def test_dbscan(self): distances = CondensedMatrix([ 0., 0., 2., 2., 0., 2., 2., 2., 2., 0.]) dbscan_alg = DBSCANAlgorithm(distances) eps = 1.0 minpts = 2 clustering = dbscan_alg.perform_clustering(kwargs = {"eps":eps, "minpts":minpts}) expected = [1, 1, 1] self.assertItemsEqual(clustering.gen_class_list(starts_with = 1), expected) dbscan_alg.element_class = [PointClassType.UNCLASSIFIED]*5 eps = 1.0 minpts = 1 clustering = dbscan_alg.perform_clustering(kwargs = {"eps":eps, "minpts":minpts}) self.assertItemsEqual(clustering.gen_class_list(starts_with = 1),[1, 1, 1, 2, 2])
def test_dbscan_regression_mini(self): distances = CondensedMatrix([ 12.36931688, 5.83095189, 9.43398113, 12.52996409, 15.65247584, 17.4642492, 9.21954446, 4.47213595, 3.16227766, 4.47213595, 5.65685425, 5., 8.06225775, 11.18033989, 13.15294644, 3.16227766, 6.32455532, 8.24621125, 3.16227766, 5.09901951, 2. ]) dbscan_alg = DBSCANAlgorithm(distances) clustering = dbscan_alg.perform_clustering(kwargs = {"eps":4.0, "minpts":3}) self.assertItemsEqual(clustering.gen_class_list(starts_with = 1),[-1, 1, -1, 1, 1, 1]) #[0, 1, 0, 1, 1, 1, 0]
def test_dbscan(self): distances = CondensedMatrix([0., 0., 2., 2., 0., 2., 2., 2., 2., 0.]) dbscan_alg = DBSCANAlgorithm(distances) eps = 1.0 minpts = 2 clustering = dbscan_alg.perform_clustering(kwargs={ "eps": eps, "minpts": minpts }) expected = [1, 1, 1] self.assertItemsEqual(clustering.gen_class_list(starts_with=1), expected) dbscan_alg.element_class = [PointClassType.UNCLASSIFIED] * 5 eps = 1.0 minpts = 1 clustering = dbscan_alg.perform_clustering(kwargs={ "eps": eps, "minpts": minpts }) self.assertItemsEqual(clustering.gen_class_list(starts_with=1), [1, 1, 1, 2, 2])
def test_dbscan_regression_mini(self): distances = CondensedMatrix([ 12.36931688, 5.83095189, 9.43398113, 12.52996409, 15.65247584, 17.4642492, 9.21954446, 4.47213595, 3.16227766, 4.47213595, 5.65685425, 5., 8.06225775, 11.18033989, 13.15294644, 3.16227766, 6.32455532, 8.24621125, 3.16227766, 5.09901951, 2. ]) dbscan_alg = DBSCANAlgorithm(distances) clustering = dbscan_alg.perform_clustering(kwargs={ "eps": 4.0, "minpts": 3 }) self.assertItemsEqual(clustering.gen_class_list(starts_with=1), [-1, 1, -1, 1, 1, 1]) #[0, 1, 0, 1, 1, 1, 0]