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"""! |
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@brief Cluster analysis algorithm: Ant-Mean |
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@details Implementation based on article: |
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- W.A.Tao, Y.Ma, J.H.Tian, M.Y.Li, W.S.Duan, Y.Y.Liang. An improved ant colony clustering algorithm. 2012. |
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@authors Andrei Novikov, Aleksey Kukushkin ([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.antmean_wrapper as wrapper; |
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class antmean_clustering_params: |
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"""! |
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@brief Ant-Mean algorithm parameters. |
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""" |
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def __init__(self): |
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"""! |
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@brief Constructs Ant-Mean algorithm parameters. |
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""" |
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## Used for pheramone evaporation |
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self.ro = 0.9; |
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## Initial value for pheramones |
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self.pheramone_init = 0.1; |
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## Amount of iterations that is used for solving |
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self.iterations = 50; |
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## Amount of ants that is used on each iteration |
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self.count_ants = 20; |
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class antmean: |
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"""! |
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@brief The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired |
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by the behavior of ant colonies that cluster their corpses and sort their larvae. |
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@details |
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Code example: |
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@code |
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# Define ant-colony parameters |
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params = antmean_clustering_params(); |
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params.iterations = 300; |
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params.count_ants = 200; |
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params.pheramone_init = 0.1; |
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params.ro = 0.9; |
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# Read data from text file |
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sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2); |
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# Create instance of the algorithm |
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algo = antmean(sample, 3, params); |
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# Start clustering process |
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algo.process(); |
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# Obtain allocated clusters by the colony |
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res = algo.get_clusters(); |
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# Display output result (allocated clusters) |
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print(res); |
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@endcode |
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""" |
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def __init__(self, sample, count_clusters, parameters): |
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"""! |
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@brief Construct ant mean clustering algorithm using colony parameters. |
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@details This algorithm is implemented on core side only (C/C++ part of the library). |
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@warning Ant-Mean is working using core of the library - CCORE. |
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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] count_clusters (uint): Amount of clusters that should be allocated. |
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@param[in] parameters (antmean_clustering_params): Ant colony parameters. |
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""" |
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self.__parameters = parameters if parameters is not None else antmean_clustering_params(); |
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self.__clusters = []; |
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self.__sample = sample; |
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self.__count_clusters = count_clusters; |
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def process(self): |
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"""! |
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@brief Performs cluster analysis using ant-mean colony. |
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@see get_clusters |
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""" |
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self.__clusters = wrapper.antmean_clustering_process(self.__parameters, self.__count_clusters, self.__sample); |
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def get_clusters(self): |
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"""! |
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@brief Returns allocated clusters after processing. |
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@see process |
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""" |
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return self.__clusters; |
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annoviko /
pyclustering
