Ranker ranker = new Ranker(); InfoGainAttributeEval ig = new InfoGainAttributeEval(); Instances instances = SamplesManager.asWekaInstances(trainSet); ig.buildEvaluator(instances); firstAttributes = ranker.search(ig,instances); candidates = Arrays.copyOfRange(firstAttributes, 0, FIRST_SIZE_REDUCTION); instances = reduceDimenstions(instances, candidates) PrincipalComponents pca = new PrincipalComponents(); pca.setVarianceCovered(var); ranker = new Ranker(); ranker.setNumToSelect(numFeatures); selection = new AttributeSelection(); selection.setEvaluator(pca); selection.setSearch(ranker); selection.SelectAttributes(instances ); instances = selection.reduceDimensionality(wekaInstances);
/** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { runEvaluator(new InfoGainAttributeEval(), args); } }
/** Creates a default InfoGainAttributeEval */ public ASEvaluation getEvaluator() { return new InfoGainAttributeEval(); }
/** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { runEvaluator(new InfoGainAttributeEval(), args); } }
/** Creates a default InfoGainAttributeEval */ public ASEvaluation getEvaluator() { return new InfoGainAttributeEval(); }
/** Creates a default InfoGainAttributeEval */ public ASEvaluation getEvaluator() { return new InfoGainAttributeEval(); }
/** Creates a default InfoGainAttributeEval */ public ASEvaluation getEvaluator() { return new InfoGainAttributeEval(); }
InfoGainAttributeEval eval = new InfoGainAttributeEval(); Ranker search = new Ranker(); search.setThreshold(-1.7976931348623157E308);
InfoGainAttributeEval infoGainAttrEval = new InfoGainAttributeEval(); as.setEvaluator(infoGainAttrEval); as.setSearch(ranker);
Normalize norm = new Normalize(); norm.setInputFormat(train); train = Filter.useFilter(train, norm); RemoveUseless ru = new RemoveUseless(); ru.setInputFormat(train); train = Filter.useFilter(train, ru); Ranker rank = new Ranker(); InfoGainAttributeEval eval = new InfoGainAttributeEval(); eval.buildEvaluator(train);
if (result == JOptionPane.YES_OPTION) { m_AttributeEvaluatorEditor .setValue(new weka.attributeSelection.InfoGainAttributeEval()); } else {
int n = insts.numAttributes(); Attribute classAttribute = insts.attribute(insts.numAttributes() - 1); InfoGainAttributeEval ig = new InfoGainAttributeEval(); insts.setClass(classAttribute); ig.buildEvaluator(insts);
if (result == JOptionPane.YES_OPTION) { m_AttributeEvaluatorEditor .setValue(new weka.attributeSelection.InfoGainAttributeEval()); } else {