C# Class xpidea.neuro.net.son.SelfOrganizingNetwork

Implements the Self Organizing Network (SON).

The basic Self-Organizing Network can be visualized as a sheet-like neural-network array , the cells (or nodes) of which become specifically tuned to various input signal patterns or classes of patterns in an orderly fashion. The learning process is competitive and unsupervised, meaning that no teacher is needed to define the correct output (or actually the cell into which the input is mapped) for an input. In the basic version, only one map node (winner) at a time is activated corresponding to each input. The locations of the responses in the array tend to become ordered in the learning process as if some meaningful nonlinear coordinate system for the different input features were being created over the network (Kohonen, 1995c).The SOM was developed by Prof. Teuvo Kohonen in the early 1980s. The first application area of the SOM was speech recognition, or perhaps more accurately, speech-to-text transformation. (Timo Honkela)

Inheritance: xpidea.neuro.net.adaline.AdalineNetwork

Afficher le fichier Open project: AlexCherkasov/Neuro.NET

Protected Properties

Свойство	Type	Description
columsCount	int
currentIteration	long
currentNeighborhoodSize	int
finalLearningRate	double
initialLearningRate	double
initialNeighborhoodSize	int
kohonenLayer	].NeuroNode[
neighborhoodReduceInterval	int
rowsCount	int
trainingIterations	long
winnigCol	int
winnigRow	int

Méthodes publiques

Méthode	Description
Epoch ( int epoch ) : void	Overridden.Epoch - number of patterns that was exposed to a network during one training cycle.
Learn ( ) : void	Overridden.Teaches the network.
Load ( BinaryReader binaryReader ) : void	Overridden.Loads network data from the binary stream.
Run ( ) : void	Overridden.Runs the network.
Save ( BinaryWriter binaryWriter ) : void	Overridden.Stores network into to a binary stream.
SelfOrganizingNetwork ( ) : System	Constructs uninitialized instance of SON network. Used for persistance purposes.
SelfOrganizingNetwork ( int aInputNodesCount, int aRowCount, int aColCount, double aInitialLearningRate, double aFinalLearningRate, int aInitialNeighborhoodSize, int aNeighborhoodReduceInterval, long aTrainingIterationsCount ) : System	Constructs the network.
SelfOrganizingNetwork ( string fileName ) : System	Creates the network from a file.
Train ( PatternsCollection patterns ) : void	Overridden.Trains the network.

Méthodes protégées

Méthode	Description
CreateNetwork ( ) : void	Overridden.Constructs network topology.
GetInputNode ( int index ) : NeuroNode	Overridden.Retrieves the input node by its index.
GetInputNodesCount ( ) : int	Overridden.Returns number of nodes in input layer.
GetNetworkType ( ) : NeuralNetworkType	Overridden.Returns xpidea.neuro.net.NeuralNetworkType.nntSON for SON network.
GetNodeError ( ) : double	Overridden.Always returns 0. There is no output node.
GetOutPutNodesCount ( ) : int	Overridden.Number of nodes in output layer. Always return 0 since there are no nodes as its have an Kohonen layer.
GetOutputNode ( int index ) : NeuroNode	Overridden.Returns an output node by its index.
SetNodeError ( double value ) : void	Overridden.Doesn't do anything. There is no output node.

Method Details

CreateNetwork() protected méthode

Overridden.Constructs network topology.

protected CreateNetwork ( ) : void
Résultat	void

Epoch() public méthode

Overridden.Epoch - number of patterns that was exposed to a network during one training cycle.

public Epoch ( int epoch ) : void
epoch	int
Résultat	void

GetInputNode() protected méthode

Overridden.Retrieves the input node by its index.

protected GetInputNode ( int index ) : NeuroNode
index	int	Input node index.
Résultat	NeuroNode

GetInputNodesCount() protected méthode

Overridden.Returns number of nodes in input layer.

protected GetInputNodesCount ( ) : int
Résultat	int

GetNetworkType() protected méthode

Overridden.Returns xpidea.neuro.net.NeuralNetworkType.nntSON for SON network.

