MultiLayerFFNN Class Reference

#include <multilayerffnn.h>

Inheritance diagram for MultiLayerFFNN:

[legend]Collaboration diagram for MultiLayerFFNN:

[legend]List of all members.

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Detailed Description

multi layer neural network with configurable activation functions

Definition at line 22 of file multilayerffnn.h.

Public Member Functions
	MultiLayerFFNN (double eps, const vector< Layer > &layers)
virtual	~MultiLayerFFNN ()
virtual void	init (unsigned int inputDim, unsigned int outputDim)
	initialisation of the network with the given number of input and output units
virtual const matrix::Matrix	process (const matrix::Matrix &input) const
	passive processing of the input
virtual const matrix::Matrix	learn (const matrix::Matrix &input, const matrix::Matrix &nom_output, double learnRateFactor=1)
	performs learning and returns the network output before learning
virtual unsigned int	getInputDim () const
	returns the number of input neurons
virtual unsigned int	getOutputDim () const
	returns the number of output neurons
virtual void	damp (double damping)
	damps the weights and the biases by multiplying (1-damping)
virtual paramkey	getName () const
	return the name of the object (with version number) Hint: { return "$ID$"; }
virtual paramval	getParam (const paramkey key) const
virtual bool	setParam (const paramkey key, paramval val)
virtual paramlist	getParamList () const
	The list of all parameters with there value as allocated lists.

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Constructor & Destructor Documentation

MultiLayerFFNN (  double  eps, const vector< Layer > &  layers )  [inline]

Parameters: eps  learning rate layers  Layer description (the input layer is not specified (always linear)) Definition at line 28 of file multilayerffnn.h.

virtual ~MultiLayerFFNN (   )  [inline, virtual]

Definition at line 34 of file multilayerffnn.h.

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Member Function Documentation

void damp (  double  damping  )  [virtual]

damps the weights and the biases by multiplying (1-damping) Implements FeedForwardNN. Definition at line 86 of file multilayerffnn.cpp.

virtual unsigned int getInputDim (   )  const [inline, virtual]

returns the number of input neurons Implements FeedForwardNN. Definition at line 48 of file multilayerffnn.h.

virtual paramkey getName (   )  const [inline, virtual]

return the name of the object (with version number) Hint: { return "$ID$"; } Reimplemented from Configurable. Definition at line 60 of file multilayerffnn.h.

virtual unsigned int getOutputDim (   )  const [inline, virtual]

returns the number of output neurons Implements FeedForwardNN. Definition at line 52 of file multilayerffnn.h.

virtual paramval getParam (  const paramkey  key  )  const [inline, virtual]

Definition at line 64 of file multilayerffnn.h.

virtual paramlist getParamList (   )  const [inline, virtual]

The list of all parameters with there value as allocated lists. Returns: list of key-value pairs Reimplemented from Configurable. Definition at line 76 of file multilayerffnn.h.

void init (  unsigned int  inputDim, unsigned int  outputDim )  [virtual]

initialisation of the network with the given number of input and output units Implements FeedForwardNN. Definition at line 9 of file multilayerffnn.cpp.

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

performs learning and returns the network output before learning Implements FeedForwardNN. Definition at line 42 of file multilayerffnn.cpp.

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

passive processing of the input Implements FeedForwardNN. Definition at line 29 of file multilayerffnn.cpp.

virtual bool setParam (  const paramkey  key, paramval  val )  [inline, virtual]

Definition at line 69 of file multilayerffnn.h.

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