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]
