| ▼Npyclustering | PyClustering module that consists of general modules related to clustering, graph coloring, containers, oscillatory networks |
| ▼Ncluster | Pyclustering module for cluster analysis |
| ►Nagglomerative | Cluster analysis algorithm: agglomerative algorithm |
| Cagglomerative | Class represents agglomerative algorithm for cluster analysis |
| Ctype_link | Enumerator of types of link between clusters |
| ►Nbang | Cluster analysis algorithm: BANG |
| Cbang | Class implements BANG grid based clustering algorithm |
| Cbang_animator | Provides service for creating 2-D animation using BANG clustering results |
| Cbang_block | BANG-block that represent spatial region in data space |
| Cbang_directory | BANG directory stores BANG-blocks that represents grid in data space |
| Cbang_visualizer | Visualizer of BANG algorithm's results |
| Cspatial_block | Geometrical description of BANG block in data space |
| ►Nbirch | BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) cluster analysis algorithm |
| Cbirch | Class represents the clustering algorithm BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) |
| ►Nbsas | Cluster analysis algorithm: BSAS (Basic Sequential Algorithmic Scheme) |
| Cbsas | Class represents BSAS clustering algorithm - basic sequential algorithmic scheme |
| Cbsas_visualizer | Visualizer of BSAS algorithm's results |
| ►Ncenter_initializer | Collection of center initializers for algorithm that uses initial centers, for example, for K-Means or X-Means |
| Ckmeans_plusplus_initializer | K-Means++ is an algorithm for choosing the initial centers for algorithms like K-Means or X-Means |
| Crandom_center_initializer | Random center initializer is for generation specified amount of random of centers for specified data |
| ►Nclarans | Cluster analysis algorithm: CLARANS |
| Cclarans | Class represents clustering algorithm CLARANS (a method for clustering objects for spatial data mining) |
| ►Nclique | Cluster analysis algorithm: CLIQUE |
| Cclique | Class implements CLIQUE grid based clustering algorithm |
| Cclique_block | CLIQUE block contains information about its logical location in grid, spatial location in data space and points that are covered by the block |
| Cclique_visualizer | Visualizer of CLIQUE algorithm's results |
| Ccoordinate_iterator | Coordinate iterator is used to generate logical location description for each CLIQUE block |
| Cspatial_block | Geometrical description of CLIQUE block in data space |
| ►Ncure | Cluster analysis algorithm: CURE |
| Ccure | Class represents clustering algorithm CURE with KD-tree optimization |
| Ccure_cluster | Represents data cluster in CURE term |
| ►Ndbscan | Cluster analysis algorithm: DBSCAN |
| Cdbscan | Class represents clustering algorithm DBSCAN |
| ►Nelbow | Elbow method to determine the optimal number of clusters for k-means clustering |
| Celbow | Class represents Elbow method that is used to find out appropriate amount of clusters in a dataset |
| ►Nema | Cluster analysis algorithm: Expectation-Maximization Algorithm for Gaussian Mixture Model |
| Cema | Expectation-Maximization clustering algorithm for Gaussian Mixture Model (GMM) |
| Cema_init_type | Enumeration of initialization types for Expectation-Maximization algorithm |
| Cema_initializer | Provides servies for preparing initial means and covariances for Expectation-Maximization algorithm |
| Cema_observer | Observer of EM algorithm for collecting algorithm state on each step |
| Cema_visualizer | Visualizer of EM algorithm's results |
| ►Nencoder | Module for representing clustering results |
| Ccluster_encoder | Provides service to change clustering result representation |
| Ctype_encoding | Enumeration of encoding types (index labeling, index list separation, object list separation) |
| ►Nfcm | Cluster analysis algorithm: Fuzzy C-Means |
| Cfcm | Class represents Fuzzy C-means (FCM) clustering algorithm |
| ►Nga | Cluster analysis algorithm: Genetic clustering algorithm (GA) |
| Cga_observer | Genetic algorithm observer that is used to collect information about clustering process on each iteration |
| Cga_visualizer | Genetic algorithm visualizer is used to show clustering results that are specific for this particular algorithm: clusters, evolution of global and local optimum |
| Cgenetic_algorithm | Class represents Genetic clustering algorithm |
| ►Ngenerator | Cluster generator |
| Cdata_generator | Data generator provides services to generate data with clusters with normal distribution |
| ►Ngmeans | The module contains G-Means algorithm and other related services |
| Cgmeans | Class implements G-Means clustering algorithm |
| ►Nhsyncnet | Cluster analysis algorithm: Hierarchical Sync (HSyncNet) |
| Chsyncnet | Class represents clustering algorithm HSyncNet |
| ►Nkmeans | The module contains K-Means algorithm and other related services |
| Ckmeans | Class implements K-Means clustering algorithm |
| Ckmeans_observer | Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm |
