Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 1234]

▼Npyclustering	PyClustering module that consists of general modules related to clustering, graph coloring, containers, neural networks, 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	Cluster analysis algorithm: BIRCH
Cbirch	Class represents clustering algorithm BIRCH
▶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
▶Nhsyncnet	Cluster analysis algorithm: Hierarchical Sync (HSyncNet)
Chsyncnet	Class represents clustering algorithm HSyncNet
▶Nkmeans	Cluster analysis algorithm: K-Means
Ckmeans	Class represents 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