content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    LearningRateResultProducer.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  
  package weka.experiment;
  
  import weka.core.AdditionalMeasureProducer;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset. Normally used with an AveragingResultProducer and CrossValidationResultProducer combo to generate learning curve results. For non-numeric result fields, the first value is used.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -X &lt;num steps&gt;
   *  The number of steps in the learning rate curve.
   *  (default 10)</pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of a ResultProducer.
   *  eg: weka.experiment.CrossValidationResultProducer</pre>
   * 
   * <pre> 
   * Options specific to result producer weka.experiment.AveragingResultProducer:
   * </pre>
   * 
   * <pre> -F &lt;field name&gt;
   *  The name of the field to average over.
   *  (default "Fold")</pre>
   * 
   * <pre> -X &lt;num results&gt;
   *  The number of results expected per average.
   *  (default 10)</pre>
   * 
   * <pre> -S
   *  Calculate standard deviations.
   *  (default only averages)</pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of a ResultProducer.
   *  eg: weka.experiment.CrossValidationResultProducer</pre>
   * 
   * <pre> 
   * Options specific to result producer weka.experiment.CrossValidationResultProducer:
   * </pre>
   * 
   * <pre> -X &lt;number of folds&gt;
   *  The number of folds to use for the cross-validation.
   *  (default 10)</pre>
   * 
   * <pre> -D
   * Save raw split evaluator output.</pre>
   * 
   * <pre> -O &lt;file/directory name/path&gt;
   *  The filename where raw output will be stored.
   *  If a directory name is specified then then individual
   *  outputs will be gzipped, otherwise all output will be
   *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of a SplitEvaluator.
   *  eg: weka.experiment.ClassifierSplitEvaluator</pre>
   * 
   * <pre> 
   * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
   * </pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of the classifier.
   *  eg: weka.classifiers.bayes.NaiveBayes</pre>
   * 
   * <pre> -C &lt;index&gt;
   *  The index of the class for which IR statistics
   *  are to be output. (default 1)</pre>
   * 
   * <pre> -I &lt;index&gt;
   *  The index of an attribute to output in the
   *  results. This attribute should identify an
   *  instance in order to know which instances are
   *  in the test set of a cross validation. if 0
   *  no output (default 0).</pre>
   * 
   * <pre> -P
   *  Add target and prediction columns to the result
   *  for each fold.</pre>
   * 
   * <pre> 
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   * </pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   <!-- options-end -->
   *
   * All options after -- will be passed to the result producer.
   * 
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 6426 $
   */
  public class LearningRateResultProducer 
    implements ResultListener, ResultProducer, OptionHandler,
  	     AdditionalMeasureProducer, RevisionHandler {
  
    /** for serialization */
    static final long serialVersionUID = -3841159673490861331L;
    
    /** The dataset of interest */
    protected Instances m_Instances;
  
    /** The ResultListener to send results to */
    protected ResultListener m_ResultListener = new CSVResultListener();
  
    /** The ResultProducer used to generate results */
    protected ResultProducer m_ResultProducer
      = new AveragingResultProducer();
  
    /** The names of any additional measures to look for in SplitEvaluators */
    protected String [] m_AdditionalMeasures = null;
  
    /** 
     * The minimum number of instances to use. If this is zero, the first
     * step will contain m_StepSize instances 
     */
    protected int m_LowerSize = 0;
    
    /**
     * The maximum number of instances to use. -1 indicates no maximum 
     * (other than the total number of instances)
     */
    protected int m_UpperSize = -1;
  
    /** The number of instances to add at each step */
    protected int m_StepSize = 10;
  
    /** The current dataset size during stepping */
    protected int m_CurrentSize = 0;
  
    /** The name of the key field containing the learning rate step number */
    public static String STEP_FIELD_NAME = "Total_instances";
  
    /**
     * Returns a string describing this result producer
     * @return a description of the result producer suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return "Tells a sub-ResultProducer to reproduce the current run for "
        +"varying sized subsamples of the dataset. Normally used with "
        +"an AveragingResultProducer and CrossValidationResultProducer "
        +"combo to generate learning curve results. For non-numeric "
        +"result fields, the first value is used.";
    }
  