| Ckmeans_visualizer | Visualizer of K-Means algorithm's results |
| ►Nkmedians | Cluster analysis algorithm: K-Medians |
| Ckmedians | Class represents clustering algorithm K-Medians |
| ►Nkmedoids | Cluster analysis algorithm: K-Medoids |
| Ckmedoids | Class represents clustering algorithm K-Medoids |
| ►Nmbsas | Cluster analysis algorithm: MBSAS (Modified Basic Sequential Algorithmic Scheme) |
| Cmbsas | Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme) |
| ►Noptics | Cluster analysis algorithm: OPTICS (Ordering Points To Identify Clustering Structure) |
| Coptics | Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure) with KD-tree optimization (ccore options is supported) |
| Coptics_descriptor | Object description that used by OPTICS algorithm for cluster analysis |
| Cordering_analyser | Analyser of cluster ordering diagram |
| Cordering_visualizer | Cluster ordering diagram visualizer that represents dataset graphically as density-based clustering structure |
| ►Nrock | Cluster analysis algorithm: ROCK |
| Crock | Class represents clustering algorithm ROCK |
| ►Nsilhouette | Silhouette - method of interpretation and validation of consistency |
| Csilhouette | Represents Silhouette method that is used interpretation and validation of consistency |
| Csilhouette_ksearch | Represent algorithm for searching optimal number of clusters using specified K-algorithm (K-Means, K-Medians, K-Medoids) that is based on Silhouette method |
| Csilhouette_ksearch_type | Defines algorithms that can be used to find optimal number of cluster using Silhouette method |
| ►Nsomsc | Cluster analysis algorithm: SOM-SC (Self-Organized Feature Map for Simple Clustering) |
| Csomsc | Class represents simple clustering algorithm based on self-organized feature map |
| ►Nsyncnet | Cluster analysis algorithm: Sync |
| Csyncnet | Class represents clustering algorithm SyncNet |
| Csyncnet_analyser | Performs analysis of output dynamic of the oscillatory network syncnet to extract information about cluster allocation |
| Csyncnet_visualizer | Visualizer of output dynamic of oscillatory network 'syncnet' for cluster analysis |
| ►Nsyncsom | Cluster analysis algorithm: SYNC-SOM |
| Csyncsom | Class represents clustering algorithm SYNC-SOM |
| ►Nttsas | Cluster analysis algorithm: TTSAS (Two-Threshold Sequential Algorithmic Scheme) |
| Cttsas | Class represents TTSAS (Two-Threshold Sequential Algorithmic Scheme) |
| ►Nxmeans | Cluster analysis algorithm: X-Means |
| Csplitting_type | Enumeration of splitting types that can be used as splitting creation of cluster in X-Means algorithm |
| Cxmeans | Class represents clustering algorithm X-Means |
| Ccanvas_cluster_descr | Description of cluster for representation on canvas |
| Ccluster_visualizer | Common visualizer of clusters on 1D, 2D or 3D surface |
| Ccluster_visualizer_multidim | Visualizer for cluster in multi-dimensional data |
| ▼Ncontainer | Pyclustering module of data structures (containers) |
| ►Ncftree | Data Structure: CF-Tree |
| Ccfentry | Clustering feature representation |
| Ccfnode | Representation of node of CF-Tree |
| Ccfnode_type | Enumeration of CF-Node types that are used by CF-Tree |
| Ccftree | CF-Tree representation |
| Cleaf_node | Represents clustering feature leaf node |
| Cmeasurement_type | Enumeration of measurement types for CF-Tree |
| Cnon_leaf_node | Representation of clustering feature non-leaf node |
| ►Nkdtree | Data Structure: KD-Tree |
| Ckdtree | Represents KD Tree that is a space-partitioning data structure for organizing points in a k-dimensional space |
| Ckdtree_text_visualizer | KD-tree text visualizer that provides service to diplay tree structure using text representation |
| Cnode | Represents node of KD-Tree |
| ▼Ngcolor | Pyclustering module for graph coloring algorithms |
| ►Ndsatur | Graph coloring algorithm: DSATUR |
| Cdsatur | Represents DSATUR algorithm for graph coloring problem that uses greedy strategy |
| ►Nhysteresis | Graph coloring algorithm: Algorithm based on Hysteresis Oscillatory Network |
| Chysteresis_analyser | Performs analysis of output dynamic of the hysteresis oscillatory network to extract information about clusters or color allocation |
| Chysteresisgcolor | Class represents graph coloring algorithm based on hysteresis oscillatory network |
| ►Nsync | Graph coloring algorithm based on Sync Oscillatory Network |
| Csyncgcolor | Oscillatory network based on Kuramoto model with negative and positive connections for graph coloring problem |
| Csyncgcolor_analyser | Analyser of output dynamic of the oscillatory network syncgcolor |
| ▼Nnnet | Neural and oscillatory network module |
| ►Ncnn | Chaotic Neural Network |
| Ccnn_dynamic | Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored |
| Ccnn_network | Chaotic neural network based on system of logistic map where clustering phenomenon can be observed |
| Ccnn_visualizer | Visualizer of output dynamic of chaotic neural network (CNN) |
| Ctype_conn | Enumeration of connection types for Chaotic Neural Network |
