nnfw Namespace Reference

the namespace that contains all More...

Classes
class	BackPropagationAlgo
	Back-Propagation Algorithm implementation. More...
class	BiasedCluster
	In this cluster a neuron have an input, an output and a bias value. More...
class	Clonable
	Clonable interface. More...
class	Cluster
	Define the common interface among Clusters. More...
class	CopyLinker
	CopyLinker Class. This linker copies the outputs of a cluster to inputs of another cluster. More...
class	DDECluster
	DDECluster Class. In this cluster the input/output relation is governed by a Discrete Differential Equation. More...
class	DerivableOutputFunction
	DerivableOutputFunction Class. More...
class	DotLinker
	DotLinker Class. More...
class	FakeCluster
	FakeCluster Class. The FakeCluster is a Cluster without neurons, it's like an array ! :-). More...
class	Pattern
	Pattern object. More...
class	PatternSet
	PatternSet object. More...
class	LearningAlgorithm
	LearningAlgorithm object. More...
class	WinnerTakeAllFunction
	WinnerTakeAllFunction. More...
class	IdentityFunction
	IdentityFunction. More...
class	ScaleFunction
	ScaleFunction. More...
class	GainFunction
	GainFunction. More...
class	SigmoidFunction
	Sigmoid Function. More...
class	FakeSigmoidFunction
	Fake Sigmoid Function !! Is a linear approximation of sigmoid function. More...
class	ScaledSigmoidFunction
	ScaledSigmoid Function. More...
class	RampFunction
	Ramp Function. More...
class	LinearFunction
	Linear equation Function. More...
class	StepFunction
	Step Function. More...
class	LeakyIntegratorFunction
	LeakyIntegrator Function !! More...
class	LogLikeFunction
	LogLike Function !! More...
class	PoolFunction
	Pool of Functions. More...
class	CompositeFunction
	Composite Function !! More...
class	LinearComboFunction
	Linear Combination of Two Function !! More...
class	PeriodicFunction
	PeriodicFunction. More...
class	SawtoothFunction
	SawtoothFunction. More...
class	TriangleFunction
	TriangleFunction. More...
class	SinFunction
	SinFunction. More...
class	PseudoGaussFunction
	PseudoGaussFunction. More...
class	GaussFunction
	GaussFunction. More...
class	Linker
	Abstract Linker Class. This define the common interface among Linkers. More...
class	MatrixData
	MatrixData Class. More...
class	MatrixLinker
	MatrixLinker Class define a full connection between two cluster. More...
class	nnfwString
class	nMessage
class	nWarning
class	nError
class	nFatal
class	BaseNeuralNet
	The Base Neural Network Class. More...
class	AbstractCreator
	Abstract Creator of Propertized objects. More...
class	Creator
	Template facility to create Creator specialization. More...
class	AbstractModifier
	Abstract Modifier for Updatable objects. More...
class	Factory
	Factory Class. More...
class	NormLinker
	NormLinker Class. More...
class	NotifyEvent
	Event Class. More...
class	Observer
	Observer Class. More...
class	Observable
	Observable Class. More...
class	OutputFunction
	OutputFunction Class. More...
class	Variant
	Incapsulate values of different types/classes in a unified way (like union). More...
class	AbstractPropertyAccess
	Encapsulates methods for accessing property data. More...
class	PropertyAccess
	Template creation of actual PropertyAccess. More...
class	VectorPropertyAccess
	Template creation of actual VectorPropertyAccess. More...
class	PropertyData
	Simple Structure for describing a property in PropertySettings. More...
class	Propertized
	Implements the capability to access internal data via properties. More...
class	Random
	Random class define some static method for accessing the random number generator. More...
class	RealMat
	RealMat Class. More...
class	RealVec
	RealVec Class. More...
class	SimpleCluster
	SimpleCluster Class. More...
class	SparseMatrixLinker
	SparseMatrixLinker Class extend MatrixLinker for allow non-full connection between Clusters. More...
class	Updatable
	Updatables objects. More...
class	SimpleTimer
	SimpleTimer object. More...
class	VectorData
	VectorData Class. More...
XML load/save
NNFW_API BaseNeuralNet *	loadXML (const char *filename)
NNFW_API bool	saveXML (const char *filename, BaseNeuralNet *net, int precision=-1, const char *skipList=0)
NNFW_API bool	saveXML (const char *filename, BaseNeuralNet *, const char *skipList)
Ouput Stream Operator
NNFW_API std::ostream &	operator<< (std::ostream &stream, const RealVec &v)
NNFW_API std::ostream &	operator<< (std::ostream &stream, const RealMat &m)
NNFW_API std::ostream &	operator<< (std::ostream &stream, const Variant::types t)
NNFW_API std::ostream &	operator<< (std::ostream &stream, const Variant var)
NNFW_API std::ostream &	operator<< (std::ostream &stream, const Propertized &p)
Typedefs
typedef unsigned int	u_int
typedef float	Real
typedef VectorData< AbstractPropertyAccess * >	PropertyAccessVec
typedef std::map< std::string, Variant >	PropertySettings
typedef VectorData< bool >	BoolVec
typedef VectorData< u_int >	U_IntVec
typedef VectorData< Updatable * >	UpdatableVec
typedef VectorData< Cluster * >	ClusterVec
typedef VectorData< Linker * >	LinkerVec
typedef VectorData< BaseTeachBlock * >	TeachBlockVec
Functions
template<class T>
void	memoryCopy (T *dest, const T *src, unsigned int size)
template<class T>
void	memoryZeroing (T *data, unsigned int size)
void	memoryCopy (float *dest, const float *src, unsigned int size)
void	memoryZeroing (float *data, unsigned int size)
void	memoryCopy (double *dest, const double *src, unsigned int size)
void	memoryZeroing (double *data, unsigned int size)
void	memoryCopy (bool *dest, const bool *src, unsigned int size)
void	memoryZeroing (bool *data, unsigned int size)
template<class T>
T	max (T a, T b)
template<class T>
T	min (T a, T b)
NNFW_API BaseNeuralNet *	feedForwardNet (U_IntVec layers, const char *clusterType="BiasedCluster", const char *linkerType="MatrixLinker")
Variables
NNFW_API const LinkerVec	emptyLinkerVec
NNFW_API const ClusterVec	emptyClusterVec

