iCub::learningmachine::Normalizer Class Reference

A class that implements normalization as a preprocessing step. More...

#include <Normalizer.h>

Inheritance diagram for iCub::learningmachine::Normalizer:

Public Member Functions
	Normalizer (double l=-1, double u=1)
	Constructor.
virtual void	update (double val)
	Feeds a single sample into the scaler, so that it can use this sample to update the offset and scale.
virtual std::string	getInfo ()
	Asks the learning machine to return a string containing statistics on its operation so far.
virtual bool	configure (yarp::os::Searchable &config)
Normalizer *	clone ()
	Asks the scaler to return a new object of its type.
virtual double	getLowerBound ()
	Accessor for the desired lower bound.
virtual void	setLowerBound (double l)
	Mutator for the desired lower bound.
virtual double	getUpperBound ()
	Accessor for the desired upper bound.
virtual void	setUpperBound (double u)
	Mutator for the desired upper bound.
Public Member Functions inherited from iCub::learningmachine::IScaler
	IScaler (double s=1., double o=0.)
	Constructor.
virtual	~IScaler ()
	Destructor (empty).
virtual double	transform (double val)
	Transforms a single sample value according to the state of the scaler.
virtual double	unTransform (double val)
	Untransforms a single sample value according to the state of the scaler.
std::string	getName () const
	Retrieve the name of this scaler.
void	setName (std::string name)
	Set the name of this machine learning technique.
virtual void	setUpdateEnabled (bool u)
	Mutator for the update state.
virtual bool	getUpdateEnabled ()
	Accessor for the update state.
virtual std::string	toString ()
	Asks the scaler to return a string serialization.
virtual bool	fromString (const std::string &str)
	Asks the scaler to initialize from a string serialization.

Protected Member Functions
virtual void	writeBottle (yarp::os::Bottle &bot)
	Writes a serialization of the scaler into a bottle.
virtual void	readBottle (yarp::os::Bottle &bot)
	Unserializes a scaler from a bottle.

Protected Attributes
double	lowest
	The actual lowest value found in the sample values.
double	highest
	The actual highest value found in the sample values.
double	lowerBound
	The desired lower bound for the normalization range.
double	upperBound
	The desired upper bound for the normalization range.
Protected Attributes inherited from iCub::learningmachine::IScaler
double	offset
	The offset for the linear transformation.
double	scale
	The scale for the linear transformation.
std::string	name
	The name of this type of scaler.
bool	updateEnabled
	Boolean indicating whether the scaler has to update each sample.

Detailed Description

A class that implements normalization as a preprocessing step.

This normalizer limits all values between a predesignated range. By default this range is defined as [-1, 1].

See also

iCub::learningmachine::IScaler

Author

Arjan Gijsberts

Definition at line 40 of file Normalizer.h.

Constructor & Destructor Documentation

Normalizer()

iCub::learningmachine::Normalizer::Normalizer	(	double	l = -1,
		double	u = 1
	)

Constructor.

Parameters

l	the initial lower bound
u	the initial upper bound

Definition at line 32 of file Normalizer.cpp.

Member Function Documentation

clone()

Normalizer * iCub::learningmachine::Normalizer::clone ( )

inlinevirtual

Asks the scaler to return a new object of its type.

Returns

a fresh instance of the specified type

Implements iCub::learningmachine::IScaler.

Definition at line 99 of file Normalizer.h.

configure()

bool iCub::learningmachine::Normalizer::configure ( yarp::os::Searchable &  config )

virtual

Reimplemented from iCub::learningmachine::IScaler.

Definition at line 75 of file Normalizer.cpp.

getInfo()

std::string iCub::learningmachine::Normalizer::getInfo ( )

virtual

Asks the learning machine to return a string containing statistics on its operation so far.

Returns

the statistics of the machine

Reimplemented from iCub::learningmachine::IScaler.

Definition at line 53 of file Normalizer.cpp.

getLowerBound()

virtual double iCub::learningmachine::Normalizer::getLowerBound ( )

inlinevirtual

Accessor for the desired lower bound.

Definition at line 106 of file Normalizer.h.

getUpperBound()

virtual double iCub::learningmachine::Normalizer::getUpperBound ( )

inlinevirtual

Accessor for the desired upper bound.

Definition at line 118 of file Normalizer.h.

readBottle()

void iCub::learningmachine::Normalizer::readBottle ( yarp::os::Bottle &  bot )

protectedvirtual

Unserializes a scaler from a bottle.

Parameters

bot	the bottle

Reimplemented from iCub::learningmachine::IScaler.

Definition at line 68 of file Normalizer.cpp.

setLowerBound()

virtual void iCub::learningmachine::Normalizer::setLowerBound ( double  l )

inlinevirtual

Mutator for the desired lower bound.

Parameters

l	the new lower bound

Definition at line 113 of file Normalizer.h.

setUpperBound()

virtual void iCub::learningmachine::Normalizer::setUpperBound ( double  u )

inlinevirtual

Mutator for the desired upper bound.

Parameters

u	the new upper bound

Definition at line 125 of file Normalizer.h.

update()

void iCub::learningmachine::Normalizer::update ( double  val )

virtual

Feeds a single sample into the scaler, so that it can use this sample to update the offset and scale.

Parameters

value

the sample value

Reimplemented from iCub::learningmachine::IScaler.

Definition at line 37 of file Normalizer.cpp.

writeBottle()

void iCub::learningmachine::Normalizer::writeBottle ( yarp::os::Bottle &  bot )

protectedvirtual

Writes a serialization of the scaler into a bottle.

Parameters

bot	the bottle

Reimplemented from iCub::learningmachine::IScaler.

Definition at line 61 of file Normalizer.cpp.

Member Data Documentation

highest

double iCub::learningmachine::Normalizer::highest

protected

The actual highest value found in the sample values.

Definition at line 50 of file Normalizer.h.

lowerBound

double iCub::learningmachine::Normalizer::lowerBound

protected

The desired lower bound for the normalization range.

Definition at line 55 of file Normalizer.h.

lowest

double iCub::learningmachine::Normalizer::lowest

protected

The actual lowest value found in the sample values.

Definition at line 45 of file Normalizer.h.

upperBound

double iCub::learningmachine::Normalizer::upperBound

protected

The desired upper bound for the normalization range.

Definition at line 60 of file Normalizer.h.

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

• icub-main/src/libraries/learningMachine/include/iCub/learningMachine/Normalizer.h
• icub-main/src/libraries/learningMachine/src/Normalizer.cpp