| ►Ndynamic_visualizer | Output dynamic visualizer |
| Ccanvas_descr | Describes plot where dynamic is displayed |
| Cdynamic_descr | Output dynamic description that used to display |
| Cdynamic_visualizer | Basic output dynamic visualizer |
| ►Nfsync | Oscillatory Neural Network based on Kuramoto model in frequency domain |
| Cfsync_dynamic | Represents output dynamic of Sync in frequency domain |
| Cfsync_network | Model of oscillatory network that uses Landau-Stuart oscillator and Kuramoto model as a synchronization mechanism |
| Cfsync_visualizer | Visualizer of output dynamic of sync network in frequency domain |
| ►Nhhn | Oscillatory Neural Network based on Hodgkin-Huxley Neuron Model |
| Ccentral_element | Central element consist of two central neurons that are described by a little bit different dynamic than peripheral |
| Chhn_network | Oscillatory Neural Network with central element based on Hodgkin-Huxley neuron model |
| Chhn_parameters | Describes parameters of Hodgkin-Huxley Oscillatory Network |
| ►Nhysteresis | Neural Network: Hysteresis Oscillatory Network |
| Chysteresis_dynamic | Represents output dynamic of hysteresis oscillatory network |
| Chysteresis_network | Hysteresis oscillatory network that uses relaxation oscillators that are represented by objective hysteresis neurons whose output in range [-1, +1] |
| Chysteresis_visualizer | Visualizer of output dynamic of hysteresis oscillatory network |
| ►Nlegion | Neural Network: Local Excitatory Global Inhibitory Oscillatory Network (LEGION) |
| Clegion_dynamic | Represents output dynamic of LEGION |
| Clegion_network | Local excitatory global inhibitory oscillatory network (LEGION) that uses relaxation oscillator based on Van der Pol model |
| Clegion_parameters | Describes parameters of LEGION |
| ►Npcnn | Neural Network: Pulse Coupled Neural Network |
| Cpcnn_dynamic | Represents output dynamic of PCNN (pulse-coupled neural network) |
| Cpcnn_network | Model of oscillatory network that is based on the Eckhorn model |
| Cpcnn_parameters | Parameters for pulse coupled neural network |
| Cpcnn_visualizer | Visualizer of output dynamic of pulse-coupled neural network (PCNN) |
| ►Nsom | Neural Network: Self-Organized Feature Map |
| Csom | Represents self-organized feature map (SOM) |
| Csom_parameters | Represents SOM parameters |
| Ctype_conn | Enumeration of connection types for SOM |
| Ctype_init | Enumeration of initialization types for SOM |
| ►Nsync | Neural Network: Oscillatory Neural Network based on Kuramoto model |
| Corder_estimator | Provides services to calculate order parameter and local order parameter that are used for synchronization level estimation |
| Csync_dynamic | Represents output dynamic of Sync |
| Csync_network | Model of oscillatory network that is based on the Kuramoto model of synchronization |
| Csync_visualizer | Visualizer of output dynamic of sync network (Sync) |
| ►Nsyncpr | Phase oscillatory network for patten recognition based on modified Kuramoto model |
| Csyncpr | Model of phase oscillatory network for pattern recognition that is based on the Kuramoto model |
| Csyncpr_dynamic | Represents output dynamic of syncpr (Sync for Pattern Recognition) |
| Csyncpr_visualizer | Visualizer of output dynamic of syncpr network (Sync for Pattern Recognition) |
| ►Nsyncsegm | Double-layer oscillatory network with phase oscillator for image segmentation |
| Csyncsegm | Class represents segmentation algorithm syncsegm |
| Csyncsegm_analyser | Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results |
| Csyncsegm_visualizer | Result visualizer of double-layer oscillatory network 'syncsegm' |
| Cconn_represent | Enumerator of internal network connection representation between oscillators |
| Cconn_type | Enumerator of connection types between oscillators |
| Cinitial_type | Enumerator of types of oscillator output initialization |
| Cnetwork | Common network description that consists of information about oscillators and connection between them |
| Csolve_type | Enumerator of solver types that are used for network simulation |
| ▼Nsamples | Pyclustering module for samples |
| Canswer_reader | Answer reader for samples that are used by pyclustering library |
| ▼Nutils | Utils that are used by modules of pyclustering |
| ►Ncolor | Colors used by pyclustering library for visualization |
| Ccolor | Consists titles of colors that are used by pyclustering for visualization |
| ►Ngraph | Graph representation (uses format GRPR) |
| Cgraph | Graph representation |
| Ctype_graph_descr | Enumeration of graph description |
| ►Nmetric | Module provides various distance metrics - abstraction of the notion of distance in a metric space |
| Cdistance_metric | Distance metric performs distance calculation between two points in line with encapsulated function, for example, euclidean distance or chebyshev distance, or even user-defined |
| Ctype_metric | Enumeration of supported metrics in the module for distance calculation between two points |
1.8.13