---

Typedef Documentation

typedef VectorData<bool> BoolVec

Array of Boolean

typedef VectorData<Cluster*> ClusterVec

Array of Clusters

typedef VectorData<Linker*> LinkerVec

Array of Linkers

typedef VectorData<AbstractPropertyAccess*> PropertyAccessVec

Vector of PropertyAccess

typedef std::map< std::string, Variant > PropertySettings

PropertySettings

typedef float Real

Abstraction on the type of real numbers

typedef VectorData<BaseTeachBlock*> TeachBlockVec

Array of Updatable

typedef unsigned int u_int

Unsigned integer

typedef VectorData<u_int> U_IntVec

Array of Unsigned Integer

typedef VectorData<Updatable*> UpdatableVec

Array of Updatable

---

Function Documentation

NNFW_API BaseNeuralNet* nnfw::feedForwardNet	(	U_IntVec	layers,
		const char *	clusterType = "BiasedCluster",
		const char *	linkerType = "MatrixLinker"
	)

Function to quickly construct a FeedForward Neural Network
Return a BaseNeuralNetwork that represent a feedforwar neural net.
The neural network returned is composed by Cluster of clusterType speficied. The number of neurons of each Cluster is taken from layers vector. Also, the first Cluster is setted as input of BaseNeuralNet and the last one as output.
The Linker created will link each Cluster to its successive.

Parameters:

	layers	an integer vector contains the number of neurons of each Cluster
	clusterType	the Typename of Cluster to create (it must be a Typename registered to Factory)
	linkerType	the Typename of Linker to create (it must be a Typename registered to Factory)

NNFW_API BaseNeuralNet* nnfw::loadXML

(

const char *

filename

)

Load the net from an XML file, and return a BaseNeuralNet

T nnfw::max	(	T	a,
		T	b
	)			[inline]

max function

void nnfw::memoryCopy	(	bool *	dest,
		const bool *	src,
		unsigned int	size
	)			[inline]

specialization of memoryCopy for bool data

void nnfw::memoryCopy	(	double *	dest,
		const double *	src,
		unsigned int	size
	)			[inline]

specialization of memoryCopy for double data

void nnfw::memoryCopy	(	float *	dest,
		const float *	src,
		unsigned int	size
	)			[inline]

specialization of memoryCopy for float data

void nnfw::memoryCopy	(	T *	dest,
		const T *	src,
		unsigned int	size
	)			[inline]

template for memory copy of data

void nnfw::memoryZeroing	(	bool *	data,
		unsigned int	size
	)			[inline]

specialization of memoryZeroing for bool data

void nnfw::memoryZeroing	(	double *	data,
		unsigned int	size
	)			[inline]

specialization of memoryZeroing for double data

void nnfw::memoryZeroing	(	float *	data,
		unsigned int	size
	)			[inline]

specialization of memoryZeroing for float data

void nnfw::memoryZeroing	(	T *	data,
		unsigned int	size
	)			[inline]

template for memory zeroing of data

T nnfw::min	(	T	a,
		T	b
	)			[inline]

min function

NNFW_API std::ostream& nnfw::operator<<	(	std::ostream &	stream,
		const Propertized &	p
	)

Operator << with Propertized

NNFW_API std::ostream& nnfw::operator<<	(	std::ostream &	stream,
		const Variant	var
	)

Operator << with Variant

NNFW_API std::ostream& nnfw::operator<<	(	std::ostream &	stream,
		const Variant::types	t
	)

Operator << with Variant::types

NNFW_API std::ostream& nnfw::operator<<	(	std::ostream &	stream,
		const RealMat &	m
	)

Operator << with RealMat

NNFW_API std::ostream& nnfw::operator<<	(	std::ostream &	stream,
		const RealVec &	v
	)

Operator << with RealVec

NNFW_API bool nnfw::saveXML	(	const char *	filename,
		BaseNeuralNet *	,
		const char *	skipList
	)

Save the BaseNeuralNet passed into an XML file; return true on success

NNFW_API bool nnfw::saveXML	(	const char *	filename,
		BaseNeuralNet *	net,
		int	precision = -1,
		const char *	skipList = 0
	)

Save the BaseNeuralNet passed into an XML file; return true on success

Parameters:

	filename	the file on which the net will be saved. All previous data will be overwritten
	net	the Neural Network to save
	precision	the number of significant decimal digits to save
	skipList	a list of properties separeted by spaces to skip during saving (It will not save those properties) (ex: "inputs outputs")

---

Variable Documentation

NNFW_API const ClusterVec emptyClusterVec

Empty ClusterVec constant

NNFW_API const LinkerVec emptyLinkerVec

Empty LinkerVec constant

Copyright © 2005-2008 G. Massera

Contact: emmegian (at) yahoo (dot) it

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