content: /*  UnderSampledClassifier.java
  
      Copyright (c) 2012-2014 Andrew Rosenberg
  
    This file is part of the AuToBI prosodic analysis package.
  
    AuToBI is free software: you can redistribute it and/or modify
    it under the terms of the Apache License (see boilerplate below)
  
   ***********************************************************************************************************************
   *
   * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
   * the License. You should have received a copy of the Apache 2.0 License along with AuToBI.
   * You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
   * an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
   * specific language governing permissions and limitations under the License.
   *
   ***********************************************************************************************************************
   */
  package edu.cuny.qc.speech.AuToBI.classifier;
  
  import edu.cuny.qc.speech.AuToBI.core.Distribution;
  import edu.cuny.qc.speech.AuToBI.core.FeatureSet;
  import edu.cuny.qc.speech.AuToBI.core.Word;
  import edu.cuny.qc.speech.AuToBI.util.PartitionUtils;
  
  /**
   * UnderSampledClassifier downsamples majority class tokens before training.
   */
  public class UnderSampledClassifier extends AuToBIClassifier {
    private AuToBIClassifier classifier;
    private String class_attribute;
  
    public UnderSampledClassifier(AuToBIClassifier classifier, String class_attribute) {
      this.classifier = classifier;
      this.class_attribute = class_attribute;
    }
  
    public Distribution distributionForInstance(Word testing_point) throws Exception {
      return classifier.distributionForInstance(testing_point);
    }
  
    public void train(FeatureSet feature_set) throws Exception {
  
      FeatureSet undersampled = feature_set.newInstance();
      undersampled.setDataPoints(PartitionUtils.performUnderSampling(feature_set.getDataPoints(), class_attribute));
      classifier.train(undersampled);
    }
  
    public AuToBIClassifier newInstance() {
      return new UnderSampledClassifier(classifier.newInstance(), class_attribute);
    }
  }
  