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_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_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)