protected GetNetworkType ( ) : NeuralNetworkType
Résultat	NeuralNetworkType

GetNodeError() protected méthode

Overridden.Always returns 0. There is no output node.

protected GetNodeError ( ) : double
Résultat	double

GetOutPutNodesCount() protected méthode

Overridden.Number of nodes in output layer. Always return 0 since there are no nodes as its have an Kohonen layer.

protected GetOutPutNodesCount ( ) : int
Résultat	int

GetOutputNode() protected méthode

Overridden.Returns an output node by its index.

protected GetOutputNode ( int index ) : NeuroNode
index	int	Output node index.
Résultat	NeuroNode

Learn() public méthode

Overridden.Teaches the network.

public Learn ( ) : void
Résultat	void

Load() public méthode

Overridden.Loads network data from the binary stream.

public Load ( BinaryReader binaryReader ) : void
binaryReader	System.IO.BinaryReader	Binary stream reader.
Résultat	void

Run() public méthode

Overridden.Runs the network.

public Run ( ) : void
Résultat	void

Save() public méthode

Overridden.Stores network into to a binary stream.

public Save ( BinaryWriter binaryWriter ) : void
binaryWriter	System.IO.BinaryWriter	Binary stream writer.
Résultat	void

SelfOrganizingNetwork() public méthode

Constructs uninitialized instance of SON network. Used for persistance purposes.

public SelfOrganizingNetwork ( ) : System
Résultat	System

SelfOrganizingNetwork() public méthode

Constructs the network.

public SelfOrganizingNetwork ( int aInputNodesCount, int aRowCount, int aColCount, double aInitialLearningRate, double aFinalLearningRate, int aInitialNeighborhoodSize, int aNeighborhoodReduceInterval, long aTrainingIterationsCount ) : System
aInputNodesCount	int	Number of input nodes.
aRowCount	int	Number of rows in output layer.
aColCount	int	Number of colums in output layer.
aInitialLearningRate	double	Starting learning rate.
aFinalLearningRate	double	Ending learning rate.
aInitialNeighborhoodSize	int	Initial neighborhood size.
aNeighborhoodReduceInterval	int	Number of training iterations after neighborhood size will be reduced.
aTrainingIterationsCount	long	Number of training iterations.
Résultat	System

SelfOrganizingNetwork() public méthode

Creates the network from a file.

public SelfOrganizingNetwork ( string fileName ) : System
fileName	string
Résultat	System

SetNodeError() protected méthode

Overridden.Doesn't do anything. There is no output node.

protected SetNodeError ( double value ) : void
value	double	Error value.
Résultat	void

Train() public méthode

Overridden.Trains the network.

public Train ( PatternsCollection patterns ) : void
patterns	xpidea.neuro.net.patterns.PatternsCollection
Résultat	void

Property Details

columsCount protected_oe property

Number of colums in output layer.

protected int columsCount
Résultat	int

currentIteration protected_oe property

Current iteration.

protected long currentIteration
Résultat	long

currentNeighborhoodSize protected_oe property

Current neighborhood size.

protected int currentNeighborhoodSize
Résultat	int

finalLearningRate protected_oe property

Final learning rate.

protected double finalLearningRate
Résultat	double

initialLearningRate protected_oe property

Initial learning rate.

protected double initialLearningRate
Résultat	double

initialNeighborhoodSize protected_oe property

Initial neighborhood size.

protected int initialNeighborhoodSize
Résultat	int

kohonenLayer protected_oe property

Represents the Kohonen layer as two-dimetional array of xpidea.neuro.net.NeuroNode.

protected NeuroNode[,] kohonenLayer
Résultat	].NeuroNode[

neighborhoodReduceInterval protected_oe property

Neighborhood reduce interval.

protected int neighborhoodReduceInterval
Résultat	int

rowsCount protected_oe property

Number of rows in output layer.

protected int rowsCount
Résultat	int

trainingIterations protected_oe property

Number of training iterations.

protected long trainingIterations
Résultat	long

winnigCol protected_oe property

Winning column in output layer.

protected int winnigCol
Résultat	int

winnigRow protected_oe property

Winning row in output layer.

protected int winnigRow
Résultat	int