  
    /**
     * Determines if there are any constraints (imposed by the
     * destination) on the result columns to be produced by
     * resultProducers. Null should be returned if there are NO
     * constraints, otherwise a list of column names should be
     * returned as an array of Strings.
     * @param rp the ResultProducer to which the constraints will apply
     * @return an array of column names to which resutltProducer's
     * results will be restricted.
     * @throws Exception if constraints can't be determined
     */
    public String [] determineColumnConstraints(ResultProducer rp) 
      throws Exception {
      return null;
    }
  
    /**
     * Gets the keys for a specified run number. Different run
     * numbers correspond to different randomizations of the data. Keys
     * produced should be sent to the current ResultListener
     *
     * @param run the run number to get keys for.
     * @throws Exception if a problem occurs while getting the keys
     */
    public void doRunKeys(int run) throws Exception {
  
      if (m_ResultProducer == null) {
        throw new Exception("No ResultProducer set");
      }
      if (m_ResultListener == null) {
        throw new Exception("No ResultListener set");
      }
      if (m_Instances == null) {
        throw new Exception("No Instances set");
      }
  
      // Tell the resultproducer to send results to us
      m_ResultProducer.setResultListener(this);
      m_ResultProducer.setInstances(m_Instances);
  
      // For each subsample size
      if (m_LowerSize == 0) {
        m_CurrentSize = m_StepSize;
      } else {
        m_CurrentSize = m_LowerSize;
      }
      while (m_CurrentSize <= m_Instances.numInstances() &&
             ((m_UpperSize == -1) ||
              (m_CurrentSize <= m_UpperSize))) {
        m_ResultProducer.doRunKeys(run);
        m_CurrentSize += m_StepSize;
      }
    }
  
  
    /**
     * Gets the results for a specified run number. Different run
     * numbers correspond to different randomizations of the data. Results
     * produced should be sent to the current ResultListener
     *
     * @param run the run number to get results for.
     * @throws Exception if a problem occurs while getting the results
     */
    public void doRun(int run) throws Exception {
  
      if (m_ResultProducer == null) {
        throw new Exception("No ResultProducer set");
      }
      if (m_ResultListener == null) {
        throw new Exception("No ResultListener set");
      }
      if (m_Instances == null) {
        throw new Exception("No Instances set");
      }
  
      // Randomize on a copy of the original dataset
      Instances runInstances = new Instances(m_Instances);
      runInstances.randomize(new Random(run));
      
      /*if (runInstances.classAttribute().isNominal() && (m_Instances.numInstances() / m_StepSize >= 1)) {
  //      runInstances.stratify(m_Instances.numInstances() / m_StepSize);
      }*/
  
      // Tell the resultproducer to send results to us
      m_ResultProducer.setResultListener(this);
  
      // For each subsample size
      if (m_LowerSize == 0) {
        m_CurrentSize = m_StepSize;
      } else {
        m_CurrentSize = m_LowerSize;
      }
      while (m_CurrentSize <= m_Instances.numInstances() &&
             ((m_UpperSize == -1) ||
              (m_CurrentSize <= m_UpperSize))) {
        m_ResultProducer.setInstances(new Instances(runInstances, 0, 
                                                    m_CurrentSize));
        m_ResultProducer.doRun(run);
        m_CurrentSize += m_StepSize;
      }
    }
  
    
    
    /**
     * Prepare for the results to be received.
     *
     * @param rp the ResultProducer that will generate the results
     * @throws Exception if an error occurs during preprocessing.
     */
    public void preProcess(ResultProducer rp) throws Exception {
  
      if (m_ResultListener == null) {
        throw new Exception("No ResultListener set");
      }
      m_ResultListener.preProcess(this);
    }
  
    /**
     * Prepare to generate results. The ResultProducer should call
     * preProcess(this) on the ResultListener it is to send results to.
     *
     * @throws Exception if an error occurs during preprocessing.
     */
    public void preProcess() throws Exception {
      
      if (m_ResultProducer == null) {
        throw new Exception("No ResultProducer set");
      }
      // Tell the resultproducer to send results to us
      m_ResultProducer.setResultListener(this);
      m_ResultProducer.preProcess();
    }
    
