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"""!
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@brief Wrapper for CCORE library (part of this project).
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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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from ctypes import *;
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from pyclustering.core.definitions import *;
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# API that is required for interaction with DLL.
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def create_pointer_data(sample):
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"Allocates memory for representing input data for processing that is described by structure 'data_representation' and returns pointer this structure."
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"(in) sample - dataset for processing."
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"Returns pointer to the data for processing."
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input_data = data_representation();
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input_data.number_objects = len(sample);
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input_data.dimension = len(sample[0]);
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pointer_objects = (POINTER(c_double) * input_data.number_objects)();
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for index in range(0, input_data.number_objects):
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point = (c_double * input_data.dimension)();
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for dimension in range(0, input_data.dimension):
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point[dimension] = sample[index][dimension];
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pointer_objects[index] = cast(point, POINTER(c_double));
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input_data.pointer_objects = cast(pointer_objects, POINTER(POINTER(c_double)));
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input_data = pointer(input_data);
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return input_data;
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def extract_clusters(ccore_result):
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"Parse clustering result that is provided by the CCORE. Return Python list of clusters."
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"(in) ccore_result - pointer to clustering result that has been returned by CCORE."
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"Returns Python list of clusters."
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pointer_clustering_result = cast(ccore_result, POINTER(clustering_result)); # clustering_result * clusters
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number_clusters = pointer_clustering_result[0].number_clusters;
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list_of_clusters = [];
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for index_cluster in range(0, number_clusters):
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clusters = cast(pointer_clustering_result[0].pointer_clusters, POINTER(cluster_representation)); # cluster_representation * cluster
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objects = cast(clusters[index_cluster].pointer_objects, POINTER(c_uint)); # cluster->objects (unsigned int *)
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list_of_clusters.append([]);
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pointer_container = list_of_clusters[index_cluster];
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for index_object in range(0, clusters[index_cluster].number_objects):
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pointer_container.append(objects[index_object]);
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return list_of_clusters;
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def extract_dynamics(ccore_result):
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"Parse dynamic result that is provided by the CCORE. Return Python tuple that represent dynamics (times, dynamic)."
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"(in) ccore_result - pointer to dynamic result that has been returned by CCORE."
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"Returns Python tuple dynamic (times, dynamic)."
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pointer_dynamic_result = cast(ccore_result, POINTER(dynamic_result)); # dynamic_result * pointer_dynamic_result
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size_dynamic = pointer_dynamic_result[0].size_dynamic;
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size_network = pointer_dynamic_result[0].size_network;
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pointer_time = cast(pointer_dynamic_result[0].times, POINTER(c_double));
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pointer_pointer_dynamic = cast(pointer_dynamic_result[0].dynamic, POINTER(POINTER(c_double)));
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times = [];
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dynamic = [];
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for index in range(0, size_dynamic):
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times.append(pointer_time[index]);
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dynamic.append([]);
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pointer_dynamic = cast(pointer_pointer_dynamic[index], POINTER(c_double));
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for object_dynamic in range(0, size_network):
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dynamic[index].append(pointer_dynamic[object_dynamic]);
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return (times, dynamic);
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# Implemented algorithms.
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"CCORE Interface for HSYNCNET oscillatory network"
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def hsyncnet_create_network(sample, number_clusters, initial_phases, initial_neighbors, increase_persent):
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pointer_data = create_pointer_data(sample);
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
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ccore.hsyncnet_create_network.restype = POINTER(c_void_p);
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pointer_network = ccore.hsyncnet_create_network(pointer_data, c_uint(number_clusters), c_uint(initial_phases), c_uint(initial_neighbors), c_double(increase_persent));
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return pointer_network;
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def hsyncnet_destroy_network(pointer_network):
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
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ccore.hsyncnet_destroy_network(pointer_network);
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def hsyncnet_process(network_pointer, order, solution, collect_dynamic):
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
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ccore.hsyncnet_process.restype = POINTER(c_void_p);
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return ccore.hsyncnet_process(network_pointer, c_double(order), c_uint(solution), c_bool(collect_dynamic));
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def hsyncnet_analyser_destroy(pointer_analyser):
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
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ccore.syncnet_analyser_destroy(pointer_analyser);
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annoviko /
pyclustering
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