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"""!
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@brief Cluster analysis algorithm: SOM-SC (Self-Organized Feature Map for Simple Clustering)
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@details Based on article description:
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- no reference
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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.nnet.som import som;
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from pyclustering.nnet.som import type_conn;
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class somsc:
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"""!
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@brief Class represents simple clustering algorithm based on self-organized feature map.
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@details This algorithm uses amount of clusters that should be allocated as a size of SOM map. Captured objects by neurons are clusters.
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Algorithm is able to process data with Gaussian distribution that has spherical forms.
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"""
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def __init__(self, data, amount_clusters, epouch = 100, ccore = False):
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self.__data_pointer = data;
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self.__amount_clusters = amount_clusters;
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self.__epouch = epouch;
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self.__ccore = ccore;
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self.__network = None;
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def process(self):
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"""!
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@brief Performs cluster analysis in line with rules of K-Means algorithm.
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@remark Results of clustering can be obtained using corresponding get methods.
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@see get_clusters()
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"""
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self.__network = som(1, self.__amount_clusters, type_conn.grid_four, None, self.__ccore);
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self.__network.train(self.__data_pointer, self.__epouch, True);
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def get_clusters(self):
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return self.__network.capture_objects;
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annoviko /
pyclustering