    /**
     * When this method is called, it indicates that no more results
     * will be sent that need to be grouped together in any way.
     *
     * @param rp the ResultProducer that generated the results
     * @throws Exception if an error occurs
     */
    public void postProcess(ResultProducer rp) throws Exception {
  
      m_ResultListener.postProcess(this);
    }
  
    /**
     * When this method is called, it indicates that no more requests to
     * generate results for the current experiment will be sent. The
     * ResultProducer should call preProcess(this) on the
     * ResultListener it is to send results to.
     *
     * @throws Exception if an error occurs
     */
    public void postProcess() throws Exception {
  
      m_ResultProducer.postProcess();
    }
    
    /**
     * Accepts results from a ResultProducer.
     *
     * @param rp the ResultProducer that generated the results
     * @param key an array of Objects (Strings or Doubles) that uniquely
     * identify a result for a given ResultProducer with given compatibilityState
     * @param result the results stored in an array. The objects stored in
     * the array may be Strings, Doubles, or null (for the missing value).
     * @throws Exception if the result could not be accepted.
     */
    public void acceptResult(ResultProducer rp, Object [] key, Object [] result)
      throws Exception {
  
      if (m_ResultProducer != rp) {
        throw new Error("Unrecognized ResultProducer sending results!!");
      }
      // Add in current step as key field
      Object [] newKey = new Object [key.length + 1];
      System.arraycopy(key, 0, newKey, 0, key.length);
      newKey[key.length] = new String("" + m_CurrentSize);
      // Pass on to result listener
      m_ResultListener.acceptResult(this, newKey, result);
    }
  
    /**
     * Determines whether the results for a specified key must be
     * generated.
     *
     * @param rp the ResultProducer wanting to generate the results
     * @param key an array of Objects (Strings or Doubles) that uniquely
     * identify a result for a given ResultProducer with given compatibilityState
     * @return true if the result should be generated
     * @throws Exception if it could not be determined if the result 
     * is needed.
     */
    public boolean isResultRequired(ResultProducer rp, Object [] key) 
      throws Exception {
  
      if (m_ResultProducer != rp) {
        throw new Error("Unrecognized ResultProducer sending results!!");
      }
      // Add in current step as key field
      Object [] newKey = new Object [key.length + 1];
      System.arraycopy(key, 0, newKey, 0, key.length);
      newKey[key.length] = new String("" + m_CurrentSize);
      // Pass on request to result listener
      return m_ResultListener.isResultRequired(this, newKey);
    }
  
    /**
     * Gets the names of each of the columns produced for a single run.
     *
     * @return an array containing the name of each column
     * @throws Exception if key names cannot be generated
     */
    public String [] getKeyNames() throws Exception {
  
      String [] keyNames = m_ResultProducer.getKeyNames();
      String [] newKeyNames = new String [keyNames.length + 1];
      System.arraycopy(keyNames, 0, newKeyNames, 0, keyNames.length);
      // Think of a better name for this key field
      newKeyNames[keyNames.length] = STEP_FIELD_NAME;
      return newKeyNames;
    }
  
    /**
     * Gets the data types of each of the columns produced for a single run.
     * This method should really be static.
     *
     * @return an array containing objects of the type of each column. The 
     * objects should be Strings, or Doubles.
     * @throws Exception if the key types could not be determined (perhaps
     * because of a problem from a nested sub-resultproducer)
     */
    public Object [] getKeyTypes() throws Exception {
  
      Object [] keyTypes = m_ResultProducer.getKeyTypes();
      Object [] newKeyTypes = new Object [keyTypes.length + 1];
      System.arraycopy(keyTypes, 0, newKeyTypes, 0, keyTypes.length);
      newKeyTypes[keyTypes.length] = "";
      return newKeyTypes;
    }
  
