def templateTreeVisualization(self, path, **kwargs):
sample = None
if path is not None:
sample = read_sample(path)
exception = kwargs.get('exception', None)
if exception is not None:
self.assertRaises(exception, kdtree_visualizer, kdtree(sample))
self.assertRaises(exception, kdtree_visualizer,
kdtree_balanced(sample))
else:
kdtree_visualizer(kdtree(sample)).visualize()
kdtree_visualizer(kdtree_balanced(sample)).visualize()
def templateSeachNearestNodeInBalancedTree(self,
sample_path,
generate_payload=False,
**kwargs):
numpy_usage = kwargs.get('numpy_usage', False)
sample = read_sample(sample_path)
if numpy_usage is True:
sample = numpy.array(sample)
payloads = None
if generate_payload is True:
payloads = [random.randint(1, 1000) for _ in range(len(sample))]
tree = kdtree_balanced(sample, payloads)
for i in range(len(sample)):
node = tree.find_nearest_dist_node(sample[i], 0.0)
self.assertIsNotNone(node)
if numpy_usage is False:
self.assertIs(node.data, sample[i])
else:
self.assertTrue((node.data == sample[i]).all())
if payloads is not None:
self.assertEqual(node.payload, payloads[i])
def process(self):
"""!
@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
@return (dbscan) Returns itself (DBSCAN instance).
@see get_clusters()
@see get_noise()
"""
if self.__ccore is True:
(self.__clusters,
self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps,
self.__neighbors, self.__data_type)
else:
if self.__data_type == 'points':
self.__kdtree = kdtree_balanced(
self.__pointer_data, range(len(self.__pointer_data)))
for i in range(0, len(self.__pointer_data)):
if self.__visited[i] is False:
cluster = self.__expand_cluster(i)
if cluster is not None:
self.__clusters.append(cluster)
for i in range(0, len(self.__pointer_data)):
if self.__belong[i] is False:
self.__noise.append(i)
return self
def __process_by_python(self):
"""!
@brief Performs cluster analysis using python code.
"""
if self.__data_type == 'points':
self.__kdtree = kdtree_balanced(self.__sample_pointer,
range(len(self.__sample_pointer)))
self.__allocate_clusters()
if (self.__amount_clusters is not None) and (self.__amount_clusters !=
len(self.get_clusters())):
analyser = ordering_analyser(self.get_ordering())
radius, _ = analyser.calculate_connvectivity_radius(
self.__amount_clusters)
if radius is not None:
self.__eps = radius
self.__allocate_clusters()