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"""! |
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@brief Cluster analysis algorithm: BSAS (Basic Sequential Algorithmic Scheme). |
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@details Implementation based on book: |
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- Theodoridis, Koutroumbas, Konstantinos. Elsevier Academic Press - Pattern Recognition - 2nd Edition. 2003. |
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@authors Andrei Novikov ([email protected]) |
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@date 2014-2018 |
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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.cluster.encoder import type_encoding; |
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from pyclustering.utils.metric import type_metric, distance_metric; |
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class bsas: |
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"""! |
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@brief Class represents BSAS clustering algorithm - basic sequential algorithmic scheme. |
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@details Algorithm has two mandatory parameters: maximum allowable number of clusters and threshold |
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of dissimilarity or in other words maximum distance between points. Distance metric also can |
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be specified using 'metric' parameters. BSAS using following rule for updating cluster representative: |
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\f[ |
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\vec{m}_{C_{k}}^{new}=\frac{ \left ( n_{C_{k}^{new}} - 1 \right )\vec{m}_{C_{k}}^{old} + \vec{x} }{n_{C_{k}^{new}}} |
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\f] |
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@see pyclustering.cluster.mbsas, pyclustering.cluster.ttsas |
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""" |
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def __init__(self, data, maximum_clusters, threshold, ccore=True, **kwargs): |
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"""! |
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@brief Creates classical BSAS algorithm. |
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@param[in] data (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] maximum_clusters: Maximum allowable number of clusters that can be allocated during processing. |
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@param[in] threshold: Threshold of dissimilarity (maximum distance) between points. |
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@param[in] ccore (bool): If True than DLL CCORE (C++ solution) will be used for solving. |
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@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'metric'). |
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Keyword Args: |
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metric (distance_metric): Metric that is used for distance calculation between two points. |
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""" |
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self.__data = data; |
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self.__amount = maximum_clusters; |
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self.__threshold = threshold; |
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self.__ccore = ccore; |
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self.__metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN)); |
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self.__clusters = []; |
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self.__representatives = []; |
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def process(self): |
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"""! |
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@brief Performs cluster analysis in line with rules of BSAS 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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@see get_medians() |
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""" |
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self.__clusters.append([0]); |
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self.__representatives.append(self.__data[0]); |
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for i in range(1, len(self.__data)): |
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point = self.__data[i]; |
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index_cluster, distance = self.__find_nearest_cluster(point); |
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if (distance > self.__threshold) and (len(self.__clusters) < self.__amount): |
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self.__representatives.append(point); |
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self.__clusters.append([i]); |
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else: |
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self.__clusters[index_cluster].append(i); |
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self.__update_representative(index_cluster, point); |
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def get_clusters(self): |
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"""! |
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@brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data. |
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@see process() |
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@see get_representatives() |
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""" |
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return self.__clusters; |
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def get_representatives(self): |
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"""! |
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@brief Returns list of representatives of allocated clusters. |
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@see process() |
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@see get_clusters() |
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""" |
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return self.__representatives; |
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def get_cluster_encoding(self): |
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"""! |
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@brief Returns clustering result representation type that indicate how clusters are encoded. |
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@return (type_encoding) Clustering result representation. |
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@see get_clusters() |
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""" |
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return type_encoding.CLUSTER_INDEX_LIST_SEPARATION; |
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def __find_nearest_cluster(self, point): |
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"""! |
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@brief Find nearest cluster to the specified point. |
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@param[in] point (list): Point from dataset. |
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@return (uint, double) Index of nearest cluster and distance to it. |
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""" |
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index_cluster = -1; |
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nearest_distance = float('inf'); |
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for index in range(len(self.__representatives)): |
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distance = self.__metric(point, self.__representatives[index]); |
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if distance < nearest_distance: |
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index_cluster = index; |
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nearest_distance = distance; |
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return index_cluster, nearest_distance; |
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def __update_representative(self, index_cluster, point): |
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"""! |
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@brief Update cluster representative in line with new cluster size and added point to it. |
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@param[in] index_cluster (uint): Index of cluster whose representative should be updated. |
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@param[in] point (list): Point that was added to cluster. |
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""" |
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length = len(self.__clusters[index_cluster]); |
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rep = self.__representatives[index_cluster]; |
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for dimension in range(len(rep)): |
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rep[dimension] = ( (length - 1) * rep[dimension] + point[dimension] ) / length; |
annoviko /
pyclustering
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rorRare they interpreted as regular expressions.The escape sequence that was used indicates that you might have intended to write a regular expression.
Learn more about the available escape sequences. in the Python documentation.