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"""!
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@brief Cluster analysis algorithm: DBSCAN.
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@details Implementation based on article:
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- M.Ester, H.Kriegel, J.Sander, X.Xiaowei. A density-based algorithm for discovering clusters in large spatial databases with noise. 1996.
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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 enum import IntEnum;
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from pyclustering.container.kdtree import kdtree;
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from pyclustering.cluster.encoder import type_encoding;
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from pyclustering.core.wrapper import ccore_library;
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from pyclustering.utils import get_argument;
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import pyclustering.core.dbscan_wrapper as wrapper;
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class dbscan:
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"""!
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@brief Class represents clustering algorithm DBSCAN.
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@details This DBSCAN algorithm is KD-tree optimized.
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CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
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Example:
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@code
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# sample for cluster analysis (represented by list)
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sample = read_sample(path_to_sample);
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# create object that uses CCORE for processing
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dbscan_instance = dbscan(sample, 0.5, 3, True);
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# cluster analysis
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dbscan_instance.process();
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# obtain results of clustering
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clusters = dbscan_instance.get_clusters();
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noise = dbscan_instance.get_noise();
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@endcode
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"""
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def __init__(self, data, eps, neighbors, ccore = True, **kwargs):
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"""!
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@brief Constructor of clustering algorithm DBSCAN.
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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] eps (double): Connectivity radius between points, points may be connected if distance between them less then the radius.
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@param[in] neighbors (uint): minimum number of shared neighbors that is required for establish links between points.
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@param[in] ccore (bool): if True than DLL CCORE (C++ solution) will be used for solving the problem.
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@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'data_type').
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<b>Keyword Args:</b><br>
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- data_type (string): Data type of input sample 'data' that is processed by the algorithm ('points', 'distance_matrix').
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"""
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self.__pointer_data = data;
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self.__kdtree = None;
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self.__eps = eps;
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self.__sqrt_eps = eps * eps;
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self.__neighbors = neighbors;
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self.__visited = [False] * len(self.__pointer_data);
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self.__belong = [False] * len(self.__pointer_data);
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self.__data_type = kwargs.get('data_type', 'points');
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self.__clusters = [];
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self.__noise = [];
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self.__neighbor_searcher = self.__create_neighbor_searcher(self.__data_type);
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self.__ccore = ccore;
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if self.__ccore:
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self.__ccore = ccore_library.workable();
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def process(self):
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"""!
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@brief Performs cluster analysis in line with rules of DBSCAN algorithm.
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@see get_clusters()
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@see get_noise()
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"""
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if self.__ccore is True:
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(self.__clusters, self.__noise) = wrapper.dbscan(self.__pointer_data, self.__eps, self.__neighbors, self.__data_type);
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else:
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if self.__data_type == 'points':
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self.__kdtree = kdtree(self.__pointer_data, range(len(self.__pointer_data)));
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for i in range(0, len(self.__pointer_data)):
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if self.__visited[i] is False:
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cluster = self.__expand_cluster(i);
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if cluster is not None:
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self.__clusters.append(cluster);
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else:
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self.__noise.append(i);
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self.__belong[i] = True;
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def get_clusters(self):
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"""!
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@brief Returns allocated clusters.
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@remark Allocated clusters can be returned only after data processing (use method process()). Otherwise empty list is returned.
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@return (list) 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_noise()
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"""
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return self.__clusters;
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def get_noise(self):
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"""!
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@brief Returns allocated noise.
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@remark Allocated noise can be returned only after data processing (use method process() before). Otherwise empty list is returned.
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@return (list) List of indexes that are marked as a noise.
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@see process()
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@see get_clusters()
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"""
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return self.__noise;
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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 __create_neighbor_searcher(self, data_type):
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"""!
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@brief Returns neighbor searcher in line with data type.
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@param[in] data_type (string): Data type (points or distance matrix).
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"""
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if data_type == 'points':
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return self.__neighbor_indexes_points;
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elif data_type == 'distance_matrix':
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return self.__neighbor_indexes_distance_matrix;
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else:
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raise TypeError("Unknown type of data is specified '%s'" % data_type);
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def __expand_cluster(self, index_point):
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"""!
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@brief Expands cluster from specified point in the input data space.
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@param[in] index_point (list): Index of a point from the data.
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@return (list) Return tuple of list of indexes that belong to the same cluster and list of points that are marked as noise: (cluster, noise), or None if nothing has been expanded.
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"""
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cluster = None;
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self.__visited[index_point] = True;
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neighbors = self.__neighbor_searcher(index_point);
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if len(neighbors) >= self.__neighbors:
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cluster = [ index_point ];
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self.__belong[index_point] = True;
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for i in neighbors:
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if self.__visited[i] is False:
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self.__visited[i] = True;
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next_neighbors = self.__neighbor_searcher(i);
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if len(next_neighbors) >= self.__neighbors:
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# if some node has less then minimal number of neighbors than we shouldn't look at them
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# because maybe it's a noise.
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neighbors += [k for k in next_neighbors if ( (k in neighbors) == False)];
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if self.__belong[i] is False:
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cluster.append(i);
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self.__belong[i] = True;
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return cluster;
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def __neighbor_indexes_points(self, index_point):
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"""!
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@brief Return neighbors of the specified object in case of sequence of points.
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@param[in] index_point (uint): Index point whose neighbors are should be found.
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@return (list) List of indexes of neighbors in line the connectivity radius.
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"""
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kdnodes = self.__kdtree.find_nearest_dist_nodes(self.__pointer_data[index_point], self.__eps);
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return [node_tuple[1].payload for node_tuple in kdnodes if node_tuple[1].payload != index_point];
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def __neighbor_indexes_distance_matrix(self, index_point):
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"""!
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@brief Return neighbors of the specified object in case of distance matrix.
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@param[in] index_point (uint): Index point whose neighbors are should be found.
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@return (list) List of indexes of neighbors in line the connectivity radius.
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"""
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distances = self.__pointer_data[index_point];
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return [index_neighbor for index_neighbor in range(len(distances))
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if ((distances[index_neighbor] <= self.__eps) and (index_neighbor != index_point))]; |
annoviko /
pyclustering
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