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)