geneticsearch.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

   * in its toString() method.
   * @param startSet a string containing a list of attributes (and or ranges),
   * eg. 1,2,6,10-15.
   * @exception Exception if start set can't be set.
   */
  public void setStartSet (String startSet) throws Exception {
    m_startRange.setRanges(startSet);
  }

  /**
   * Returns a list of attributes (and or attribute ranges) as a String
   * @return a list of attributes (and or attribute ranges)
   */
  public String getStartSet () {
    return m_startRange.getRanges();
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String seedTipText() {
    return "Set the random seed.";
  }

  /**
   * set the seed for random number generation
   * @param s seed value
   */
  public void setSeed(int s) {
    m_seed = s;
  }

  /**
   * get the value of the random number generator's seed
   * @return the seed for random number generation
   */
  public int getSeed() {
    return m_seed;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String reportFrequencyTipText() {
    return "Set how frequently reports are generated. Default is equal to "
      +"the number of generations meaning that a report will be printed for "
      +"initial and final generations. Setting the value to 5 will result in "
      +"a report being printed every 5 generations.";
  }

  /**
   * set how often reports are generated
   * @param f generate reports every f generations
   */
  public void setReportFrequency(int f) {
    m_reportFrequency = f;
  }

  /**
   * get how often repports are generated
   * @return how often reports are generated
   */
  public int getReportFrequency() {
    return m_reportFrequency;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String mutationProbTipText() {
    return "Set the probability of mutation occuring.";
  }

  /**
   * set the probability of mutation
   * @param m the probability for mutation occuring
   */
  public void setMutationProb(double m) {
    m_pMutation = m;
  }

  /**
   * get the probability of mutation
   * @return the probability of mutation occuring
   */
  public double getMutationProb() {
    return m_pMutation;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String crossoverProbTipText() {
    return "Set the probability of crossover. This is the probability that "
      +"two population members will exchange genetic material."; 
  }

  /**
   * set the probability of crossover
   * @param c the probability that two population members will exchange
   * genetic material
   */
  public void setCrossoverProb(double c) {
    m_pCrossover = c;
  }

  /**
   * get the probability of crossover
   * @return the probability of crossover
   */
  public double getCrossoverProb() {
    return m_pCrossover;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String maxGenerationsTipText() {
    return "Set the number of generations to evaluate.";
  }

  /**
   * set the number of generations to evaluate
   * @param m the number of generations
   */
  public void setMaxGenerations(int m) {
    m_maxGenerations = m;
  }

  /**
   * get the number of generations
   * @return the maximum number of generations
   */
  public int getMaxGenerations() {
    return m_maxGenerations;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String populationSizeTipText() {
    return "Set the population size. This is the number of individuals "
      +"(attribute sets) in the population.";
  }

  /**
   * set the population size
   * @param p the size of the population
   */
  public void setPopulationSize(int p) {
    m_popSize = p;
  }

  /**
   * get the size of the population
   * @return the population size
   */
  public int getPopulationSize() {
    return m_popSize;
  }

  /**
   * Returns a string describing this search method
   * @return a description of the search suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "GeneticSearch :\n\nPerforms a search using the simple genetic "
      +"algorithm described in Goldberg (1989).\n";
  }

  /**
   * Constructor. Make a new GeneticSearch object
   */
  public GeneticSearch() {
    resetOptions();
  }

  /**
   * converts the array of starting attributes to a string. This is
   * used by getOptions to return the actual attributes specified
   * as the starting set. This is better than using m_startRanges.getRanges()
   * as the same start set can be specified in different ways from the
   * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that
   * is stored in a database is comparable.
   * @return a comma seperated list of individual attribute numbers as a String
   */
  private String startSetToString() {
    StringBuffer FString = new StringBuffer();
    boolean didPrint;
    
    if (m_starting == null) {
      return getStartSet();
    }

    for (int i = 0; i < m_starting.length; i++) {
      didPrint = false;
      
      if ((m_hasClass == false) || 
	  (m_hasClass == true && i != m_classIndex)) {
	FString.append((m_starting[i] + 1));
	didPrint = true;
      }
      
      if (i == (m_starting.length - 1)) {
	FString.append("");
      }
      else {
	if (didPrint) {
	  FString.append(",");
	  }
      }
    }

    return FString.toString();
  }

  /**
   * returns a description of the search
   * @return a description of the search as a String
   */
  public String toString() {
    StringBuffer GAString = new StringBuffer();
    GAString.append("\tGenetic search.\n\tStart set: ");

    if (m_starting == null) {
      GAString.append("no attributes\n");
    }
    else {
      GAString.append(startSetToString()+"\n");
    }
    GAString.append("\tPopulation size: "+m_popSize);
    GAString.append("\n\tNumber of generations: "+m_maxGenerations);
    GAString.append("\n\tProbability of crossover: "
		+Utils.doubleToString(m_pCrossover,6,3));
    GAString.append("\n\tProbability of mutation: "
		+Utils.doubleToString(m_pMutation,6,3));
    GAString.append("\n\tReport frequency: "+m_reportFrequency);
    GAString.append("\n\tRandom number seed: "+m_seed+"\n");
    GAString.append(m_generationReports.toString());
    return GAString.toString();
  }

