setPercentage(50.0); setInvertSelection(Utils.getFlag('V', options));
setPercentage(50.0); setInvertSelection(Utils.getFlag('V', options));
rmvp.setInvertSelection(true); rmvp.setPercentage(Double.parseDouble(percentage)); rmvp.setInputFormat(dataSet);
@Override protected void buildInternal(MultiLabelInstances trainingSet) throws Exception { Instances dataSet = new Instances(trainingSet.getDataSet()); for (int i = 0; i < numOfModels; i++) { dataSet.randomize(rand); RemovePercentage rmvp = new RemovePercentage(); rmvp.setInputFormat(dataSet); rmvp.setPercentage(percentage); rmvp.setInvertSelection(true); Instances trainDataSet = Filter.useFilter(dataSet, rmvp); MultiLabelInstances train = new MultiLabelInstances(trainDataSet, trainingSet.getLabelsMetaData()); ensemble[i].build(train); } }
rmvp.setInvertSelection(true); rmvp.setPercentage(samplingPercentage); rmvp.setInputFormat(dataSet);
/** * Split the dataset into p% train an (100-p)% test set * * @param data Input data * @param p train percentage * @return Array of instances: (0) Train, (1) Test * @throws Exception Filterapplication went wrong */ public static Instances[] splitTrainVal(Instances data, double p) throws Exception { // Randomize data Randomize rand = new Randomize(); rand.setInputFormat(data); rand.setRandomSeed(42); data = Filter.useFilter(data, rand); // Remove testpercentage from data to get the train set RemovePercentage rp = new RemovePercentage(); rp.setInputFormat(data); rp.setPercentage(p); Instances train = Filter.useFilter(data, rp); // Remove trainpercentage from data to get the test set rp = new RemovePercentage(); rp.setInputFormat(data); rp.setPercentage(p); rp.setInvertSelection(true); Instances test = Filter.useFilter(data, rp); return new Instances[]{train, test}; }
public void testInverting() { // non-inverted m_Filter = getFilter(); ((RemovePercentage) m_Filter).setPercentage(20.0); Instances result = useFilter(); // inverted m_Filter = getFilter(); ((RemovePercentage) m_Filter).setPercentage(20.0); ((RemovePercentage) m_Filter).setInvertSelection(true); Instances resultInv = useFilter(); assertEquals( m_Instances.numInstances(), result.numInstances() + resultInv.numInstances()); }
public void testInverting() { // non-inverted m_Filter = getFilter(); ((RemovePercentage) m_Filter).setPercentage(20.0); Instances result = useFilter(); // inverted m_Filter = getFilter(); ((RemovePercentage) m_Filter).setPercentage(20.0); ((RemovePercentage) m_Filter).setInvertSelection(true); Instances resultInv = useFilter(); assertEquals( m_Instances.numInstances(), result.numInstances() + resultInv.numInstances()); }
/** * Split the dataset into p% train and (100-p)% testImdb set * * @param data Input data * @param p train percentage * @return Array of instances: (0) Train, (1) Test * @throws Exception Filterapplication went wrong */ public static Instances[] splitTrainTest(Instances data, double p) throws Exception { Randomize rand = new Randomize(); rand.setInputFormat(data); rand.setRandomSeed(42); data = Filter.useFilter(data, rand); RemovePercentage rp = new RemovePercentage(); rp.setInputFormat(data); rp.setPercentage(p); rp.setInvertSelection(true); Instances train = Filter.useFilter(data, rp); rp = new RemovePercentage(); rp.setInputFormat(data); rp.setPercentage(p); Instances test = Filter.useFilter(data, rp); return new Instances[] {train, test}; }