def get_parameters(self):
"""
This function creates some parameters to be used with DBScan.
@return: A tuple with the generated parameters and an empty list corresponding to the clusterings.
"""
run_parameters = []
# (minpts, eps tuples)
if "max" in self.parameters["clustering"]["algorithms"]["dbscan"]:
max_eps_tries = self.parameters["clustering"]["algorithms"]["dbscan"]["max"]
else:
max_eps_tries = 10
num_elements = self.distance_matrix.row_length
klist = k_scale_gen(math.log(num_elements))
buffer = numpy.empty(num_elements)
kdist_matrix = k_dist(klist, buffer, self.distance_matrix)
dbscan_param_pairs = dbscanTools.dbscan_param_space_search(self.parameters["clustering"]["evaluation"]["maximum_noise"],
max_eps_tries,
num_elements,
klist,
kdist_matrix) +\
zhou_adaptative_determination(kdist_matrix, self.distance_matrix)
for (minpts, eps) in dbscan_param_pairs:
run_parameter = ParametersGenerator.get_base_parameters()
run_parameter["minpts"] = minpts
run_parameter["eps"] = eps
run_parameters.append(run_parameter)
return run_parameters, []
def test_k_scale_gen(self):
self.assertItemsEqual(k_scale_gen(100), [2, 4, 8, 16, 32, 64, 128])
self.assertItemsEqual(k_scale_gen(7), [2, 4, 8])
self.assertItemsEqual(k_scale_gen(0), [])
def test_k_scale_gen(self):
self.assertItemsEqual( k_scale_gen(100), [2, 4, 8, 16, 32, 64,128])
self.assertItemsEqual( k_scale_gen(7), [2, 4, 8])
self.assertItemsEqual( k_scale_gen(0), [])