  /**
   * Searches the attribute subset space using a genetic algorithm.
   *
   * @param ASEvaluator the attribute evaluator to guide the search
   * @param data the training instances.
   * @return an array (not necessarily ordered) of selected attribute indexes
   * @exception Exception if the search can't be completed
   */
   public int[] search (ASEvaluation ASEval, Instances data)
    throws Exception {

     m_best = null;
     m_generationReports = new StringBuffer();

     if (!(ASEval instanceof SubsetEvaluator)) {
       throw  new Exception(ASEval.getClass().getName() 
			    + " is not a " 
			    + "Subset evaluator!");
     }
     
    if (ASEval instanceof UnsupervisedSubsetEvaluator) {
      m_hasClass = false;
    }
    else {
      m_hasClass = true;
      m_classIndex = data.classIndex();
    }

    SubsetEvaluator ASEvaluator = (SubsetEvaluator)ASEval;
    m_numAttribs = data.numAttributes();

    m_startRange.setUpper(m_numAttribs-1);
    if (!(getStartSet().equals(""))) {
      m_starting = m_startRange.getSelection();
    }

    // initial random population
    m_lookupTable = new Hashtable(m_lookupTableSize);
    m_random = new Random(m_seed);
    m_population = new GABitSet [m_popSize];

    // set up random initial population
    initPopulation();
    evaluatePopulation(ASEvaluator);
    populationStatistics();
    scalePopulation();
    checkBest();
    m_generationReports.append(populationReport(0));

    boolean converged;
    for (int i=1;i<=m_maxGenerations;i++) {
      generation();
      evaluatePopulation(ASEvaluator);
      populationStatistics();
      scalePopulation();
      // find the best pop member and check for convergence
      converged = checkBest();

      if ((i == m_maxGenerations) || 
	  ((i % m_reportFrequency) == 0) ||
	  (converged == true)) {
	m_generationReports.append(populationReport(i));
	if (converged == true) {
	  break;
	}
      }
    }
    return attributeList(m_best.getChromosome());
   }

  /**
   * converts a BitSet into a list of attribute indexes 
   * @param group the BitSet to convert
   * @return an array of attribute indexes
   **/
  private int[] attributeList (BitSet group) {
    int count = 0;

    // count how many were selected
    for (int i = 0; i < m_numAttribs; i++) {
      if (group.get(i)) {
	count++;
      }
    }

    int[] list = new int[count];
    count = 0;

    for (int i = 0; i < m_numAttribs; i++) {
      if (group.get(i)) {
	list[count++] = i;
      }
    }

    return  list;
  }

  /**
   * checks to see if any population members in the current
   * population are better than the best found so far. Also checks
   * to see if the search has converged---that is there is no difference
   * in fitness between the best and worse population member
   * @return true is the search has converged
   * @exception Exception if something goes wrong
   */
  private boolean checkBest() throws Exception {
    int i,j,count,lowestCount = m_numAttribs;
    double b = -Double.MAX_VALUE;
    GABitSet localbest = null;
    BitSet temp;
    boolean converged = false;
    int oldcount = Integer.MAX_VALUE;

    if (m_maxFitness - m_minFitness > 0) {
      // find the best in this population
      for (i=0;i<m_popSize;i++) {
	if (m_population[i].getObjective() > b) {
	  b = m_population[i].getObjective();
	  localbest = m_population[i];
	  oldcount = countFeatures(localbest.getChromosome());
	} else if (Utils.eq(m_population[i].getObjective(), b)) {
	  // see if it contains fewer features
	  count = countFeatures(m_population[i].getChromosome());
	  if (count < oldcount) {
	    b = m_population[i].getObjective();
	    localbest = m_population[i];
	    oldcount = count;
	  }
	}
      }
    } else {
      // look for the smallest subset
      for (i=0;i<m_popSize;i++) {
	temp = m_population[i].getChromosome();
	count = countFeatures(temp);;

	if (count < lowestCount) {
	  lowestCount = count;
	  localbest = m_population[i];