예제 #1
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 /**
  * 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;
 }
예제 #2
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 /**
  * 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);
   }
 }
예제 #3
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 /**
  * 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;
 }
예제 #4
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 /** Resets this classifier. It must be similar to starting a new classifier from scratch. */
 @Override
 public void resetLearning() {
   learner.resetLearning();
 }