NetworkFactory Class Reference

#include <NetworkFactory.h>

List of all members.

Public Member Functions
	NetworkFactory (Network *nn)
	~NetworkFactory (void)
void	createLayer (float weight, int dirs, int scales, QString layerName, int layerType)
	Create network layer.
void	tmpltInputFeaturePlane (QString *fpName, QString *fileName, QString *taskFileName, float min_activation, float max_activation, bool scale, bool visible, bool isST)
	Construct input feature planes.
void	tmpltInputLearningFeaturePlane (QString *fpName, QString *fileName, QString *taskFileName, float min_activation, float max_activation, bool scale, bool visible, bool isST, int levels)
void	tmpltInputScales (QString *fpName, QString *inputBaseName, int w, int h, int scales, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct scaled input feature planes.
void	tmpltLGN (QString *fpName, QString *inputBaseName, int w, int h, int scales, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct LGN feature planes - i.e. center-surround at the given number of scales.
void	tmpltV1Edges (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V1 edge feature planes - i.e. edge detectors at the given number of scales and orientations.
void	tmpltV2EndStopped (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V2 end stopped feature planes - use filters 3x the size of the edge detectors, at 90 degrees.
void	tmpltV2Circle (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V2 cicles feature planes - use 4 filters 3x the size of the edge detectors.
void	tmpltV2Plus (QString *fpName, QString *inputBaseName, int w, int h, int scales, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V2 PLUS detector.
void	tmpltV2X (QString *fpName, QString *inputBaseName, int w, int h, int scales, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V2 X detector.
void	tmpltMultiInputFeaturePlane (QString *fpName, QString *fileName, QString *taskFileName, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct motion input feature plane.
void	tmpltV1Motion (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct V1 motion feature planes - i.e. edge detectors at the given number of scales and directions.
void	tmpltMT_T (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct MT motion translation feature planes.
void	tmpltMT_R (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct MT motion rotation feature planes.
void	tmpltMST_T (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct MST motion translation feature planes.
void	tmpltMST_R (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA)
	Construct MST motion rotation feature planes.
void	tmpltSTSOMLayer (QString *fpName, QString *inputBaseName, int w, int h, int scales, int angles, int neuronType, float nParam1, float nParam2, float wta_theta, float min_activation, float max_activation, bool scale, bool visible, bool isWTA, bool learning)
	Construct ST SOM FPs.

---

Detailed Description

Provides the infrastructure for a C++ network generator, to replace the XSLT

The intent is two fold

• to work towards a higher level network description

• to be able to have networks generated in a batch mode

Uses the Factory design pattern

---

Constructor & Destructor Documentation

NetworkFactory::NetworkFactory

(

Network *

nn

)

NetworkFactory::~NetworkFactory

(

void

)

---

Member Function Documentation

void NetworkFactory::createLayer	(	float	weight,
		int	dirs,
		int	scales,
		QString	layerName,
		int	layerType
	)

Create network layer.

void NetworkFactory::tmpltInputFeaturePlane	(	QString *	fpName,
		QString *	fileName,
		QString *	taskFileName,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isST
	)

Construct input feature planes.

void NetworkFactory::tmpltInputLearningFeaturePlane	(	QString *	fpName,
		QString *	fileName,
		QString *	taskFileName,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isST,
		int	levels
	)

void NetworkFactory::tmpltInputScales	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct scaled input feature planes.

void NetworkFactory::tmpltLGN	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct LGN feature planes - i.e. center-surround at the given number of scales.

void NetworkFactory::tmpltMST_R	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct MST motion rotation feature planes.

void NetworkFactory::tmpltMST_T	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct MST motion translation feature planes.

void NetworkFactory::tmpltMT_R	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct MT motion rotation feature planes.

void NetworkFactory::tmpltMT_T	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct MT motion translation feature planes.

void NetworkFactory::tmpltMultiInputFeaturePlane	(	QString *	fpName,
		QString *	fileName,
		QString *	taskFileName,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct motion input feature plane.

void NetworkFactory::tmpltSTSOMLayer	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA,
		bool	learning
	)

Construct ST SOM FPs.

void NetworkFactory::tmpltV1Edges	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V1 edge feature planes - i.e. edge detectors at the given number of scales and orientations.

void NetworkFactory::tmpltV1Motion	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V1 motion feature planes - i.e. edge detectors at the given number of scales and directions.

void NetworkFactory::tmpltV2Circle	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V2 cicles feature planes - use 4 filters 3x the size of the edge detectors.

void NetworkFactory::tmpltV2EndStopped	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	angles,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V2 end stopped feature planes - use filters 3x the size of the edge detectors, at 90 degrees.

void NetworkFactory::tmpltV2Plus	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V2 PLUS detector.

void NetworkFactory::tmpltV2X	(	QString *	fpName,
		QString *	inputBaseName,
		int	w,
		int	h,
		int	scales,
		int	neuronType,
		float	nParam1,
		float	nParam2,
		float	wta_theta,
		float	min_activation,
		float	max_activation,
		bool	scale,
		bool	visible,
		bool	isWTA
	)

Construct V2 X detector.

---

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

• C:/Users/albertlr/projects/TarzaNN/TarzaNN/NetworkFactory.h
• C:/Users/albertlr/projects/TarzaNN/TarzaNN/NetworkFactory.cpp