C# 클래스 Encog.Neural.Networks.BasicNetwork

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

파일 보기 프로젝트 열기: encog/encog-silverlight-core 1 사용 예제들

공개 메소드들

메소드	설명
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.

메소드 상세

AddLayer() 공개 메소드

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.
리턴	void

AddWeight() 공개 메소드

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.
리턴	void

BasicNetwork() 공개 메소드

Construct an empty neural network.

public BasicNetwork ( ) : System
리턴	System

CalculateError() 공개 메소드

Calculate the error for this neural network.

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

CalculateNeuronCount() 공개 메소드

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

public CalculateNeuronCount ( ) : int
리턴	int

Classify() 공개 메소드

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

ClearContext() 공개 메소드

Clear any data from any context layers.

public ClearContext ( ) : void
리턴	void

Clone() 공개 메소드

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

public Clone ( ) : Object
리턴	Object

Compute() 공개 메소드

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

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

Compute() 공개 메소드

Compute the output for this network.

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

DecodeFromArray() 공개 메소드

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

DumpWeights() 공개 메소드

public DumpWeights ( ) : String
리턴	String

EnableConnection() 공개 메소드

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.
리턴	void

EncodeToArray() 공개 메소드

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

EncodedArrayLength() 공개 메소드

public EncodedArrayLength ( ) : int
리턴	int

Equals() 공개 메소드

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.
리턴	bool

Equals() 공개 메소드

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.
리턴	bool

GetActivation() 공개 메소드

Get the activation function for the specified layer.

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

GetHashCode() 공개 최종 메소드

Generate a hash code.

public final GetHashCode ( ) : int
리턴	int

GetLayerBiasActivation() 공개 메소드

Get the bias activation for the specified layer.

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

GetLayerNeuronCount() 공개 메소드

Get the neuron count.

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

GetLayerOutput() 공개 메소드

Get the layer output for the specified neuron.

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

GetLayerTotalNeuronCount() 공개 메소드

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

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

GetWeight() 공개 메소드

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.
리턴	double

IsConnected() 공개 메소드

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.
리턴	bool

IsLayerBiased() 공개 메소드

Determine if the specified layer is biased.

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

Reset() 공개 메소드

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
리턴	void

Reset() 공개 메소드

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

SetLayerBiasActivation() 공개 메소드

Set the bias activation for the specified layer.

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

SetWeight() 공개 메소드

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.
리턴	void

ToString() 공개 최종 메소드

public final ToString ( ) : String
리턴	String

UpdateProperties() 공개 최종 메소드

public final UpdateProperties ( ) : void
리턴	void

ValidateNeuron() 공개 메소드

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.
리턴	void

Winner() 공개 메소드

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.
리턴	int