java.lang.Object
- adams.core.logging.LoggingObject
- - adams.core.logging.CustomLoggingLevelObject
  - - adams.core.option.AbstractOptionHandler
    - - adams.opt.genetic.AbstractGeneticAlgorithm
      - adams.opt.genetic.AbstractClassifierBasedGeneticAlgorithm

All Implemented Interfaces:

Destroyable, GlobalInfoSupporter, LoggingLevelHandler, LoggingSupporter, OptionHandler, Pausable, Randomizable, ShallowCopySupporter<AbstractGeneticAlgorithm>, SizeOfHandler, Stoppable, StoppableWithFeedback, ThreadLimiter, GeneticFitnessChangeNotifier, Serializable

Direct Known Subclasses:

AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation
```
public abstract class AbstractClassifierBasedGeneticAlgorithm
extends AbstractGeneticAlgorithm
implements GeneticFitnessChangeNotifier
```
Ancestor for genetic algorithms that evaluate classifiers.

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`static class`	`AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob<T extends AbstractClassifierBasedGeneticAlgorithm>`	Job class for algorithms with datasets.

Nested classes/interfaces inherited from class adams.opt.genetic.AbstractGeneticAlgorithm
AbstractGeneticAlgorithm.FitnessContainer, AbstractGeneticAlgorithm.GeneticAlgorithmJob<T extends AbstractGeneticAlgorithm>

Field Summary

Fields
Modifier and Type	Field	Description
`protected int`	`m_BitsPerGene`	the bits per gene to use.
`protected weka.classifiers.Classifier`	`m_Classifier`	the classifier to use.
`protected WekaAttributeIndex`	`m_ClassIndex`	the class index.
`protected WekaLabelIndex`	`m_ClassLabelIndex`	the label index.
`protected int`	`m_CrossValidationSeed`	the cross-validation seed.
`protected AbstractWekaEvaluationPostProcessor`	`m_EvaluationPostProcessor`	the postprocessor for the evaluation.
`protected Actor`	`m_FlowContext`	the flow context.
`protected int`	`m_Folds`	the number of folds for cross-validation.
`protected CrossValidationFoldGenerator`	`m_Generator`	the fold generator.
`protected weka.core.Instances`	`m_Instances`	the data to use for cross-validation.
`protected JobRunner<AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob>`	`m_JobRunner`	the job runner in use.
`protected JobRunnerSetup`	`m_JobRunnerSetup`	the jobrunner setup.
`protected Measure`	`m_Measure`	the measure to use for evaluating the fitness.
`protected PlaceholderDirectory`	`m_OutputDirectory`	the directory to store the generated ARFF files in.
`protected OutputPrefixType`	`m_OutputPrefixType`	the type of prefix to use for the output.
`protected OutputType`	`m_OutputType`	the type of output to generate.
`protected AbstractSetupUpload`	`m_SetupUpload`	for uploading the setups.
`Hashtable<String,Double>`	`m_StoredResults`	the cache for results.
`protected String`	`m_SuppliedPrefix`	the supplied prefix.
`protected weka.core.Instances`	`m_TestInstances`	the data to use for evaluation (if null, cross-validation is used).
`static String`	`PROPS_FILTER`	the key for a filter setup in the setup properties.
`static String`	`PROPS_MASK`	the key for the mask in the setup properties.
`static String`	`PROPS_RELATION`	the key for the relation name in the generated properties file.

Fields inherited from class adams.opt.genetic.AbstractGeneticAlgorithm
m_ActualNumChrom, m_BestFitness, m_BestRange, m_BestSetup, m_BestWeights, m_CurrentIteration, m_FavorZeroes, m_Fitness, m_FitnessChangeListeners, m_FitnessHistory, m_Genes, m_InitialSetupsProvider, m_InitialWeights, m_LastNotificationTime, m_MaxFitnessHistorySize, m_NotificationInterval, m_NumChrom, m_NumGenes, m_NumThreads, m_Paused, m_Random, m_Running, m_Seed, m_Stopped, m_StoppingCriterion

Fields inherited from class adams.core.option.AbstractOptionHandler
m_OptionManager

