code: /**
   * Copyright (c) 2012-2013, Daniele Codecasa <codecasa.job@gmail.com>,
   * Models and Algorithms for Data & Text Mining (MAD) laboratory of
   * Milano-Bicocca University, and all the CTBNCToolkit contributors
   * that will follow.
   * All rights reserved.
   *
   * @author Daniele Codecasa and all the CTBNCToolkit contributors that will follow.
   * @copyright 2012-2013 Daniele Codecasa, MAD laboratory, and all the CTBNCToolkit contributors that will follow
   */
  package CTBNCToolkit;
  
  import java.util.*;
  
  /**
   * 
   * @author Daniele Codecasa <codecasa.job@gmail.com>
   *
   * Interface that define the transitions for
   * a classified trajectory.
   * 
   * @param <TimeType> type of the time interval (Integer = discrete time, Double = continuous time)
   */
  public interface IClassificationTransition<TimeType extends Number> extends ITransition<TimeType> {
  
  	/**
  	 * Set the probability distribution for each
  	 * state of the class node in the given transition
  	 * time.
  	 *
  	 * @param p the probability distribution
  	 * @param stateToIndex the mapping between the state name and the index for the probability
  	 * @throws IllegalArgumentException in case of illegal arguments
  	 */
  	public void setProbability(double[] p, Map<String,Integer> stateToIndex) throws IllegalArgumentException;
  	
  	/**
  	 * Return the classes probability distribution.
  	 * Null if the probability distribution is not
  	 * set.
  	 * 
  	 * @return probability distribution of the classes
  	 */
  	public double[] getPDistribution();
  	
  	/**
  	 * Return the probability value for the class
  	 * state in input in the current time.
  	 * Null if it is not set.
  	 * 
  	 * @param classStateName class state who get the probability
  	 * @return probability of the state
  	 * @throws RuntimeException in case of error
  	 */
  	public Double getProbability(String classStateName) throws RuntimeException;
  	
  }
  