options.add("" + getSplitPercentage());
options.add("" + getSplitPercentage());
if (getSplitPercentage() != 0) { if ((getSplitPercentage() < 0) || (getSplitPercentage() > 100)) { throw new IllegalArgumentException("Split percentage in IterativeClassifierOptimizer not in [0,100]"); Instances[][] testSets = new Instances[numRuns][numFolds]; final IterativeClassifier[][] classifiers = new IterativeClassifier[numRuns][numFolds]; if (getSplitPercentage() == 0) { for (int j = 0; j < numRuns; j++) { data.randomize(randomInstance); data.randomize(randomInstance); int trainSize = (int) Math.round(data.numInstances() * getSplitPercentage() / 100); int testSize = data.numInstances() - trainSize; trainingSets[0][0] = new Instances(data, 0, trainSize);
if (getSplitPercentage() != 0) { if ((getSplitPercentage() < 0) || (getSplitPercentage() > 100)) { throw new IllegalArgumentException("Split percentage in IterativeClassifierOptimizer not in [0,100]"); Instances[][] testSets = new Instances[numRuns][numFolds]; final IterativeClassifier[][] classifiers = new IterativeClassifier[numRuns][numFolds]; if (getSplitPercentage() == 0) { for (int j = 0; j < numRuns; j++) { data.randomize(randomInstance); data.randomize(randomInstance); int trainSize = (int) Math.round(data.numInstances() * getSplitPercentage() / 100); int testSize = data.numInstances() - trainSize; trainingSets[0][0] = new Instances(data, 0, trainSize);