net.sourceforge.cilib.problem
Class FunctionLearningProblem

java.lang.Object
  net.sourceforge.cilib.problem.OptimisationProblemAdapter
      net.sourceforge.cilib.problem.FunctionLearningProblem

All Implemented Interfaces:

Serializable, OptimisationProblem, Problem, Cloneable

public class FunctionLearningProblem
extends OptimisationProblemAdapter

See Also:

Serialized Form

Field Summary

Fields inherited from class net.sourceforge.cilib.problem.OptimisationProblemAdapter

dataSetBuilder, fitnessEvaluations

Constructor Summary

FunctionLearningProblem()

FunctionLearningProblem(Function function, int sampleSetSize, double trainingSetPercentage, NeuralNetworkProblem neuralNetwork)

FunctionLearningProblem(FunctionLearningProblem copy)

Method Summary

protected Fitness	calculateFitness(Type solution) This bases the fitness of the solution on the trainingSet samples.
DomainRegistry	getBehaviouralDomain() Returns the domain component that describes the search space of the needed behavioral characteristics of the problem.
FunctionLearningProblem	getClone() Create a cloned copy of the current object and return it.
DomainRegistry	getDomain() Returns the domain component that describes the search space for this problem.
double	getError(Object solution)
Function	getFunction()
Random	getRandom()
int	getSampleSetSize()
List<Double[]>	getTestingSet()
double	getTestingSetPercentage()
List<Double[]>	getTrainingSet()
double	getTrainingSetPercentage()
void	initialise()
void	setFunction(Function function)
void	setRandom(Random random)
void	setSampleSetSize(int sampleSetSize)
void	setTestingSet(List<Double[]> testingSet)
void	setTestingSetPercentage(double testingSetPercentage)
void	setTrainingSet(List<Double[]> trainingSet)
void	setTrainingSetPercentage(double trainingSetPercentage)

Methods inherited from class net.sourceforge.cilib.problem.OptimisationProblemAdapter

accept, changeEnvironment, getChangeStrategy, getDataSetBuilder, getFitness, getFitnessEvaluations, setChangeStrategy, setDataSetBuilder

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FunctionLearningProblem

public FunctionLearningProblem()

FunctionLearningProblem

public FunctionLearningProblem(Function function,
                               int sampleSetSize,
                               double trainingSetPercentage,
                               NeuralNetworkProblem neuralNetwork)

FunctionLearningProblem

public FunctionLearningProblem(FunctionLearningProblem copy)

Method Detail

getClone

public FunctionLearningProblem getClone()

Description copied from interface: OptimisationProblem

Create a cloned copy of the current object and return it. In general the created copy will be a deep copy of the provided instance. As a result this operation an be quite expensive if used incorrectly.

Specified by:

getClone in interface OptimisationProblem

Specified by:

getClone in interface Problem

Specified by:

getClone in interface Cloneable

Specified by:

getClone in class OptimisationProblemAdapter

Returns:

An exact clone of the current object instance.

See Also:

Object.clone()

initialise

public void initialise()

calculateFitness

protected Fitness calculateFitness(Type solution)

This bases the fitness of the solution on the trainingSet samples.

Specified by:

calculateFitness in class OptimisationProblemAdapter

Parameters:

solution -

Returns:

The average of all fitness values from the trainingSample set

See Also:

OptimisationProblemAdapter.getFitness(Type, boolean)

getError

public double getError(Object solution)

setFunction

public void setFunction(Function function)

getFunction

public Function getFunction()

setTestingSetPercentage

public void setTestingSetPercentage(double testingSetPercentage)

getTestingSetPercentage

public double getTestingSetPercentage()

setTrainingSetPercentage

public void setTrainingSetPercentage(double trainingSetPercentage)

getTrainingSetPercentage

public double getTrainingSetPercentage()

getTrainingSet

public List<Double[]> getTrainingSet()

setTrainingSet

public void setTrainingSet(List<Double[]> trainingSet)

getTestingSet

public List<Double[]> getTestingSet()

setTestingSet

public void setTestingSet(List<Double[]> testingSet)

getRandom

public Random getRandom()

setRandom

public void setRandom(Random random)

setSampleSetSize

public void setSampleSetSize(int sampleSetSize)

getSampleSetSize

public int getSampleSetSize()

getDomain

public DomainRegistry getDomain()

Description copied from interface: OptimisationProblem

Returns the domain component that describes the search space for this problem.

Returns:

A DomainRegistry object representing the search space.

getBehaviouralDomain

public DomainRegistry getBehaviouralDomain()

Description copied from interface: OptimisationProblem

Returns the domain component that describes the search space of the needed behavioral characteristics of the problem. This is mainly used for ECs, however, the use of behavioral parameters with PSO will be investigated.

Returns:

The domain of the behavioral component