pyclustering 0.10.0

pyclustering 0.10.0 library is a collection of clustering algorithms and methods, oscillatory networks, etc. GENERAL CHANGES: Supported command test for setup.py script (Python: pyclustering). See: https://github.com/annoviko/pyclustering/issues/607 Introduced parameter random_seed for algorithms/models to control the seed of the random functionality: kmeans++, random_center_initializer, ga, gmeans, xmeans, som, somsc, elbow, silhouette_ksearch (Python: pyclustering.cluster; C++: pyclustering.clst). See: https://github.com/annoviko/pyclustering/issues/578 Introduced parameter k_max to G-Means algorithm to use it as an optional stop condition for the algorithm (Python: pyclustering.cluster.gmeans; C++: pyclustering::clst::gmeans). See: https://github.com/annoviko/pyclustering/issues/602 Implemented method save() for cluster_visualizer and cluster_visualizer_multidim to save visualization to file (Python: pyclustering.cluster). See: https://github.com/annoviko/pyclustering/issues/601 Optimization of CURE algorithm using balanced KD-tree (Python: pyclustering.cluster.cure; C++: pyclustering::clst::cure). See: https://github.com/annoviko/pyclustering/issues/589 Optimization of OPTICS algorithm using balanced KD-tree (Python: pyclustering.cluster.optics; C++: pyclustering::clst::optics). See: https://github.com/annoviko/pyclustering/issues/588 Optimization of DBSCAN algorithm using balanced KD-tree (Python: pyclustering.cluster.dbscan; C++: pyclustering::clst::dbscan). See: https://github.com/annoviko/pyclustering/issues/587 Implemented new optimized balanced KD-tree kdtree_balanced (Python: pyclustering.cluster.kdtree; C++: pyclustering::container::kdtree_balanced). See: https://github.com/annoviko/pyclustering/issues/379 Implemented KD-tree graphical visualizer kdtree_visualizer for KD-trees with 2-dimensional data (Python: pyclustering.container.kdtree). See: https://github.com/annoviko/pyclustering/issues/586 Updated interface of each clustering algorithm in C/C++ pyclustering cluster_data is substituted by concrete classes (C++ pyclustering::clst). See: https://github.com/annoviko/pyclustering/issues/577 CORRECTED MAJOR BUGS: Bug with wrong data type for scores in Silhouette K-search algorithm in case of using C++ (Python: pyclustering.cluster.silhouette). See: https://github.com/annoviko/pyclustering/issues/606 Bug with a random distribution in the random center initializer (Python: pyclustering.cluster.center_initializer). See: https://github.com/annoviko/pyclustering/issues/573 Bug with incorrect converting Index List and Object List to Labeling when clusters do not contains one or more points from an input data (Python pyclustering.cluster.encoder). See: https://github.com/annoviko/pyclustering/issues/596 Bug with an exception in case of using user-defined metric for K-Means algorithm (Python pyclustering.cluster.kmeans). See: https://github.com/annoviko/pyclustering/pull/600 Memory leakage in the interface between python and C++ pyclustering library in case of CURE algorithm usage (C++ pyclustering). See: https://github.com/annoviko/pyclustering/issues/58