C# Class Encog.Neural.Networks.SVM.SVMNetwork

This is a network that is backed by one or more Support Vector Machines (SVM). It is designed to function very similarly to an Encog neural network, and is largely interchangeable with an Encog neural network. The support vector machine supports several types. Regression is used when you want the network to predict a value, given the input. Function approximation is a good example of regression. Classification is used when you want the SVM to group the input data into one or more classes. Support Vector Machines typically have a single output. Neural networks can have multiple output neurons. To get around this issue, this class will create multiple SVM's if there is more than one output specified. Because a SVM is trained quite differently from a neural network, none of the neural network training classes will work. This class must be trained using SVMTrain.

Inheritance: BasicNetwork

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Public Methods

Method	Description
Compute ( INeuralData input ) : INeuralData	Compute the output for the given input.
Compute ( INeuralData input, NeuralOutputHolder useHolder ) : INeuralData	Compute the output for the given input.
CreatePersistor ( ) : IPersistor	Create a persistor for this object.
MakeSparse ( INeuralData data ) : Encog.MathUtil.LIBSVM.svm_node[]	Convert regular Encog NeuralData into the "sparse" data needed by an SVM.
SVMNetwork ( int inputCount, int outputCount, SVMType svmType, KernelType kernelType ) : System	Construct a SVM network.
SVMNetwork ( int inputCount, int outputCount, bool regression ) : System	Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.

Method Details

Compute() public method

Compute the output for the given input.

public Compute ( INeuralData input ) : INeuralData
input	INeuralData	The input to the SVM.
return	INeuralData

Compute() public method

Compute the output for the given input.

public Compute ( INeuralData input, NeuralOutputHolder useHolder ) : INeuralData
input	INeuralData	The input to the SVM.
useHolder	NeuralOutputHolder	The output holder to use.
return	INeuralData

CreatePersistor() public method

Create a persistor for this object.

public CreatePersistor ( ) : IPersistor
return	IPersistor

MakeSparse() public method

Convert regular Encog NeuralData into the "sparse" data needed by an SVM.

public MakeSparse ( INeuralData data ) : Encog.MathUtil.LIBSVM.svm_node[]
data	INeuralData	The data to convert.
return	Encog.MathUtil.LIBSVM.svm_node[]

SVMNetwork() public method

Construct a SVM network.

public SVMNetwork ( int inputCount, int outputCount, SVMType svmType, KernelType kernelType ) : System
inputCount	int	The input count.
outputCount	int	The output count.
svmType	SVMType	The type of SVM.
kernelType	KernelType	The SVM kernal type.
return	System

SVMNetwork() public method

Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.

public SVMNetwork ( int inputCount, int outputCount, bool regression ) : System
inputCount	int	The input count.
outputCount	int	The output count.
regression	bool	True if this network is used for regression.
return	System