C# Class Encog.Neural.Networks.BasicNetwork

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

Exibir arquivo Open project: encog/encog-silverlight-core Class Usage Examples

Public Methods

Method	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 method

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.
return	void

AddWeight() public method

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.
return	void

BasicNetwork() public method

Construct an empty neural network.

public BasicNetwork ( ) : System
return	System

CalculateError() public method

Calculate the error for this neural network.

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

CalculateNeuronCount() public method

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

public CalculateNeuronCount ( ) : int
return	int

Classify() public method

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

ClearContext() public method

Clear any data from any context layers.

public ClearContext ( ) : void
return	void

Clone() public method

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

public Clone ( ) : Object
return	Object

Compute() public method

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

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

Compute() public method

Compute the output for this network.

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

DecodeFromArray() public method

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

DumpWeights() public method

public DumpWeights ( ) : String
return	String

EnableConnection() public method

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.
return	void

EncodeToArray() public method

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

EncodedArrayLength() public method

public EncodedArrayLength ( ) : int
return	int

Equals() public method

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.
return	bool

Equals() public method

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.
return	bool

GetActivation() public method

Get the activation function for the specified layer.

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

GetHashCode() public final method

Generate a hash code.

public final GetHashCode ( ) : int
return	int

GetLayerBiasActivation() public method

Get the bias activation for the specified layer.

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

GetLayerNeuronCount() public method

Get the neuron count.

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

GetLayerOutput() public method

Get the layer output for the specified neuron.

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

GetLayerTotalNeuronCount() public method

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

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

GetWeight() public method

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.
return	double

IsConnected() public method

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.
return	bool

IsLayerBiased() public method

Determine if the specified layer is biased.

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

Reset() public method

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
return	void

Reset() public method

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

SetLayerBiasActivation() public method

Set the bias activation for the specified layer.

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

SetWeight() public method

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.
return	void

ToString() public final method

public final ToString ( ) : String
return	String

UpdateProperties() public final method

public final UpdateProperties ( ) : void
return	void

ValidateNeuron() public method

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.
return	void

Winner() public method

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.
return	int