def process(self, order = 0.998, solution = solve_type.FAST, collect_dynamic = False):
"""!
@brief Performs clustering of input data set in line with input parameters.
@param[in] order (double): Level of local synchronization between oscillator that defines end of synchronization process, range [0..1].
@param[in] solution (solve_type) Type of solving differential equation.
@param[in] collect_dynamic (bool): If True - returns whole history of process synchronization otherwise - only final state (when process of clustering is over).
@return (tuple) Returns dynamic of the network as tuple of lists on each iteration (time, oscillator_phases) that depends on collect_dynamic parameter.
@see get_clusters()
"""
if (self.__ccore_network_pointer is not None):
analyser = wrapper.hsyncnet_process(self.__ccore_network_pointer, order, solution, collect_dynamic);
return syncnet_analyser(None, None, analyser);
number_neighbors = self.__initial_neighbors;
current_number_clusters = float('inf');
dyn_phase = [];
dyn_time = [];
radius = average_neighbor_distance(self._osc_loc, number_neighbors);
increase_step = int(len(self._osc_loc) * self.__increase_persent);
if (increase_step < 1):
increase_step = 1;
analyser = None;
while(current_number_clusters > self._number_clusters):
self._create_connections(radius);
analyser = self.simulate_dynamic(order, solution, collect_dynamic);
if (collect_dynamic == True):
if (len(dyn_phase) == 0):
self.__store_dynamic(dyn_phase, dyn_time, analyser, True);
self.__store_dynamic(dyn_phase, dyn_time, analyser, False);
clusters = analyser.allocate_sync_ensembles(0.05);
# Get current number of allocated clusters
current_number_clusters = len(clusters);
# Increase number of neighbors that should be used
number_neighbors += increase_step;
# Update connectivity radius and check if average function can be used anymore
radius = self.__calculate_radius(number_neighbors, radius);
if (collect_dynamic != True):
self.__store_dynamic(dyn_phase, dyn_time, analyser, False);
return syncnet_analyser(dyn_phase, dyn_time, None);
def process(self, order = 0.998, solution = solve_type.FAST, collect_dynamic = False):
"""!
@brief Performs clustering of input data set in line with input parameters.
@param[in] order (double): Level of local synchronization between oscillator that defines end of synchronization process, range [0..1].
@param[in] solution (solve_type) Type of solving differential equation.
@param[in] collect_dynamic (bool): If True - returns whole history of process synchronization otherwise - only final state (when process of clustering is over).
@return (tuple) Returns dynamic of the network as tuple of lists on each iteration (time, oscillator_phases) that depends on collect_dynamic parameter.
@see get_clusters()
"""
if (self.__ccore_network_pointer is not None):
analyser = wrapper.hsyncnet_process(self.__ccore_network_pointer, order, solution, collect_dynamic);
return syncnet_analyser(None, None, analyser);
number_neighbors = 0;
current_number_clusters = float('inf');
dyn_phase = [];
dyn_time = [];
radius = 0.0;
while(current_number_clusters > self._number_clusters):
self._create_connections(radius);
analyser = self.simulate_dynamic(order, solution, collect_dynamic);
if (collect_dynamic == True):
dyn_phase += analyser.output;
if (len(dyn_time) > 0):
point_time_last = dyn_time[len(dyn_time) - 1];
dyn_time += [time_point + point_time_last for time_point in analyser.time];
else:
dyn_time += analyser.time;
clusters = analyser.allocate_sync_ensembles(0.05);
# Get current number of allocated clusters
current_number_clusters = len(clusters);
# Increase number of neighbors that should be used
number_neighbors += 1;
# Update connectivity radius and check if average function can be used anymore
if (number_neighbors >= len(self._osc_loc)):
radius = radius * 0.1 + radius;
else:
radius = average_neighbor_distance(self._osc_loc, number_neighbors);
return syncnet_analyser(dyn_phase, dyn_time, None);
def process(self,
order=0.998,
solution=solve_type.FAST,
collect_dynamic=False):
"""!
@brief Performs clustering of input data set in line with input parameters.
@param[in] order (double): Level of local synchronization between oscillator that defines end of synchronization process, range [0..1].
@param[in] solution (solve_type) Type of solving differential equation.
@param[in] collect_dynamic (bool): If True - returns whole history of process synchronization otherwise - only final state (when process of clustering is over).
@return (tuple) Returns dynamic of the network as tuple of lists on each iteration (time, oscillator_phases) that depends on collect_dynamic parameter.
@see get_clusters()
"""
if (self.__ccore_network_pointer is not None):
analyser = wrapper.hsyncnet_process(self.__ccore_network_pointer,
order, solution,
collect_dynamic)
return syncnet_analyser(None, None, analyser)
number_neighbors = self.__initial_neighbors
current_number_clusters = float('inf')
dyn_phase = []
dyn_time = []
radius = average_neighbor_distance(self._osc_loc, number_neighbors)
increase_step = int(len(self._osc_loc) * self.__increase_persent)
if (increase_step < 1):
increase_step = 1
analyser = None
while (current_number_clusters > self._number_clusters):
self._create_connections(radius)
analyser = self.simulate_dynamic(order, solution, collect_dynamic)
if (collect_dynamic == True):
if (len(dyn_phase) == 0):
self.__store_dynamic(dyn_phase, dyn_time, analyser, True)
self.__store_dynamic(dyn_phase, dyn_time, analyser, False)
clusters = analyser.allocate_sync_ensembles(0.05)
# Get current number of allocated clusters
current_number_clusters = len(clusters)
# Increase number of neighbors that should be used
number_neighbors += increase_step
# Update connectivity radius and check if average function can be used anymore
radius = self.__calculate_radius(number_neighbors, radius)
if (collect_dynamic != True):
self.__store_dynamic(dyn_phase, dyn_time, analyser, False)
return syncnet_analyser(dyn_phase, dyn_time, None)
