AbstractModel Class Reference

abstract class (interface) for a model that can be used by a controller More...

#include <abstractmodel.h>

Inherits Configurable, and Storeable.

Inherited by FeedForwardNN, and InvertableModel.

Inheritance diagram for AbstractModel:

[legend]Collaboration diagram for AbstractModel:

[legend]List of all members.

Public Member Functions
	AbstractModel ()
virtual	~AbstractModel ()
virtual void	init (unsigned int inputDim, unsigned int outputDim, double unit_map=0.0)=0
	initialisation of the network with the given number of input and output units
virtual const matrix::Matrix	process (const matrix::Matrix &input)=0
	passive processing of the input (this function is not constant since a recurrent network for example might change internal states
virtual const matrix::Matrix	learn (const matrix::Matrix &input, const matrix::Matrix &nom_output, double learnRateFactor=1)=0
virtual unsigned int	getInputDim () const =0
	returns the number of input neurons
virtual unsigned int	getOutputDim () const =0
	returns the number of output neurons

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Detailed Description

abstract class (interface) for a model that can be used by a controller

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Constructor & Destructor Documentation

AbstractModel (   )  [inline]

virtual ~AbstractModel (   )  [inline, virtual]

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Member Function Documentation

virtual unsigned int getInputDim (   )  const [pure virtual]

returns the number of input neurons Implemented in MultiLayerFFNN, and OneLayerFFNN.

virtual unsigned int getOutputDim (   )  const [pure virtual]

returns the number of output neurons Implemented in MultiLayerFFNN, and OneLayerFFNN.

virtual void init (  unsigned int  inputDim, unsigned int  outputDim, double  unit_map = 0.0 )  [pure virtual]

initialisation of the network with the given number of input and output units Parameters: inputDim  length of input vector outputDim  length of output vector unit_map  if 0 the parametes are choosen randomly. Otherwise the model is initialised to represent a unit_map with the given response strength. Implemented in Elman, MultiLayerFFNN, and OneLayerFFNN.

virtual const matrix::Matrix learn (  const matrix::Matrix &  input, const matrix::Matrix &  nom_output, double  learnRateFactor = 1 )  [pure virtual]

Implemented in Elman, MultiLayerFFNN, and OneLayerFFNN.

virtual const matrix::Matrix process (  const matrix::Matrix &  input  )  [pure virtual]

passive processing of the input (this function is not constant since a recurrent network for example might change internal states Implemented in Elman, MultiLayerFFNN, and OneLayerFFNN.

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The documentation for this class was generated from the following file:

• abstractmodel.h

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