrix, rand_seed = 10)
# With a small cutoff we get all 3 connected components
numpy.testing.assert_array_equal( kmed_alg.gromos_seeding(3, 1.4),[0, 7, 4]) # if it's 1.5 we'll have 6 instead of 7 as medoid (as it is the first one to have 3 neighbours)
# With a bigger cutoff it has to try to find the minimum cutoff for 3 clusters, then 6 is returned instead of 7
numpy.testing.assert_array_equal( kmed_alg.gromos_seeding(3, 3.2),[3, 6, 5])
# This one is regression
numpy.testing.assert_array_equal( kmed_alg.gromos_seeding(2, 3.2),[4, 7])
# This one should return a random sequence, so is only testable because of the rand_seed
numpy.testing.assert_array_equal(kmed_alg.gromos_seeding(2, 0), [5, 3])
