/**
* Predicts the class memberships for a given instance. If an instance is unclassified, the
* returned array elements must be all zero.
*
* @param inst the instance to be classified
* @return an array containing the estimated membership probabilities of the test instance in each
* class
*/
@Override
public double[] getVotesForInstance(Instance inst) {
double[] ret;
inst.setDataset(dataset);
if (this.isInit == false) {
ret = new double[dataset.numClasses()];
} else {
ret = learner.getVotesForInstance(inst);
}
return ret;
}
/**
* Trains this classifier incrementally using the given instance.
*
* @param inst the instance to be used for training
*/
@Override
public void trainOnInstance(Instance inst) {
if (this.isInit == false) {
this.isInit = true;
InstancesHeader instances = new InstancesHeader(dataset);
this.learner.setModelContext(instances);
this.learner.prepareForUse();
}
if (inst.weight() > 0) {
inst.setDataset(dataset);
learner.trainOnInstance(inst);
}
}
/**
* Instantiates a new learner.
*
* @param learner the learner
* @param dataset the dataset
*/
public SimpleClassifierAdapter(
com.yahoo.labs.samoa.moa.classifiers.Classifier learner, Instances dataset) {
this.learner = learner.copy();
this.isInit = false;
this.dataset = dataset;
}
/** Resets this classifier. It must be similar to starting a new classifier from scratch. */
@Override
public void resetLearning() {
learner.resetLearning();
}