    /**
     * Gets the names of each of the columns produced for a single run.
     * A new result field is added for the number of results used to
     * produce each average.
     * If only averages are being produced the names are not altered, if
     * standard deviations are produced then "Dev_" and "Avg_" are prepended
     * to each result deviation and average field respectively.
     *
     * @return an array containing the name of each column
     * @throws Exception if the result names could not be determined (perhaps
     * because of a problem from a nested sub-resultproducer)
     */
    public String [] getResultNames() throws Exception {
  
      return m_ResultProducer.getResultNames();
    }
  
    /**
     * Gets the data types of each of the columns produced for a single run.
     *
     * @return an array containing objects of the type of each column. The 
     * objects should be Strings, or Doubles.
     * @throws Exception if the result types could not be determined (perhaps
     * because of a problem from a nested sub-resultproducer)
     */
    public Object [] getResultTypes() throws Exception {
  
      return m_ResultProducer.getResultTypes();
    }
  
    /**
     * Gets a description of the internal settings of the result
     * producer, sufficient for distinguishing a ResultProducer
     * instance from another with different settings (ignoring
     * those settings set through this interface). For example,
     * a cross-validation ResultProducer may have a setting for the
     * number of folds. For a given state, the results produced should
     * be compatible. Typically if a ResultProducer is an OptionHandler,
     * this string will represent the command line arguments required
     * to set the ResultProducer to that state.
     *
     * @return the description of the ResultProducer state, or null
     * if no state is defined
     */
    public String getCompatibilityState() {
  
      String result = " ";
      // + "-F " + Utils.quote(getKeyFieldName())
      // + " -X " + getStepSize() + " ";
      if (m_ResultProducer == null) {
        result += "<null ResultProducer>";
      } else {
        result += "-W " + m_ResultProducer.getClass().getName();
        result  += " -- " + m_ResultProducer.getCompatibilityState();
      }
      
      return result.trim();
    }
  
  
    /**
     * Returns an enumeration describing the available options..
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(2);
  
      newVector.addElement(new Option(
  	     "\tThe number of steps in the learning rate curve.\n"
  	      +"\t(default 10)", 
  	     "X", 1, 
  	     "-X <num steps>"));
      newVector.addElement(new Option(
  	     "\tThe full class name of a ResultProducer.\n"
  	      +"\teg: weka.experiment.CrossValidationResultProducer", 
  	     "W", 1, 
  	     "-W <class name>"));
  
      if ((m_ResultProducer != null) &&
  	(m_ResultProducer instanceof OptionHandler)) {
        newVector.addElement(new Option(
  	     "",
  	     "", 0, "\nOptions specific to result producer "
  	     + m_ResultProducer.getClass().getName() + ":"));
        Enumeration enu = ((OptionHandler)m_ResultProducer).listOptions();
        while (enu.hasMoreElements()) {
  	newVector.addElement(enu.nextElement());
        }
      }
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -X &lt;num steps&gt;
     *  The number of steps in the learning rate curve.
     *  (default 10)</pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of a ResultProducer.
     *  eg: weka.experiment.CrossValidationResultProducer</pre>
     * 
     * <pre> 
     * Options specific to result producer weka.experiment.AveragingResultProducer:
     * </pre>
     * 
     * <pre> -F &lt;field name&gt;
     *  The name of the field to average over.
     *  (default "Fold")</pre>
     * 
     * <pre> -X &lt;num results&gt;
     *  The number of results expected per average.
     *  (default 10)</pre>
     * 
     * <pre> -S
     *  Calculate standard deviations.
     *  (default only averages)</pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of a ResultProducer.
     *  eg: weka.experiment.CrossValidationResultProducer</pre>
     * 
     * <pre> 
     * Options specific to result producer weka.experiment.CrossValidationResultProducer:
     * </pre>
     * 
     * <pre> -X &lt;number of folds&gt;
     *  The number of folds to use for the cross-validation.
     *  (default 10)</pre>
     * 
     * <pre> -D
     * Save raw split evaluator output.</pre>
     * 
     * <pre> -O &lt;file/directory name/path&gt;
     *  The filename where raw output will be stored.
     *  If a directory name is specified then then individual
     *  outputs will be gzipped, otherwise all output will be
     *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of a SplitEvaluator.
     *  eg: weka.experiment.ClassifierSplitEvaluator</pre>
     * 
     * <pre> 
     * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
     * </pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of the classifier.
     *  eg: weka.classifiers.bayes.NaiveBayes</pre>
     * 
     * <pre> -C &lt;index&gt;
     *  The index of the class for which IR statistics
     *  are to be output. (default 1)</pre>
     * 
     * <pre> -I &lt;index&gt;
     *  The index of an attribute to output in the
     *  results. This attribute should identify an
     *  instance in order to know which instances are
     *  in the test set of a cross validation. if 0
     *  no output (default 0).</pre>
     * 
     * <pre> -P
     *  Add target and prediction columns to the result
     *  for each fold.</pre>
     * 
     * <pre> 
     * Options specific to classifier weka.classifiers.rules.ZeroR:
     * </pre>
     * 
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     * 
     <!-- options-end -->
     *
     * All options after -- will be passed to the result producer.
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {
      
