C# Class Encog.Neural.Networks.BasicNetwork

Inheritance: Encog.ML.BasicML, IMLMethod, IContainsFlat, IMLContext, IMLRegression, IMLInputOutput, IMLInput, IMLOutput, IMLEncodable, IMLResettable, IMLClassification, IMLError

Afficher le fichier Open project: encog/encog-silverlight-core Class Usage Examples

Méthodes publiques

Méthode	Description
AddLayer ( ILayer layer ) : void	Add a layer to the neural network. If there are no layers added this layer will become the input layer. This function automatically updates both the input and output layer references.
AddWeight ( int fromLayer, int fromNeuron, int toNeuron, double v ) : void	Add to a weight.
BasicNetwork ( ) : System	Construct an empty neural network.
CalculateError ( IMLDataSet data ) : double	Calculate the error for this neural network.
CalculateNeuronCount ( ) : int	Calculate the total number of neurons in the network across all layers.
Classify ( IMLData input ) : int
ClearContext ( ) : void	Clear any data from any context layers.
Clone ( ) : Object	Return a clone of this neural network. Including structure, weights and bias values. This is a deep copy.
Compute ( IMLData input ) : IMLData	Compute the output for a given input to the neural network.
Compute ( double input, double output ) : void	Compute the output for this network.
DecodeFromArray ( double encoded ) : void
DumpWeights ( ) : String
EnableConnection ( int fromLayer, int fromNeuron, int toNeuron, bool enable ) : void	Enable, or disable, a connection.
EncodeToArray ( double encoded ) : void
EncodedArrayLength ( ) : int
Equals ( BasicNetwork other ) : bool	Compare the two neural networks. For them to be equal they must be of the same structure, and have the same matrix values.
Equals ( BasicNetwork other, int precision ) : bool	Determine if this neural network is equal to another. Equal neural networks have the same weight matrix and bias values, within a specified precision.
GetActivation ( int layer ) : IActivationFunction	Get the activation function for the specified layer.
GetHashCode ( ) : int	Generate a hash code.
GetLayerBiasActivation ( int l ) : double	Get the bias activation for the specified layer.
GetLayerNeuronCount ( int l ) : int	Get the neuron count.
GetLayerOutput ( int layer, int neuronNumber ) : double	Get the layer output for the specified neuron.
GetLayerTotalNeuronCount ( int l ) : int	Get the total (including bias and context) neuron cont for a layer.
GetWeight ( int fromLayer, int fromNeuron, int toNeuron ) : double	Get the weight between the two layers.
IsConnected ( int layer, int fromNeuron, int toNeuron ) : bool	Determine if the specified connection is enabled.
IsLayerBiased ( int l ) : bool	Determine if the specified layer is biased.
Reset ( ) : void	Reset the weight matrix and the bias values. This will use a Nguyen-Widrow randomizer with a range between -1 and 1. If the network does not have an input, output or hidden layers, then Nguyen-Widrow cannot be used and a simple range randomize between -1 and 1 will be used.
Reset ( int seed ) : void	Reset the weight matrix and the bias values. This will use a Nguyen-Widrow randomizer with a range between -1 and 1. If the network does not have an input, output or hidden layers, then Nguyen-Widrow cannot be used and a simple range randomize between -1 and 1 will be used. Use the specified seed.
SetLayerBiasActivation ( int l, double v ) : void	Set the bias activation for the specified layer.
SetWeight ( int fromLayer, int fromNeuron, int toNeuron, double v ) : void	Set the weight between the two specified neurons.
ToString ( ) : String
UpdateProperties ( ) : void
ValidateNeuron ( int targetLayer, int neuron ) : void	Validate the the specified targetLayer and neuron are valid.
Winner ( IMLData input ) : int	Determine the winner for the specified input. This is the number of the winning neuron.

Method Details

AddLayer() public méthode

Add a layer to the neural network. If there are no layers added this layer will become the input layer. This function automatically updates both the input and output layer references.

public AddLayer ( ILayer layer ) : void
layer	ILayer	The layer to be added to the network.
Résultat	void

AddWeight() public méthode

Add to a weight.

public AddWeight ( int fromLayer, int fromNeuron, int toNeuron, double v ) : void
fromLayer	int	The from layer.
fromNeuron	int	The from neuron.
toNeuron	int	The to neuron.
v	double	The value to add.
Résultat	void

BasicNetwork() public méthode

Construct an empty neural network.

public BasicNetwork ( ) : System
Résultat	System

CalculateError() public méthode

Calculate the error for this neural network.

public CalculateError ( IMLDataSet data ) : double
data	IMLDataSet	The training set.
Résultat	double

CalculateNeuronCount() public méthode

Calculate the total number of neurons in the network across all layers.

public CalculateNeuronCount ( ) : int
Résultat	int

Classify() public méthode

public Classify ( IMLData input ) : int
input	IMLData
Résultat	int

ClearContext() public méthode

Clear any data from any context layers.

