C# Class Encog.Neural.Flat.FlatNetwork

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Public Methods

Method	Description
CalculateError ( IMLDataSet data ) : double	Calculate the error for this neural network. The error is calculated using root-mean-square(RMS).
ClearConnectionLimit ( ) : void	Clear any connection limits.
ClearContext ( ) : void	Clear any context neurons.
Clone ( ) : Object	Clone the network.
CloneFlatNetwork ( FlatNetwork result ) : void	Clone into the flat network passed in.
Compute ( double input, double output ) : void	Calculate the output for the given input.
DecodeNetwork ( double data ) : void	Decode the specified data into the weights of the neural network. This method performs the opposite of encodeNetwork.
EncodeNetwork ( ) : double[]	Encode the neural network to an array of doubles. This includes the network weights. To read this into a neural network, use the decodeNetwork method.
FlatNetwork ( ) : System	Default constructor.
FlatNetwork ( FlatLayer layers ) : System	Create a flat network from an array of layers.
FlatNetwork ( int input, int hidden1, int hidden2, int output, bool tanh ) : System	Construct a flat neural network.
HasSameActivationFunction ( ) : Type	Neural networks with only one type of activation function offer certain optimization options. This method determines if only a single activation function is used.
Init ( FlatLayer layers ) : void	Construct a flat network.
Randomize ( ) : void	Perform a simple randomization of the weights of the neural network between -1 and 1.
Randomize ( double hi, double lo ) : void	Perform a simple randomization of the weights of the neural network between the specified hi and lo.

Protected Methods

Method	Description
ComputeLayer ( int currentLayer ) : void	Calculate a layer.

Method Details

CalculateError() public method

Calculate the error for this neural network. The error is calculated using root-mean-square(RMS).

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

ClearConnectionLimit() public method

Clear any connection limits.

public ClearConnectionLimit ( ) : void
return	void

ClearContext() public method

Clear any context neurons.

public ClearContext ( ) : void
return	void

Clone() public method

Clone the network.

public Clone ( ) : Object
return	Object

CloneFlatNetwork() public method

Clone into the flat network passed in.

public CloneFlatNetwork ( FlatNetwork result ) : void
result	FlatNetwork	The network to copy into.
return	void

Compute() public method

Calculate the output for the given input.

public Compute ( double input, double output ) : void
input	double	The input.
output	double	Output will be placed here.
return	void

ComputeLayer() protected method

Calculate a layer.

protected ComputeLayer ( int currentLayer ) : void
currentLayer	int	The layer to calculate.
return	void

DecodeNetwork() public method

Decode the specified data into the weights of the neural network. This method performs the opposite of encodeNetwork.

public DecodeNetwork ( double data ) : void
data	double	The data to be decoded.
return	void

EncodeNetwork() public method

Encode the neural network to an array of doubles. This includes the network weights. To read this into a neural network, use the decodeNetwork method.

public EncodeNetwork ( ) : double[]
return	double[]

FlatNetwork() public method

Default constructor.

public FlatNetwork ( ) : System
return	System

FlatNetwork() public method

Create a flat network from an array of layers.

public FlatNetwork ( FlatLayer layers ) : System
layers	FlatLayer	The layers.
return	System

FlatNetwork() public method

Construct a flat neural network.

public FlatNetwork ( int input, int hidden1, int hidden2, int output, bool tanh ) : System
input	int	Neurons in the input layer.
hidden1	int
hidden2	int
output	int	Neurons in the output layer.
tanh	bool	True if this is a tanh activation, false for sigmoid.
return	System

HasSameActivationFunction() public method

Neural networks with only one type of activation function offer certain optimization options. This method determines if only a single activation function is used.

public HasSameActivationFunction ( ) : Type
return	System.Type

Init() public method

Construct a flat network.

public Init ( FlatLayer layers ) : void
layers	FlatLayer	The layers of the network to create.
return	void

Randomize() public method

Perform a simple randomization of the weights of the neural network between -1 and 1.

public Randomize ( ) : void
return	void

Randomize() public method

Perform a simple randomization of the weights of the neural network between the specified hi and lo.

public Randomize ( double hi, double lo ) : void
hi	double	The network high.
lo	double	The network low.
return	void