Fields inherited from class adams.core.logging.LoggingObject
m_Logger, m_LoggingIsEnabled, m_LoggingLevel

Constructor Summary

Constructors
Constructor Description

AbstractClassifierBasedGeneticAlgorithm()

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method	Description
`protected void`	`addResult(String key, Double val)`	Adds a result to the cache.
`String`	`bitsPerGeneTipText()`	Returns the tip text for this property.
`void`	`calcFitness()`	Calculates the fitness of the population.
`String`	`classifierTipText()`	Returns the tip text for this property.
`String`	`classIndexTipText()`	Returns the tip text for this property.
`String`	`classLabelIndexTipText()`	Returns the tip text for this property.
`protected void`	`clearResults()`	Clears all currently stored results.
`String`	`crossValidationSeedTipText()`	Returns the tip text for this property.
`void`	`defineOptions()`	Adds options to the internal list of options.
`String`	`evaluationPostProcessorTipText()`	Returns the tip text for this property.
`String`	`foldsTipText()`	Returns the tip text for this property.
`String`	`generatorTipText()`	Returns the tip text for this property.
`int`	`getBitsPerGene()`	Returns the currently set number of bits per gene.
`weka.classifiers.Classifier`	`getClassifier()`	Returns the currently set classifier.
`WekaAttributeIndex`	`getClassIndex()`	Returns the current class index.
`WekaLabelIndex`	`getClassLabelIndex()`	Returns the index of the class label to use for statistics that work on a per-label-basis.
`int`	`getCrossValidationSeed()`	Returns the current seed value for cross-validation.
`double`	`getCurrentFitness()`	Returns the currently best fitness.
`protected weka.classifiers.Classifier`	`getDefaultClassifier()`	Returns the default classifier to use.
`protected abstract OutputType`	`getDefaultOutputType()`	Returns the default output type to use.
`AbstractWekaEvaluationPostProcessor`	`getEvaluationPostProcessor()`	Returns the post-processing scheme for the evaluation.
`Actor`	`getFlowContext()`	Return the flow context, if any.
`int`	`getFolds()`	Returns the number of folds to use in cross-validation.
`CrossValidationFoldGenerator`	`getGenerator()`	Returns the scheme for generating the folds.
`weka.core.Instances`	`getInstances()`	Returns the currently set dataset for cross-validation.
`JobRunnerSetup`	`getJobRunnerSetup()`	Returns the jobrunner setup in use.
`Measure`	`getMeasure()`	Returns the current measure for evaluating the fitness.
`PlaceholderDirectory`	`getOutputDirectory()`	Returns the currently set directory for the generated ARFF files.
`OutputPrefixType`	`getOutputPrefixType()`	Returns the type of prefix to use for the output.
`OutputType`	`getOutputType()`	Returns the type of output to generate.
`protected Double`	`getResult(String key)`	Returns a value from the cache.
`AbstractSetupUpload`	`getSetupUpload()`	Returns the scheme for uploading the currently best job setup.
`String`	`getSuppliedPrefix()`	Returns the prefix to use in case of `OutputPrefixType.SUPPLIED`.
`weka.core.Instances`	`getTestInstances()`	Returns the currently set test set (if null, cross-validation is used).
`String`	`measureTipText()`	Returns the tip text for this property.
`protected abstract AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob`	`newJob(int chromosome, int[] w, weka.core.Instances data, weka.core.Instances testData)`	Creates a new Job instance.
`String`	`outputDirectoryTipText()`	Returns the tip text for this property.
`String`	`outputPrefixTypeTipText()`	Returns the tip text for this property.
`String`	`outputTypeTipText()`	Returns the tip text for this property.
`protected void`	`postRun(String error)`	Further clean-ups in derived classes.
`protected void`	`preRun()`	Some more initializations.
`void`	`setBitsPerGene(int value)`	Sets the bits per gene to use.
`void`	`setClassifier(weka.classifiers.Classifier value)`	Sets the classifier to use.
`void`	`setClassIndex(WekaAttributeIndex value)`	Sets the class index.
`void`	`setClassLabelIndex(WekaLabelIndex value)`	Sets the index of the class label to use for statistics that work on a per-label-basis.
`void`	`setCrossValidationSeed(int value)`	Sets the seed value to use for cross-validation.
`void`	`setEvaluationPostProcessor(AbstractWekaEvaluationPostProcessor value)`	Sets the post-processing scheme for the evaluation.
`void`	`setFlowContext(Actor value)`	Sets the flow context, if any.
`void`	`setFolds(int value)`	Sets the number of folds to use in cross-validation.
`void`	`setGenerator(CrossValidationFoldGenerator value)`	Sets the scheme for generating the folds.
`void`	`setInstances(weka.core.Instances value)`	Sets the data to use for cross-validation.
`void`	`setJobRunnerSetup(JobRunnerSetup value)`	Sets the jobrunner setup to use.
`void`	`setMeasure(Measure value)`	Sets the measure used for evaluating the fitness.
`void`	`setOutputDirectory(PlaceholderDirectory value)`	Sets the directory for the generated ARFF files.
`void`	`setOutputPrefixType(OutputPrefixType value)`	Sets the type of prefix to use for the output.
`void`	`setOutputType(OutputType value)`	Sets the type of output to generate.
`void`	`setSetupUpload(AbstractSetupUpload value)`	Sets the scheme for uploading the currently best job setup.
`void`	`setSuppliedPrefix(String value)`	Sets the prefix to use in case of `OutputPrefixType.SUPPLIED`.
`void`	`setTestInstances(weka.core.Instances value)`	Sets the currently set test set (if null, cross-validation is used).
`String`	`setupUploadTipText()`	Returns the tip text for this property.
`void`	`stopExecution()`	Stops the execution of the algorithm.
`protected Properties`	`storeSetup(weka.core.Instances data, AbstractGeneticAlgorithm.GeneticAlgorithmJob job)`	Generates a Properties file that stores information on the setup of the genetic algorithm.
`String`	`suppliedPrefixTipText()`	Returns the tip text for this property.
`String`	`toString()`	Returns a short string of the algorithm with the currently best fitness.
`weka.core.Instances`	`updateHeader(weka.core.Instances data, AbstractGeneticAlgorithm.GeneticAlgorithmJob job)`	Creates a new dataset, with the setup as the new relation name.

