Arrays.fill(zeroValues, 0.0d); SparseInstance wekaInstance = new SparseInstance(1.0d, zeroValues); wekaInstance.setDataset(instances);
SparseInstance wekaInstance = new SparseInstance(1.0d, zeroValues); wekaInstance.setDataset(instances);
inst.setDataset(race); copy.setDataset(inst.dataset()); System.out.println("Shallow copy with dataset set: " + copy); copy.setDataset(null); copy.deleteAttributeAt(0); copy.insertAttributeAt(0); copy.setDataset(inst.dataset()); System.out.println("Copy with first attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(1); copy.insertAttributeAt(1); copy.setDataset(inst.dataset()); System.out.println("Copy with second attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(2); copy.insertAttributeAt(2); copy.setDataset(inst.dataset()); System.out.println("Copy with third attribute deleted and inserted: " + copy);
copy.setDataset(inst.dataset()); System.out.println("Shallow copy with dataset set: " + copy); copy.setDataset(null); copy.deleteAttributeAt(0); copy.insertAttributeAt(0); copy.setDataset(inst.dataset()); System.out.println("Copy with first attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(1); copy.insertAttributeAt(1); copy.setDataset(inst.dataset()); System.out.println("Copy with second attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(2); copy.insertAttributeAt(2); copy.setDataset(inst.dataset()); System.out.println("Copy with third attribute deleted and inserted: " + copy);
inst.setDataset(race); copy.setDataset(inst.dataset()); System.out.println("Shallow copy with dataset set: " + copy); copy.setDataset(null); copy.deleteAttributeAt(0); copy.insertAttributeAt(0); copy.setDataset(inst.dataset()); System.out.println("Copy with first attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(1); copy.insertAttributeAt(1); copy.setDataset(inst.dataset()); System.out.println("Copy with second attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(2); copy.insertAttributeAt(2); copy.setDataset(inst.dataset()); System.out.println("Copy with third attribute deleted and inserted: " + copy);
copy.setDataset(inst.dataset()); System.out.println("Shallow copy with dataset set: " + copy); copy.setDataset(null); copy.deleteAttributeAt(0); copy.insertAttributeAt(0); copy.setDataset(inst.dataset()); System.out.println("Copy with first attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(1); copy.insertAttributeAt(1); copy.setDataset(inst.dataset()); System.out.println("Copy with second attribute deleted and inserted: " + copy); copy.setDataset(null); copy.deleteAttributeAt(2); copy.insertAttributeAt(2); copy.setDataset(inst.dataset()); System.out.println("Copy with third attribute deleted and inserted: " + copy);
sparseInstance.setDataset(wekaInstances); preprocessingFilter.input(sparseInstance); return preprocessingFilter.output();
private weka.core.Instance tcInstanceToMekaInstance(Instance instance, Instances trainingData, List<String> allClassLabels) throws Exception { AttributeStore attributeStore = new AttributeStore(); List<Attribute> outcomeAttributes = createOutcomeAttributes(allClassLabels); // in Meka, class label attributes have to go on top for (Attribute attribute : outcomeAttributes) { attributeStore.addAttributeAtBegin(attribute.name(), attribute); } for (int i = outcomeAttributes.size(); i < trainingData.numAttributes(); i++) { attributeStore.addAttribute(trainingData.attribute(i).name(), trainingData.attribute(i)); } double[] featureValues = getFeatureValues(attributeStore, instance); SparseInstance sparseInstance = new SparseInstance(1.0, featureValues); trainingData.setClassIndex(outcomeAttributes.size()); sparseInstance.setDataset(trainingData); return sparseInstance; }
private weka.core.Instance tcInstanceToMekaInstance(Instance instance, Instances trainingData, List<String> allClassLabels) throws Exception { AttributeStore attributeStore = new AttributeStore(); List<Attribute> outcomeAttributes = createOutcomeAttributes(allClassLabels); // in Meka, class label attributes have to go on top for (Attribute attribute : outcomeAttributes) { attributeStore.addAttributeAtBegin(attribute.name(), attribute); } for (int i = outcomeAttributes.size(); i < trainingData.numAttributes(); i++) { attributeStore.addAttribute(trainingData.attribute(i).name(), trainingData.attribute(i)); } double[] featureValues = getFeatureValues(attributeStore, instance); SparseInstance sparseInstance = new SparseInstance(1.0, featureValues); trainingData.setClassIndex(outcomeAttributes.size()); sparseInstance.setDataset(trainingData); return sparseInstance; }
sparseInstance.setDataset(wekaInstances); sparseInstance.setClassMissing(); preprocessingFilter.input(sparseInstance);
public weka.core.Instance tcInstanceToWekaInstance(Instance instance, Instances trainingData, List<String> allClasses, boolean isRegressionExperiment) throws Exception { AttributeStore attributeStore = new AttributeStore(); // outcome attribute is last and will be ignored for (int i = 0; i < trainingData.numAttributes() - 1; i++) { attributeStore.addAttribute(trainingData.attribute(i).name(), trainingData.attribute(i)); } // add outcome attribute Attribute outcomeAttribute = createOutcomeAttribute(allClasses, isRegressionExperiment); attributeStore.addAttribute(outcomeAttribute.name(), outcomeAttribute); double[] featureValues = getFeatureValues(attributeStore, instance); SparseInstance sparseInstance = new SparseInstance(1.0, featureValues); sparseInstance.setDataset(trainingData); return sparseInstance; }
public weka.core.Instance tcInstanceToWekaInstance(Instance instance, Instances trainingData, List<String> allClasses, boolean isRegressionExperiment) throws Exception { AttributeStore attributeStore = new AttributeStore(); // outcome attribute is last and will be ignored for (int i = 0; i < trainingData.numAttributes() - 1; i++) { attributeStore.addAttribute(trainingData.attribute(i).name(), trainingData.attribute(i)); } // add outcome attribute Attribute outcomeAttribute = createOutcomeAttribute(allClasses, isRegressionExperiment); attributeStore.addAttribute(outcomeAttribute.name(), outcomeAttribute); double[] featureValues = getFeatureValues(attributeStore, instance); SparseInstance sparseInstance = new SparseInstance(1.0, featureValues); sparseInstance.setDataset(trainingData); return sparseInstance; }