/** Creates a default RemovePercentage */ public Filter getFilter() { RemovePercentage f = new RemovePercentage(); return f; }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new RemovePercentage(), argv); } }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new RemovePercentage(), argv); } }
/** Creates a default RemovePercentage */ public Filter getFilter() { RemovePercentage f = new RemovePercentage(); return f; }
/** * 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}; }
RemovePercentage rmvp = new RemovePercentage(); rmvp.setInvertSelection(true); rmvp.setPercentage(Double.parseDouble(percentage)); Instances trainDataSet = Filter.useFilter(dataSet, rmvp); rmvp = new RemovePercentage(); 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); } }
RemovePercentage rmvp = new RemovePercentage(); rmvp.setInvertSelection(true); rmvp.setPercentage(samplingPercentage);
/** * 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}; }
RemovePercentage rp = new RemovePercentage(); rp.setPercentage(98); rp.setInputFormat(data);
RemovePercentage rp = new RemovePercentage(); rp.setPercentage( m_RemovedPercentage ); rp.setInputFormat( dataSubSet );
for (Instance datum : generated) { final Instances rel = datum.relationalValue(0); RemovePercentage rp = new RemovePercentage(); rp.setInputFormat(rel); rp.setPercentage(rand.nextDouble()*100);
RemovePercentage rp = new RemovePercentage(); rp.setPercentage(95); rp.setInputFormat(data);
RemovePercentage rp = new RemovePercentage(); rp.setInputFormat(data); rp.setPercentage(98);