Methods inherited from class adams.opt.genetic.AbstractGeneticAlgorithm
addFitnessChangeListener, addToFitnessHistory, bestRangeTipText, copyGene, doCrossovers, doMutations, doMutations2, favorZeroesTipText, forCommandLine, forName, getBestRange, getCurrentIteration, getCurrentSetup, getCurrentWeights, getDefaultInitialSetupsProvider, getFavorZeroes, getFitness, getFitnessHistory, getGene, getGeneticAlgorithms, getInitialSetups, getInitialSetupsProvider, getInitialWeights, getMaxFitnessHistorySize, getNotificationInterval, getNumChrom, getNumGenes, getNumThreads, getSeed, getStoppingCriterion, hasMoreZeroes, init, initialize, initialSetupsProviderTipText, initialWeightsTipText, isBetterFitness, isPaused, isRunning, isStopped, logChromosomes, maxFitnessHistorySizeTipText, notificationIntervalTipText, notifyFitnessChangeListeners, notifyFitnessChangeListeners, numChromTipText, numThreadsTipText, pauseExecution, removeFitnessChangeListener, reset, resumeExecution, run, runGeneticAlgorithm, seedTipText, setBestRange, setBestRange, setFavorZeroes, setGene, setGene, setInitialSetupsProvider, setInitialWeights, setMaxFitnessHistorySize, setNewFitness, setNotificationInterval, setNumChrom, setNumThreads, setSeed, setStoppingCriterion, shallowCopy, shallowCopy, sort, stoppingCriterionTipText

Methods inherited from class adams.core.option.AbstractOptionHandler
cleanUpOptions, destroy, finishInit, getDefaultLoggingLevel, getOptionManager, globalInfo, loggingLevelTipText, newOptionManager, setLoggingLevel, toCommandLine

Methods inherited from class adams.core.logging.LoggingObject
configureLogger, getLogger, getLoggingLevel, initializeLogging, isLoggingEnabled, sizeOf

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface adams.event.GeneticFitnessChangeNotifier
addFitnessChangeListener, removeFitnessChangeListener

Methods inherited from interface adams.core.logging.LoggingLevelHandler
getLoggingLevel

- Field Detail
  - PROPS_RELATION
```
public static final String PROPS_RELATION
```
    the key for the relation name in the generated properties file.
    