public ClearContext ( ) : void
Résultat	void

Clone() public méthode

Return a clone of this neural network. Including structure, weights and bias values. This is a deep copy.

public Clone ( ) : Object
Résultat	Object

Compute() public méthode

Compute the output for a given input to the neural network.

public Compute ( IMLData input ) : IMLData
input	IMLData	The input to the neural network.
Résultat	IMLData

Compute() public méthode

Compute the output for this network.

public Compute ( double input, double output ) : void
input	double	The input.
output	double	The output.
Résultat	void

DecodeFromArray() public méthode

public DecodeFromArray ( double encoded ) : void
encoded	double
Résultat	void

DumpWeights() public méthode

public DumpWeights ( ) : String
Résultat	String

EnableConnection() public méthode

Enable, or disable, a connection.

public EnableConnection ( int fromLayer, int fromNeuron, int toNeuron, bool enable ) : void
fromLayer	int	The layer that contains the from neuron.
fromNeuron	int	The source neuron.
toNeuron	int	The target connection.
enable	bool	True to enable, false to disable.
Résultat	void

EncodeToArray() public méthode

public EncodeToArray ( double encoded ) : void
encoded	double
Résultat	void

EncodedArrayLength() public méthode

public EncodedArrayLength ( ) : int
Résultat	int

Equals() public méthode

Compare the two neural networks. For them to be equal they must be of the same structure, and have the same matrix values.

public Equals ( BasicNetwork other ) : bool
other	BasicNetwork	The other neural network.
Résultat	bool

Equals() public méthode

Determine if this neural network is equal to another. Equal neural networks have the same weight matrix and bias values, within a specified precision.

public Equals ( BasicNetwork other, int precision ) : bool
other	BasicNetwork	The other neural network.
precision	int	The number of decimal places to compare to.
Résultat	bool

GetActivation() public méthode

Get the activation function for the specified layer.

public GetActivation ( int layer ) : IActivationFunction
layer	int	The layer.
Résultat	IActivationFunction

GetHashCode() public final méthode

Generate a hash code.

public final GetHashCode ( ) : int
Résultat	int

GetLayerBiasActivation() public méthode

Get the bias activation for the specified layer.

public GetLayerBiasActivation ( int l ) : double
l	int	The layer.
Résultat	double

GetLayerNeuronCount() public méthode

Get the neuron count.

public GetLayerNeuronCount ( int l ) : int
l	int	The layer.
Résultat	int

GetLayerOutput() public méthode

Get the layer output for the specified neuron.

public GetLayerOutput ( int layer, int neuronNumber ) : double
layer	int	The layer.
neuronNumber	int	The neuron number.
Résultat	double

GetLayerTotalNeuronCount() public méthode

Get the total (including bias and context) neuron cont for a layer.

public GetLayerTotalNeuronCount ( int l ) : int
l	int	The layer.
Résultat	int

GetWeight() public méthode

Get the weight between the two layers.

public GetWeight ( int fromLayer, int fromNeuron, int toNeuron ) : double
fromLayer	int	The from layer.
fromNeuron	int	The from neuron.
toNeuron	int	The to neuron.
Résultat	double

IsConnected() public méthode

Determine if the specified connection is enabled.

public IsConnected ( int layer, int fromNeuron, int toNeuron ) : bool
layer	int	The layer to check.
fromNeuron	int	The source neuron.
toNeuron	int	THe target neuron.
Résultat	bool

IsLayerBiased() public méthode

Determine if the specified layer is biased.

public IsLayerBiased ( int l ) : bool
l	int	The layer number.
Résultat	bool

Reset() public méthode

Reset the weight matrix and the bias values. This will use a Nguyen-Widrow randomizer with a range between -1 and 1. If the network does not have an input, output or hidden layers, then Nguyen-Widrow cannot be used and a simple range randomize between -1 and 1 will be used.

public Reset ( ) : void
Résultat	void

Reset() public méthode

public Reset ( int seed ) : void
seed	int
Résultat	void

SetLayerBiasActivation() public méthode

Set the bias activation for the specified layer.

public SetLayerBiasActivation ( int l, double v ) : void
l	int	The layer to use.
v	double
Résultat	void

SetWeight() public méthode

Set the weight between the two specified neurons.

public SetWeight ( int fromLayer, int fromNeuron, int toNeuron, double v ) : void
fromLayer	int	The from layer.
fromNeuron	int	The from neuron.
toNeuron	int	The to neuron.
v	double	The to value.
Résultat	void

ToString() public final méthode

public final ToString ( ) : String
Résultat	String

UpdateProperties() public final méthode

public final UpdateProperties ( ) : void
Résultat	void

ValidateNeuron() public méthode

Validate the the specified targetLayer and neuron are valid.

public ValidateNeuron ( int targetLayer, int neuron ) : void
targetLayer	int	The target layer.
neuron	int	The target neuron.
Résultat	void

Winner() public méthode

Determine the winner for the specified input. This is the number of the winning neuron.

public Winner ( IMLData input ) : int
input	IMLData	The input patter to present to the neural network.
Résultat	int