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"""!
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@brief Cluster analysis algorithm: Hierarchical Sync (HSyncNet)
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@details Based on article description:
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- J.Shao, X.He, C.Bohm, Q.Yang, C.Plant. Synchronization-Inspired Partitioning and Hierarchical Clustering. 2013.
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@authors Andrei Novikov ([email protected])
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@date 2014-2017
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@copyright GNU Public License
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@cond GNU_PUBLIC_LICENSE
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PyClustering is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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PyClustering is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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@endcond
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"""
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import pyclustering.core.wrapper as wrapper;
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from pyclustering.nnet import initial_type, solve_type;
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from pyclustering.cluster.syncnet import syncnet, syncnet_analyser;
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from pyclustering.utils import average_neighbor_distance;
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class hsyncnet(syncnet):
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"""!
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@brief Class represents clustering algorithm HSyncNet. HSyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model.
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Example:
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@code
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# read list of points for cluster analysis
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sample = read_sample(file);
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# create network for allocation three clusters using CCORE (C++ implementation)
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network = hsyncnet(sample, 3, ccore = True);
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# run cluster analysis and output dynamic of the network
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(time, dynamic) = network.process(0.995, collect_dynamic = True);
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# get allocated clusters
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clusters = network.get_clusters();
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# show output dynamic of the network
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draw_dynamics(time, dynamic);
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@endcode
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"""
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def __init__(self, source_data, number_clusters, osc_initial_phases = initial_type.RANDOM_GAUSSIAN, initial_neighbors = 3, increase_persent = 0.15, ccore = False):
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"""!
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@brief Costructor of the oscillatory network hSyncNet for cluster analysis.
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@param[in] source_data (list): Input data set defines structure of the network.
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@param[in] number_clusters (uint): Number of clusters that should be allocated.
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@param[in] osc_initial_phases (initial_type): Type of initialization of initial values of phases of oscillators.
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@param[in] initial_neighbors (uint): Defines initial radius connectivity by calculation average distance to connect specify number of oscillators.
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@param[in] increase_persent (double): Percent of increasing of radius connectivity on each step (input values in range (0.0; 1.0) correspond to (0%; 100%)).
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@param[in] ccore (bool): If True than DLL CCORE (C++ solution) will be used for solving.
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"""
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self.__ccore_network_pointer = None;
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if (initial_neighbors >= len(source_data)):
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initial_neighbors = len(source_data) - 1;
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if (ccore is True):
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self.__ccore_network_pointer = wrapper.hsyncnet_create_network(source_data, number_clusters, osc_initial_phases, initial_neighbors, increase_persent);
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else:
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super().__init__(source_data, 0, initial_phases = osc_initial_phases);
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self.__initial_neighbors = initial_neighbors;
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self.__increase_persent = increase_persent;
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self._number_clusters = number_clusters;
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def __del__(self):
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"""!
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@brief Destructor of oscillatory network HSyncNet.
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"""
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if (self.__ccore_network_pointer is not None):
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wrapper.hsyncnet_destroy_network(self.__ccore_network_pointer);
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self.__ccore_network_pointer = None;
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def process(self, order = 0.998, solution = solve_type.FAST, collect_dynamic = False):
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"""!
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@brief Performs clustering of input data set in line with input parameters.
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@param[in] order (double): Level of local synchronization between oscillator that defines end of synchronization process, range [0..1].
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@param[in] solution (solve_type) Type of solving differential equation.
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@param[in] collect_dynamic (bool): If True - returns whole history of process synchronization otherwise - only final state (when process of clustering is over).
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@return (tuple) Returns dynamic of the network as tuple of lists on each iteration (time, oscillator_phases) that depends on collect_dynamic parameter.
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@see get_clusters()
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"""
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if (self.__ccore_network_pointer is not None):
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analyser = wrapper.hsyncnet_process(self.__ccore_network_pointer, order, solution, collect_dynamic);
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return syncnet_analyser(None, None, analyser);
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number_neighbors = self.__initial_neighbors;
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current_number_clusters = float('inf');
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dyn_phase = [];
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dyn_time = [];
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radius = average_neighbor_distance(self._osc_loc, number_neighbors);
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increase_step = int(len(self._osc_loc) * self.__increase_persent);
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if (increase_step < 1):
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increase_step = 1;
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analyser = None;
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while(current_number_clusters > self._number_clusters):
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self._create_connections(radius);
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analyser = self.simulate_dynamic(order, solution, collect_dynamic);
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if (collect_dynamic == True):
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if (len(dyn_phase) == 0):
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dyn_time.append(0);
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dyn_phase.append(analyser.output[0]);
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dyn_phase.append(analyser.output[len(analyser.output) - 1]);
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dyn_time.append(len(dyn_time));
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clusters = analyser.allocate_sync_ensembles(0.05);
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# Get current number of allocated clusters
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current_number_clusters = len(clusters);
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# Increase number of neighbors that should be used
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number_neighbors += increase_step;
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# Update connectivity radius and check if average function can be used anymore
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if (number_neighbors >= len(self._osc_loc)):
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radius = radius * self.__increase_persent + radius;
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else:
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radius = average_neighbor_distance(self._osc_loc, number_neighbors);
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if (collect_dynamic != True):
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dyn_phase = analyser.output;
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dyn_time = analyser.time;
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return syncnet_analyser(dyn_phase, dyn_time, None);
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annoviko /
pyclustering