    See Also:
    
    Constant Field Values
  - PROPS_FILTER
```
public static final String PROPS_FILTER
```
    the key for a filter setup in the setup properties.
    
    See Also:
    
    Constant Field Values
  - PROPS_MASK
```
public static final String PROPS_MASK
```
    the key for the mask in the setup properties.
    
    See Also:
    
    Constant Field Values
  - m_ClassIndex
```
protected WekaAttributeIndex m_ClassIndex
```
    the class index.
  - m_Instances
```
protected weka.core.Instances m_Instances
```
    the data to use for cross-validation.
  - m_TestInstances
```
protected weka.core.Instances m_TestInstances
```
    the data to use for evaluation (if null, cross-validation is used).
  - m_BitsPerGene
```
protected int m_BitsPerGene
```
    the bits per gene to use.
  - m_Classifier
```
protected weka.classifiers.Classifier m_Classifier
```
    the classifier to use.
  - m_Folds
```
protected int m_Folds
```
    the number of folds for cross-validation.
  - m_CrossValidationSeed
```
protected int m_CrossValidationSeed
```
    the cross-validation seed.
  - m_Generator
```
protected CrossValidationFoldGenerator m_Generator
```
    the fold generator.
  - m_Measure
```
protected Measure m_Measure
```
    the measure to use for evaluating the fitness.
  - m_ClassLabelIndex
```
protected WekaLabelIndex m_ClassLabelIndex
```
    the label index.
  - m_EvaluationPostProcessor
```
protected AbstractWekaEvaluationPostProcessor m_EvaluationPostProcessor
```
    the postprocessor for the evaluation.
  - m_OutputDirectory
```
protected PlaceholderDirectory m_OutputDirectory
```
    the directory to store the generated ARFF files in.
  - m_OutputType
```
protected OutputType m_OutputType
```
    the type of output to generate.
  - m_OutputPrefixType
```
protected OutputPrefixType m_OutputPrefixType
```
    the type of prefix to use for the output.
  - m_SuppliedPrefix
```
protected String m_SuppliedPrefix
```
    the supplied prefix.
  - m_SetupUpload
```
protected AbstractSetupUpload m_SetupUpload
```
    for uploading the setups.
  - m_StoredResults
```
public Hashtable<String,Double> m_StoredResults
```
    the cache for results.
  - m_JobRunnerSetup
```
protected transient JobRunnerSetup m_JobRunnerSetup
```
    the jobrunner setup.
  - m_FlowContext
```
protected Actor m_FlowContext
```
    the flow context.
  - m_JobRunner
```
protected JobRunner<AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob> m_JobRunner
```
    the job runner in use.
- Constructor Detail
  - AbstractClassifierBasedGeneticAlgorithm
```
public AbstractClassifierBasedGeneticAlgorithm()
```
- Method Detail
  - defineOptions
```
public void defineOptions()
```
    Adds options to the internal list of options.
    
    Specified by:
    
    defineOptions in interface OptionHandler
    
    Overrides:
    
    defineOptions in class AbstractGeneticAlgorithm
  - setClassIndex
```
public void setClassIndex(WekaAttributeIndex value)
```
    Sets the class index.
    
    Parameters:
    
    value - the class index
  - getClassIndex
```
public WekaAttributeIndex getClassIndex()
```
    Returns the current class index.
    
