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"""!
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@brief Cluster analysis algorithm: Sync
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@details Based on article description:
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- T.Miyano, T.Tsutsui. Data Synchronization as a Method of Data Mining. 2007.
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@authors Andrei Novikov ([email protected])
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@date 2014-2016
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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 matplotlib.pyplot as plt;
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import matplotlib.animation as animation;
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import math;
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from pyclustering.core.syncnet_wrapper import syncnet_create_network, syncnet_process, syncnet_destroy_network, syncnet_analyser_destroy;
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from pyclustering.nnet.sync import sync_dynamic, sync_network, sync_visualizer;
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from pyclustering.nnet import conn_represent, initial_type, conn_type, solve_type;
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from pyclustering.utils import euclidean_distance;
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from pyclustering.cluster import cluster_visualizer
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class syncnet_analyser(sync_dynamic):
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"""!
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@brief Performs analysis of output dynamic of the oscillatory network syncnet to extract information about cluster allocation.
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"""
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def __init__(self, phase, time, pointer_sync_analyser):
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"""!
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@brief Constructor of the analyser.
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@param[in] phase (list): Output dynamic of the oscillatory network, where one iteration consists of all phases of oscillators.
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@param[in] time (list): Simulation time.
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@param[in] pointer_sync_analyser (POINTER): Pointer to CCORE analyser, if specified then other arguments can be omitted.
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"""
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super().__init__(phase, time, pointer_sync_analyser);
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def __del__(self):
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"""!
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@brief Desctructor of the analyser.
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"""
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if (self._ccore_sync_dynamic_pointer is not None):
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syncnet_analyser_destroy(self._ccore_sync_dynamic_pointer);
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self._ccore_sync_dynamic_pointer = None;
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def allocate_clusters(self, eps = 0.01, indexes = None, iteration = None):
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"""!
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@brief Returns list of clusters in line with state of ocillators (phases).
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@param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one cluster.
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@param[in] indexes (list): List of real object indexes and it should be equal to amount of oscillators (in case of 'None' - indexes are in range [0; amount_oscillators]).
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@param[in] iteration (uint): Iteration of simulation that should be used for allocation.
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@return (list) List of clusters, for example [ [cluster1], [cluster2], ... ].
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@see allocate_noise()
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"""
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return self.allocate_sync_ensembles(eps, indexes, iteration);
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def allocate_noise(self):
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"""!
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@brief Returns allocated noise.
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@remark Allocated noise can be returned only after data processing (use method process() before). Otherwise empty list is returned.
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@return (list) List of indexes that are marked as a noise.
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@see allocate_clusters()
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"""
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return [];
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class syncnet_visualizer(sync_visualizer):
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"""!
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@brief Visualizer of output dynamic of oscillatory network 'syncnet' for cluster analysis.
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"""
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@staticmethod
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def animate_cluster_allocation(dataset, analyser, animation_velocity = 75, save_movie = None):
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"""!
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@brief Shows animation of output dynamic (output of each oscillator) during simulation on a circle from [0; 2pi].
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@param[in] dataset (list): Input data that was used for processing by the network.
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@param[in] analyser (syncnet_analyser): Output dynamic analyser of the Sync network.
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@param[in] animation_velocity (uint): Interval between frames in milliseconds.
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@param[in] save_movie (string): If it is specified then animation will be stored to file that is specified in this parameter.
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"""
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figure = plt.figure();
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clusters = analyser.allocate_clusters(iteration = 0);
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visualizer = cluster_visualizer();
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visualizer.append_clusters(clusters, dataset);
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visualizer.show(figure, display = False);
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actors = figure.gca();
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def init_frame():
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return [ actors ];
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def frame_generation(index_dynamic):
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figure.clf();
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clusters = analyser.allocate_clusters(iteration = index_dynamic);
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visualizer = cluster_visualizer();
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visualizer.append_clusters(clusters, dataset);
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visualizer.show(figure, display = False);
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actors = figure.gca();
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return [ actors ];
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cluster_animation = animation.FuncAnimation(figure, frame_generation, len(analyser), interval = animation_velocity, init_func = init_frame, repeat_delay = 5000);
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if (save_movie is not None):
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cluster_animation.save(save_movie, writer = 'ffmpeg', fps = 15);
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else:
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plt.show();
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class syncnet(sync_network):
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"""!
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@brief Class represents clustering algorithm SyncNet.
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@details SyncNet 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 sample for clustering from some file
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sample = read_sample(path_to_file);
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# create oscillatory network with connectivity radius 0.5 using CCORE (C++ implementation of pyclustering)
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network = syncnet(sample, 0.5, ccore = True);
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# run cluster analysis and collect output dynamic of the oscillatory network,
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# network simulation is performed by Runge Kutta Fehlberg 45.
