def test_subsample(self):
calculator = MeanMinimumDistanceCalculator(10)
self.assertEqual( 14.5, calculator.subsample(10,20, [4,6,3,24,7,12,9,17,20,43]))
# If the percent gives less than 1 element, we use 1
self.assertEqual( 43, calculator.subsample(10,5, [4,6,3,24,7,12,9,17,20,43]))
# If the list has no elements, returns 0
self.assertEqual( 0, calculator.subsample(10,20, []))
def test_subsampled_mean_min_dist(self):
calculator = MeanMinimumDistanceCalculator(10)
clusters = [Cluster(None, elements=[0,1,2]),
Cluster(None, elements=[3,4])]
triangle = [ 1., 2., 3., 4.,
5., 6., 7.,
8., 9.,
10.]
distances = CondensedMatrix( triangle )
self.assertEqual((8.0, 6.0),calculator.subsampled_mean_min_dist(clusters[0], clusters[1], 20, distances))
def test_mini_evaluation(self):
calculator = MeanMinimumDistanceCalculator(10)
clusters = [
Cluster(None, elements=[0, 1, 2]),
Cluster(None, elements=[3, 4])
]
triangle = [1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]
distances = CondensedMatrix(triangle)
clustering = Clustering(clusters)
self.assertEqual(7.0, calculator.evaluate(clustering, distances, 20))
def test_mini_evaluation(self):
calculator = MeanMinimumDistanceCalculator(10)
clusters = [Cluster(None, elements=[0,1,2]),
Cluster(None, elements=[3,4])]
triangle = [ 1., 2., 3., 4.,
5., 6., 7.,
8., 9.,
10.]
distances = CondensedMatrix( triangle )
clustering = Clustering(clusters)
self.assertEqual(7.0, calculator.evaluate(clustering,distances,20))
def test_subsample(self):
calculator = MeanMinimumDistanceCalculator(10)
self.assertEqual(
14.5,
calculator.subsample(10, 20, [4, 6, 3, 24, 7, 12, 9, 17, 20, 43]))
# If the percent gives less than 1 element, we use 1
self.assertEqual(
43, calculator.subsample(10, 5,
[4, 6, 3, 24, 7, 12, 9, 17, 20, 43]))
# If the list has no elements, returns 0
self.assertEqual(0, calculator.subsample(10, 20, []))
def test_subsampled_mean_min_dist(self):
calculator = MeanMinimumDistanceCalculator(10)
clusters = [
Cluster(None, elements=[0, 1, 2]),
Cluster(None, elements=[3, 4])
]
triangle = [1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]
distances = CondensedMatrix(triangle)
self.assertEqual(
(8.0, 6.0),
calculator.subsampled_mean_min_dist(clusters[0], clusters[1], 20,
distances))
def test_full_run(self):
condensed_matrix = CondensedMatrix(matrix)
cmax = condensed_matrix.calculateMax()
alg = RandomAlgorithm.RandomClusteringAlgorithm(condensed_matrix)
values = []
calculator = MeanMinimumDistanceCalculator(10)
for i in range(2,20):
clustering = alg.perform_clustering({
"max_num_of_clusters":-1,
"num_clusters":i
})
values.append( calculator.evaluate(clustering, condensed_matrix, 30))
self.assertTrue(max(values) < cmax)
def test_get_min_distances(self):
distances = CondensedMatrix( [ 1., 2., 3., 4.,
5., 6., 7.,
8., 9.,
10.])
clusters = [Cluster(None, elements=[0,1,2]),
Cluster(None, elements=[3,4])]
calculator = MeanMinimumDistanceCalculator(10)
min_dists, mean = calculator.get_mean_and_min_distances(clusters[0], clusters[1], distances)
self.assertItemsEqual( [3.0, 6.0, 8.0], min_dists)
self.assertAlmostEquals(12.33,mean,2)
def test_get_min_distances(self):
distances = CondensedMatrix([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
clusters = [
Cluster(None, elements=[0, 1, 2]),
Cluster(None, elements=[3, 4])
]
calculator = MeanMinimumDistanceCalculator(10)
min_dists, mean = calculator.get_mean_and_min_distances(
clusters[0], clusters[1], distances)
self.assertItemsEqual([3.0, 6.0, 8.0], min_dists)
self.assertAlmostEquals(12.33, mean, 2)
def test_full_run(self):
condensed_matrix = CondensedMatrix(matrix)
cmax = condensed_matrix.calculateMax()
alg = RandomAlgorithm.RandomClusteringAlgorithm(condensed_matrix)
values = []
calculator = MeanMinimumDistanceCalculator(10)
for i in range(2, 20):
clustering = alg.perform_clustering({
"max_num_of_clusters": -1,
"num_clusters": i
})
values.append(calculator.evaluate(clustering, condensed_matrix,
30))
self.assertTrue(max(values) < cmax)
def test_get_distances_less_than_mean(self):
calculator = MeanMinimumDistanceCalculator(10)
self.assertItemsEqual(range(51), calculator.get_distances_less_than_mean(range(100),50))
def test_get_distances_less_than_mean(self):
calculator = MeanMinimumDistanceCalculator(10)
self.assertItemsEqual(
range(51), calculator.get_distances_less_than_mean(range(100), 50))
