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"""! |
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@brief CCORE Wrapper for clustering Ant Means algorithm. |
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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 pyclustering.core.wrapper import *; |
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import types; |
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class c_antcolony_clustering_parameters(Structure): |
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""" |
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double ro; |
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double pheramone_init; |
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unsigned int iterations; |
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unsigned int count_ants; |
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""" |
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_fields_ = [("ro" , c_double), |
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("pheramone_init" , c_double), |
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("iterations" , c_uint), |
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("count_ants" , c_uint) ]; |
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def antmean_clustering_process(params, count_clusters, samples): |
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ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64); |
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algorithm_params = c_antcolony_clustering_parameters(); |
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algorithm_params.ro = c_double(params.ro); |
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algorithm_params.pheramone_init = c_double(params.pheramone_init); |
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algorithm_params.iterations = c_uint(params.iterations); |
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algorithm_params.count_ants = c_uint(params.count_ants); |
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algorithm_params = pointer(algorithm_params); |
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p_samples = create_pointer_data(samples) |
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""" |
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Run algorithm |
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""" |
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res = ccore.ant_mean_clustering(p_samples, algorithm_params, count_clusters) |
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res = cast(res, POINTER(clustering_result)); |
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""" |
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Cast result to python view |
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""" |
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pointer_data = cast(res[0].pointer_clusters, POINTER(cluster_representation)) |
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num_clusters = res[0].number_clusters |
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pyResult = [[] for i in range(num_clusters)] |
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for i in range(num_clusters): |
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for j in range(pointer_data[i].number_objects): |
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pyResult[i].append(pointer_data[i].pointer_objects[j]) |
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return pyResult |
annoviko /
pyclustering
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