def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Means algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_centers()
"""
if (self.__ccore is True):
self.__clusters = wrapper.kmeans(self.__pointer_data, self.__centers, self.__tolerance);
self.__centers = self.__update_centers();
else:
changes = float('inf');
stop_condition = self.__tolerance * self.__tolerance; # Fast solution
#stop_condition = self.__tolerance; # Slow solution
# Check for dimension
if (len(self.__pointer_data[0]) != len(self.__centers[0])):
raise NameError('Dimension of the input data and dimension of the initial cluster centers must be equal.');
while (changes > stop_condition):
self.__clusters = self.__update_clusters();
updated_centers = self.__update_centers(); # changes should be calculated before asignment
#changes = max([euclidean_distance(self.__centers[index], updated_centers[index]) for index in range(len(self.__centers))]); # Slow solution
changes = max([euclidean_distance_sqrt(self.__centers[index], updated_centers[index]) for index in range(len(updated_centers))]); # Fast solution
self.__centers = updated_centers;
def __process_by_ccore(self):
"""!
@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
"""
results = wrapper.kmeans(self.__pointer_data, self.__centers,
self.__tolerance,
(self.__observer is not None))
self.__clusters = results[0]
self.__centers = results[1]
if self.__observer is not None:
self.__observer.set_evolution_clusters(results[2])
self.__observer.set_evolution_centers(results[3])
def __process_by_ccore(self):
"""!
@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
"""
ccore_metric = metric_wrapper.create_instance(self.__metric)
results = wrapper.kmeans(self.__pointer_data, self.__centers, self.__tolerance, self.__itermax, (self.__observer is not None), ccore_metric.get_pointer())
self.__clusters = results[0]
self.__centers = results[1]
if self.__observer is not None:
self.__observer.set_evolution_clusters(results[2])
self.__observer.set_evolution_centers(results[3])
self.__total_wce = results[4][0]
def __process_by_ccore(self):
"""!
@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
"""
ccore_metric = metric_wrapper.create_instance(self.__metric)
results = wrapper.kmeans(self.__pointer_data, self.__centers, self.__tolerance, self.__itermax, (self.__observer is not None), ccore_metric.get_pointer())
self.__clusters = results[0]
self.__centers = results[1]
if self.__observer is not None:
self.__observer.set_evolution_clusters(results[2])
self.__observer.set_evolution_centers(results[3])
self.__total_wce = results[4][0]
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Means algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
@see get_centers()
"""
if (self.__ccore is True):
self.__clusters = wrapper.kmeans(self.__pointer_data,
self.__centers, self.__tolerance)
self.__centers = self.__update_centers()
else:
maximum_change = float('inf')
stop_condition = self.__tolerance * self.__tolerance
# Fast solution
#stop_condition = self.__tolerance; # Slow solution
# Check for dimension
if (len(self.__pointer_data[0]) != len(self.__centers[0])):
raise NameError(
'Dimension of the input data and dimension of the initial cluster centers must be equal.'
)
while (maximum_change > stop_condition):
self.__clusters = self.__update_clusters()
updated_centers = self.__update_centers()
# changes should be calculated before asignment
if (len(self.__centers) != len(updated_centers)):
maximum_change = float('inf')
else:
changes = numpy.sum(numpy.square(self.__centers -
updated_centers),
axis=1)
maximum_change = numpy.max(changes)
self.__centers = updated_centers