    Returns:
    
    the class index
  - classIndexTipText
```
public String classIndexTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setInstances
```
public void setInstances(weka.core.Instances value)
```
    Sets the data to use for cross-validation.
    
    Parameters:
    
    value - the dataset
  - getInstances
```
public weka.core.Instances getInstances()
```
    Returns the currently set dataset for cross-validation.
    
    Returns:
    
    the dataset
  - setTestInstances
```
public void setTestInstances(weka.core.Instances value)
```
    Sets the currently set test set (if null, cross-validation is used).
    
    Parameters:
    
    value - the dataset
  - getTestInstances
```
public weka.core.Instances getTestInstances()
```
    Returns the currently set test set (if null, cross-validation is used).
    
    Returns:
    
    the dataset
  - setFolds
```
public void setFolds(int value)
```
    Sets the number of folds to use in cross-validation.
    
    Parameters:
    
    value - the number of folds
  - getFolds
```
public int getFolds()
```
    Returns the number of folds to use in cross-validation.
    
    Returns:
    
    the number of folds
  - foldsTipText
```
public String foldsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setCrossValidationSeed
```
public void setCrossValidationSeed(int value)
```
    Sets the seed value to use for cross-validation.
    
    Parameters:
    
    value - the seed to use
  - getCrossValidationSeed
```
public int getCrossValidationSeed()
```
    Returns the current seed value for cross-validation.
    
    Returns:
    
    the seed value
  - crossValidationSeedTipText
```
public String crossValidationSeedTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setGenerator
```
public void setGenerator(CrossValidationFoldGenerator value)
```
    Sets the scheme for generating the folds.
    
    Parameters:
    
    value - the generator
  - getGenerator
```
public CrossValidationFoldGenerator getGenerator()
```
    Returns the scheme for generating the folds.
    
    Returns:
    
    the generator
  - generatorTipText
```
public String generatorTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - getDefaultClassifier
```
protected weka.classifiers.Classifier getDefaultClassifier()
```
    Returns the default classifier to use.
    
    Returns:
    
    the classifier
  - setClassifier
```
public void setClassifier(weka.classifiers.Classifier value)
```
    Sets the classifier to use.
    
    Parameters:
    
    value - the classifier
  - getClassifier
```
public weka.classifiers.Classifier getClassifier()
```
    Returns the currently set classifier.
    
    Returns:
    
    the classifier
  - classifierTipText
```
public String classifierTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setBitsPerGene
```
public void setBitsPerGene(int value)
```
    Sets the bits per gene to use.
    
    Parameters:
    
    value - the number of bits
  - getBitsPerGene
```
public int getBitsPerGene()
```
    Returns the currently set number of bits per gene.
    
    Returns:
    
    the number of bits
  - bitsPerGeneTipText
```
public String bitsPerGeneTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setMeasure
```
public void setMeasure(Measure value)
```
    Sets the measure used for evaluating the fitness.
    
    Parameters:
    
    value - the fitness measure
  - getMeasure
```
public Measure getMeasure()
```
    Returns the current measure for evaluating the fitness.
    
    Returns:
    
    the measure
  - measureTipText
```
public String measureTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setClassLabelIndex
```
public void setClassLabelIndex(WekaLabelIndex value)
```
    Sets the index of the class label to use for statistics that work on a per-label-basis.
    
    Parameters:
    
    value - the index
  - getClassLabelIndex
```
public WekaLabelIndex getClassLabelIndex()
```
    Returns the index of the class label to use for statistics that work on a per-label-basis.
    
    Returns:
    
    the index
  - classLabelIndexTipText
```
public String classLabelIndexTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setEvaluationPostProcessor
```
public void setEvaluationPostProcessor(AbstractWekaEvaluationPostProcessor value)
```
    Sets the post-processing scheme for the evaluation.
    
    Parameters:
    
    value - the post-processor
  - getEvaluationPostProcessor
```
public AbstractWekaEvaluationPostProcessor getEvaluationPostProcessor()
```
    Returns the post-processing scheme for the evaluation.
    
