code: package edu.berkeley.nlp.classify;
  
  import java.io.Serializable;
  
  import edu.berkeley.nlp.util.Indexer;
  
  /**
   * The Encoding maintains correspondences between the various representions of
   * the data, labels, and features. The external representations of labels and
   * features are object-based. The functions getLabelIndex() and
   * getFeatureIndex() can be used to translate those objects to integer
   * representatiosn: numbers between 0 and getNumLabels() or getNumFeatures()
   * (exclusive). The inverses of this map are the getLabel() and getFeature()
   * functions.
   */
  public class Encoding<F, L> implements Serializable {
  	/**
  	 * 
  	 */
  	private static final long serialVersionUID = 6349512704632759684L;
  	Indexer<F> featureIndexer;
  	Indexer<L> labelIndexer;
  
  	public int getNumFeatures() {
  		return featureIndexer.size();
  	}
  
  	public boolean hasFeature(F feature) {
  		return featureIndexer.contains(feature);
  	}
  
  	public int getFeatureIndex(F feature) {
  		return featureIndexer.indexOf(feature);
  	}
  
  	public F getFeature(int featureIndex) {
  		return featureIndexer.getObject(featureIndex);
  	}
  
  	public int getNumLabels() {
  		return labelIndexer.size();
  	}
  
  	public int getLabelIndex(L label) {
  		return labelIndexer.indexOf(label);
  	}
  
  	public L getLabel(int labelIndex) {
  		return labelIndexer.getObject(labelIndex);
  	}
  
  	public Encoding(Indexer<F> featureIndexer, Indexer<L> labelIndexer) {
  		this.featureIndexer = featureIndexer;
  		this.labelIndexer = labelIndexer;
  	}
  }