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"""! |
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@brief Cluster analysis algorithm: BIRCH |
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@details Implementation based on article: |
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- T.Zhang, R.Ramakrishnan, M.Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. 1996. |
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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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def __init__(self): |
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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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def __init__(self, parameters): |
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self.__parameters = None |
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if (parameters is None): |
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self.__parameters = antmean_clustering_params(); |
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else: |
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self.__parameters = parameters; |
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def process(self, count_clusters, samples): |
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return wrapper.antmean_clustering_process(self.__parameters, count_clusters, samples) |
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annoviko /
pyclustering