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(dyn_time, dyn_phase) = network.process(0.998, solve_type.RFK45, True);
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# show oscillatory network
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network.show_network();
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# obtain clustering results
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clusters = network.get_clusters();
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# show clusters
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draw_clusters(sample, clusters);
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@endcode
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"""
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def __init__(self, sample, radius, conn_repr = conn_represent.MATRIX, initial_phases = initial_type.RANDOM_GAUSSIAN, enable_conn_weight = False, ccore = False):
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"""!
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@brief Contructor of the oscillatory network SYNC for cluster analysis.
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@param[in] sample (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
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@param[in] radius (double): Connectivity radius between points, points should be connected if distance between them less then the radius.
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@param[in] conn_repr (conn_represent): Internal representation of connection in the network: matrix or list. Ignored in case of usage of CCORE library.
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@param[in] initial_phases (initial_type): Type of initialization of initial phases of oscillators (random, uniformly distributed, etc.).
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@param[in] enable_conn_weight (bool): If True - enable mode when strength between oscillators depends on distance between two oscillators.
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If False - all connection between oscillators have the same strength that equals to 1 (True).
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@param[in] ccore (bool): Defines should be CCORE C++ library used instead of Python code or not.
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"""
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self.__ccore_network_pointer = None;
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if (ccore is True):
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self.__ccore_network_pointer = syncnet_create_network(sample, radius, initial_phases, enable_conn_weight);
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else:
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super().__init__(len(sample), 1, 0, conn_type.DYNAMIC, initial_phases);
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self._conn_weight = None;
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self._ena_conn_weight = enable_conn_weight;
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self._osc_loc = sample;
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self._conn_represent = conn_repr;
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# Create connections.
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if (radius is not None):
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self._create_connections(radius);
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def __del__(self):
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"""!
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@brief Destructor of oscillatory network is based on Kuramoto model.
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"""
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if (self.__ccore_network_pointer is not None):
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syncnet_destroy_network(self.__ccore_network_pointer);
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self.__ccore_network_pointer = None;
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else:
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self._osc_loc = None; # pointer to external object
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def _create_connections(self, radius):
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"""!
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@brief Create connections between oscillators in line with input radius of connectivity.
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@param[in] radius (double): Connectivity radius between oscillators.
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"""
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if (self._ena_conn_weight is True):
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self._conn_weight = [[0] * self._num_osc for index in range(0, self._num_osc, 1)];
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maximum_distance = 0;
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minimum_distance = float('inf');
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# Create connections
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for i in range(0, self._num_osc, 1):
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for j in range(i + 1, self._num_osc, 1):
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dist = euclidean_distance(self._osc_loc[i], self._osc_loc[j]);
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if (self._ena_conn_weight is True):
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self._conn_weight[i][j] = dist;
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self._conn_weight[j][i] = dist;
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if (dist > maximum_distance): maximum_distance = dist;
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if (dist < minimum_distance): minimum_distance = dist;
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if (dist <= radius):
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self.set_connection(i, j);
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if (self._ena_conn_weight is True):
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multiplier = 1;
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subtractor = 0;
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if (maximum_distance != minimum_distance):
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multiplier = (maximum_distance - minimum_distance);
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subtractor = minimum_distance;
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for i in range(0, self._num_osc, 1):
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for j in range(i + 1, self._num_osc, 1):
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value_conn_weight = (self._conn_weight[i][j] - subtractor) / multiplier;
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self._conn_weight[i][j] = value_conn_weight;
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self._conn_weight[j][i] = value_conn_weight;
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def process(self, order = 0.998, solution = solve_type.FAST, collect_dynamic = True):
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"""!
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@brief Peforms cluster analysis using simulation of the oscillatory network.
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@param[in] order (double): Order of synchronization that is used as indication for stopping processing.
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@param[in] solution (solve_type): Specified type of solving diff. equation.
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@param[in] collect_dynamic (bool): Specified requirement to collect whole dynamic of the network.
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@return (syncnet_analyser) Returns analyser of results of clustering.
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"""
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if (self.__ccore_network_pointer is not None):
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pointer_output_dynamic = syncnet_process(self.__ccore_network_pointer, order, solution, collect_dynamic);
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return syncnet_analyser(None, None, pointer_output_dynamic);
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else:
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output_sync_dynamic = self.simulate_dynamic(order, solution, collect_dynamic);
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return syncnet_analyser(output_sync_dynamic.output, output_sync_dynamic.time, None);
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292
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def _phase_kuramoto(self, teta, t, argv):
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293
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"""!
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294
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@brief Overrided method for calculation of oscillator phase.
