@Override public void trainOnInstance(Instance inst) { boolean isTraining = (inst.weight() > 0.0); if (this instanceof SemiSupervisedLearner == false && inst.classIsMissing() == true){ isTraining = false; } if (isTraining) { this.trainingWeightSeenByModel += inst.weight(); trainOnInstanceImpl(inst); } }
/** * Stratify. * * @param numFolds the num folds */ public void stratify(int numFolds) { if (classAttribute().isNominal()) { // sort by class int index = 1; while (index < numInstances()) { Instance instance1 = instance(index - 1); for (int j = index; j < numInstances(); j++) { Instance instance2 = instance(j); if ((instance1.classValue() == instance2.classValue()) || (instance1.classIsMissing() && instance2.classIsMissing())) { swap(index, j); index++; } } index++; } stratStep(numFolds); } }
((Instance) testInst.getData()).classIsMissing() == true ? " ? " : trueClass));
int trueClass = (int) ((Instance) trainInst.getData()).classValue(); outputPredictionResultStream.println(Utils.maxIndex(prediction) + "," + ( ((Instance) testInst.getData()).classIsMissing() == true ? " ? " : trueClass));
int trueClass = (int) ((Instance) currentInst.getData()).classValue(); outputPredictionResultStream.println(Utils.maxIndex(prediction) + "," + ( ((Instance) testInst.getData()).classIsMissing() == true ? " ? " : trueClass));
m_weights = new double[instance.numAttributes() + 1]; if (!instance.classIsMissing()) {
if (instance.classIsMissing()) { return;
Instance inst = example.getData(); double weight = inst.weight(); if (inst.classIsMissing() == false) { int trueClass = (int) inst.classValue(); int predictedClass = Utils.maxIndex(classVotes);
public void trainOnInstanceImpl(Instance instance, int classLabel) { if (!instance.classIsMissing()) {
if (!instance.classIsMissing()) {