    Returns:
    
    the post-processor
  - evaluationPostProcessorTipText
```
public String evaluationPostProcessorTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setOutputDirectory
```
public void setOutputDirectory(PlaceholderDirectory value)
```
    Sets the directory for the generated ARFF files.
    
    Parameters:
    
    value - the directory
  - getOutputDirectory
```
public PlaceholderDirectory getOutputDirectory()
```
    Returns the currently set directory for the generated ARFF files.
    
    Returns:
    
    the directory
  - outputDirectoryTipText
```
public String outputDirectoryTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - getDefaultOutputType
```
protected abstract OutputType getDefaultOutputType()
```
    Returns the default output type to use.
    
    Returns:
    
    the type
  - setOutputType
```
public void setOutputType(OutputType value)
```
    Sets the type of output to generate.
    
    Parameters:
    
    value - the type
  - getOutputType
```
public OutputType getOutputType()
```
    Returns the type of output to generate.
    
    Returns:
    
    the type
  - outputTypeTipText
```
public String outputTypeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setOutputPrefixType
```
public void setOutputPrefixType(OutputPrefixType value)
```
    Sets the type of prefix to use for the output.
    
    Parameters:
    
    value - the type
  - getOutputPrefixType
```
public OutputPrefixType getOutputPrefixType()
```
    Returns the type of prefix to use for the output.
    
    Returns:
    
    the type
  - outputPrefixTypeTipText
```
public String outputPrefixTypeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setSuppliedPrefix
```
public void setSuppliedPrefix(String value)
```
    Sets the prefix to use in case of OutputPrefixType.SUPPLIED.
    
    Parameters:
    
    value - the prefix
  - getSuppliedPrefix
```
public String getSuppliedPrefix()
```
    Returns the prefix to use in case of OutputPrefixType.SUPPLIED.
    
    Returns:
    
    the number of folds
  - suppliedPrefixTipText
```
public String suppliedPrefixTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setSetupUpload
```
public void setSetupUpload(AbstractSetupUpload value)
```
    Sets the scheme for uploading the currently best job setup.
    
    Parameters:
    
    value - the upload scheme
  - getSetupUpload
```
public AbstractSetupUpload getSetupUpload()
```
    Returns the scheme for uploading the currently best job setup.
    
    Returns:
    
    the upload scheme
  - setupUploadTipText
```
public String setupUploadTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setJobRunnerSetup
```
public void setJobRunnerSetup(JobRunnerSetup value)
```
    Sets the jobrunner setup to use.
    
    Parameters:
    
    value - the setup, can be null to use default
  - getJobRunnerSetup
```
public JobRunnerSetup getJobRunnerSetup()
```
    Returns the jobrunner setup in use.
    
    Returns:
    
    the setup, null if using default
  - setFlowContext
```
public void setFlowContext(Actor value)
```
    Sets the flow context, if any.
    
    Parameters:
    
    value - the context
  - getFlowContext
```
public Actor getFlowContext()
```
    Return the flow context, if any.
    
    Returns:
    
    the context, null if none available
  - getCurrentFitness
```
public double getCurrentFitness()
```
    Returns the currently best fitness.
    
    Specified by:
    
    getCurrentFitness in interface GeneticFitnessChangeNotifier
    
    Overrides:
    
    getCurrentFitness in class AbstractGeneticAlgorithm
    
    Returns:
    
    the best fitness so far
  - storeSetup
```
protected Properties storeSetup(weka.core.Instances data,
                                AbstractGeneticAlgorithm.GeneticAlgorithmJob job)
```
    Generates a Properties file that stores information on the setup of the genetic algorithm. E.g., it backs up the original relation name. The generated properties file will be used as new relation name for the data. Derived classes can add additional parameters to this properties file.
    
    Parameters:
    
    data - the data to create the setup for
    
    job - the associated job
    
    Returns:
    
    the generated setup
    
    See Also:
    
    PROPS_RELATION
  - updateHeader
```
public weka.core.Instances updateHeader(weka.core.Instances data,
                                        AbstractGeneticAlgorithm.GeneticAlgorithmJob job)
```
    Creates a new dataset, with the setup as the new relation name.
    
    Parameters:
    
    data - the data to replace the relation name with the setup
    
    job - the associated job
    
    Returns:
    
    the updated dataset
  - addResult
```
protected void addResult(String key,
                         Double val)
```
    Adds a result to the cache.
    