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296
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View Code Duplication |
@param[in] teta (double): Current value of phase.
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297
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@param[in] t (double): Time (can be ignored).
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298
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@param[in] argv (uint): Index of oscillator whose phase represented by argument teta.
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299
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300
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@return (double) New value of phase of oscillator with index 'argv'.
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View Code Duplication |
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302
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"""
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304
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index = argv; # index of oscillator
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305
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phase = 0.0; # phase of a specified oscillator that will calculated in line with current env. states.
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306
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307
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neighbors = self.get_neighbors(index);
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308
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for k in neighbors:
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309
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View Code Duplication |
conn_weight = 1.0;
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310
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if (self._ena_conn_weight is True):
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311
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conn_weight = self._conn_weight[index][k];
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312
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313
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phase += conn_weight * self._weight * math.sin(self._phases[k] - teta);
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314
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View Code Duplication |
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315
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divider = len(neighbors);
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316
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if (divider == 0):
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317
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divider = 1.0;
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318
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319
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return ( self._freq[index] + (phase / divider) );
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320
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321
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322
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def show_network(self):
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323
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"""!
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324
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@brief Shows connections in the network. It supports only 2-d and 3-d representation.
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325
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326
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"""
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327
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328
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if (self.__ccore_network_pointer is not None):
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raise NameError("Not supported for CCORE");
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330
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331
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dimension = len(self._osc_loc[0]);
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332
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if ( (dimension != 3) and (dimension != 2) ):
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333
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raise NameError('Network that is located in different from 2-d and 3-d dimensions can not be represented');
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334
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335
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from matplotlib.font_manager import FontProperties;
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336
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from matplotlib import rcParams;
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337
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338
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rcParams['font.sans-serif'] = ['Arial'];
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339
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rcParams['font.size'] = 12;
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340
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341
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fig = plt.figure();
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342
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axes = None;
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343
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if (dimension == 2):
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344
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axes = fig.add_subplot(111);
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345
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elif (dimension == 3):
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346
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axes = fig.gca(projection='3d');
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347
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348
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surface_font = FontProperties();
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349
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surface_font.set_name('Arial');
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350
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surface_font.set_size('12');
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351
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352
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for i in range(0, self._num_osc, 1):
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353
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if (dimension == 2):
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354
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axes.plot(self._osc_loc[i][0], self._osc_loc[i][1], 'bo');
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355
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if (self._conn_represent == conn_represent.MATRIX):
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356
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for j in range(i, self._num_osc, 1): # draw connection between two points only one time
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357
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if (self.has_connection(i, j) == True):
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358
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axes.plot([self._osc_loc[i][0], self._osc_loc[j][0]], [self._osc_loc[i][1], self._osc_loc[j][1]], 'b-', linewidth = 0.5);
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359
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360
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else:
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361
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for j in self.get_neighbors(i):
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362
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if ( (self.has_connection(i, j) == True) and (i > j) ): # draw connection between two points only one time
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363
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axes.plot([self._osc_loc[i][0], self._osc_loc[j][0]], [self._osc_loc[i][1], self._osc_loc[j][1]], 'b-', linewidth = 0.5);
|
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364
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|
|
365
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elif (dimension == 3):
|
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366
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|
axes.scatter(self._osc_loc[i][0], self._osc_loc[i][1], self._osc_loc[i][2], c = 'b', marker = 'o');
|
|
367
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|
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|
|
368
|
|
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if (self._conn_represent == conn_represent.MATRIX):
|
|
369
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for j in range(i, self._num_osc, 1): # draw connection between two points only one time
|
|
370
|
|
|
if (self.has_connection(i, j) == True):
|
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371
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|
|
axes.plot([self._osc_loc[i][0], self._osc_loc[j][0]], [self._osc_loc[i][1], self._osc_loc[j][1]], [self._osc_loc[i][2], self._osc_loc[j][2]], 'b-', linewidth = 0.5);
|
|
372
|
|
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|
|
373
|
|
|
else:
|
|
374
|
|
|
for j in self.get_neighbors(i):
|
|
375
|
|
|
if ( (self.has_connection(i, j) == True) and (i > j) ): # draw connection between two points only one time
|
|
376
|
|
|
axes.plot([self._osc_loc[i][0], self._osc_loc[j][0]], [self._osc_loc[i][1], self._osc_loc[j][1]], [self._osc_loc[i][2], self._osc_loc[j][2]], 'b-', linewidth = 0.5);
|
|
377
|
|
|
|
|
378
|
|
|
plt.grid();
|
|
379
|
|
|
plt.show();
|
|
380
|
|
|
|
|
381
|
|
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|
annoviko /
pyclustering
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