    Parameters:
    
    key - the key of the result
    
    val - the value to add
  - getResult
```
protected Double getResult(String key)
```
    Returns a value from the cache.
    
    Parameters:
    
    key - the key of the result
    
    Returns:
    
    the result or null if not present
  - clearResults
```
protected void clearResults()
```
    Clears all currently stored results.
  - newJob
```
protected abstract AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob newJob(int chromosome,
                                                                                                     int[] w,
                                                                                                     weka.core.Instances data,
                                                                                                     weka.core.Instances testData)
```
    Creates a new Job instance.
    
    Parameters:
    
    chromosome - the chromosome index
    
    w - the initial weights
    
    data - the data to use
    
    testData - the test data to use, null for cross-validation
    
    Returns:
    
    the instance
  - calcFitness
```
public void calcFitness()
```
    Calculates the fitness of the population.
    
    Specified by:
    
    calcFitness in class AbstractGeneticAlgorithm
  - preRun
```
protected void preRun()
```
    Some more initializations.
    
    Overrides:
    
    preRun in class AbstractGeneticAlgorithm
  - postRun
```
protected void postRun(String error)
                throws Exception
```
    Further clean-ups in derived classes.
    
    Overrides:
    
    postRun in class AbstractGeneticAlgorithm
    
    Parameters:
    
    error - null if successful, otherwise error message
    
    Throws:
    
    Exception - if something goes wrong
  - stopExecution
```
public void stopExecution()
```
    Stops the execution of the algorithm.
    
    Specified by:
    
    stopExecution in interface Stoppable
    
    Overrides:
    
    stopExecution in class AbstractGeneticAlgorithm
  - toString
```
public String toString()
```
    Returns a short string of the algorithm with the currently best fitness.
    
    Overrides:
    
    toString in class AbstractOptionHandler
    
    Returns:
    
    a short info string

Class AbstractClassifierBasedGeneticAlgorithm

Nested Class Summary

Nested classes/interfaces inherited from class adams.opt.genetic.AbstractGeneticAlgorithm

Field Summary

Fields inherited from class adams.opt.genetic.AbstractGeneticAlgorithm

Fields inherited from class adams.core.option.AbstractOptionHandler

Fields inherited from class adams.core.logging.LoggingObject

Constructor Summary

Method Summary

Methods inherited from class adams.opt.genetic.AbstractGeneticAlgorithm

Methods inherited from class adams.core.option.AbstractOptionHandler

Methods inherited from class adams.core.logging.LoggingObject

Methods inherited from class java.lang.Object

Methods inherited from interface adams.event.GeneticFitnessChangeNotifier

Methods inherited from interface adams.core.logging.LoggingLevelHandler

Field Detail

PROPS_RELATION

PROPS_FILTER

PROPS_MASK

m_ClassIndex

m_Instances

m_TestInstances

m_BitsPerGene

m_Classifier

m_Folds

m_CrossValidationSeed

m_Generator

m_Measure

m_ClassLabelIndex

m_EvaluationPostProcessor

m_OutputDirectory

m_OutputType

m_OutputPrefixType

m_SuppliedPrefix

m_SetupUpload

m_StoredResults

m_JobRunnerSetup

m_FlowContext

m_JobRunner

Constructor Detail

AbstractClassifierBasedGeneticAlgorithm

Method Detail

defineOptions

setClassIndex

getClassIndex

classIndexTipText

setInstances

getInstances

setTestInstances

getTestInstances

setFolds

getFolds

foldsTipText

setCrossValidationSeed

getCrossValidationSeed

crossValidationSeedTipText

setGenerator

getGenerator

generatorTipText

getDefaultClassifier

setClassifier

getClassifier

classifierTipText

setBitsPerGene

getBitsPerGene

bitsPerGeneTipText

setMeasure

getMeasure

measureTipText

setClassLabelIndex

getClassLabelIndex

classLabelIndexTipText

setEvaluationPostProcessor

getEvaluationPostProcessor

evaluationPostProcessorTipText

setOutputDirectory

getOutputDirectory

outputDirectoryTipText

getDefaultOutputType

setOutputType