      String stepSize = Utils.getOption('S', options);
      if (stepSize.length() != 0) {
        setStepSize(Integer.parseInt(stepSize));
      } else {
        setStepSize(10);
      }
  
      String lowerSize = Utils.getOption('L', options);
      if (lowerSize.length() != 0) {
        setLowerSize(Integer.parseInt(lowerSize));
      } else {
        setLowerSize(0);
      }
      
      String upperSize = Utils.getOption('U', options);
      if (upperSize.length() != 0) {
        setUpperSize(Integer.parseInt(upperSize));
      } else {
        setUpperSize(-1);
      }
  
      String rpName = Utils.getOption('W', options);
      if (rpName.length() == 0) {
        throw new Exception("A ResultProducer must be specified with"
  			  + " the -W option.");
      }
      // Do it first without options, so if an exception is thrown during
      // the option setting, listOptions will contain options for the actual
      // RP.
      setResultProducer((ResultProducer)Utils.forName(
  		      ResultProducer.class,
  		      rpName,
  		      null));
      if (getResultProducer() instanceof OptionHandler) {
        ((OptionHandler) getResultProducer())
  	.setOptions(Utils.partitionOptions(options));
      }
    }
  
    /**
     * Gets the current settings of the result producer.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] seOptions = new String [0];
      if ((m_ResultProducer != null) && 
  	(m_ResultProducer instanceof OptionHandler)) {
        seOptions = ((OptionHandler)m_ResultProducer).getOptions();
      }
      
      String [] options = new String [seOptions.length + 9];
      int current = 0;
  
      options[current++] = "-S";
      options[current++] = "" + getStepSize();
      options[current++] = "-L";
      options[current++] = "" + getLowerSize();
      options[current++] = "-U";
      options[current++] = "" + getUpperSize();
      if (getResultProducer() != null) {
        options[current++] = "-W";
        options[current++] = getResultProducer().getClass().getName();
      }
      options[current++] = "--";
  
      System.arraycopy(seOptions, 0, options, current, 
  		     seOptions.length);
      current += seOptions.length;
      while (current < options.length) {
        options[current++] = "";
      }
      return options;
    }
  
    /**
     * Set a list of method names for additional measures to look for
     * in SplitEvaluators. This could contain many measures (of which only a
     * subset may be produceable by the current resultProducer) if an experiment
     * is the type that iterates over a set of properties.
     * @param additionalMeasures an array of measure names, null if none
     */
    public void setAdditionalMeasures(String [] additionalMeasures) {
      m_AdditionalMeasures = additionalMeasures;
  
      if (m_ResultProducer != null) {
        System.err.println("LearningRateResultProducer: setting additional "
  			 +"measures for "
  			 +"ResultProducer");
        m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
      }
    }
  
    /**
     * Returns an enumeration of any additional measure names that might be
     * in the result producer
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
      Vector newVector = new Vector();
      if (m_ResultProducer instanceof AdditionalMeasureProducer) {
        Enumeration en = ((AdditionalMeasureProducer)m_ResultProducer).
  	enumerateMeasures();
        while (en.hasMoreElements()) {
  	String mname = (String)en.nextElement();
  	newVector.addElement(mname);
        }
      }
      return newVector.elements();
    }
  
    /**
     * Returns the value of the named measure
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
      if (m_ResultProducer instanceof AdditionalMeasureProducer) {
        return ((AdditionalMeasureProducer)m_ResultProducer).
  	getMeasure(additionalMeasureName);
      } else {
        throw new IllegalArgumentException("LearningRateResultProducer: "
  			  +"Can't return value for : "+additionalMeasureName
  			  +". "+m_ResultProducer.getClass().getName()+" "
  			  +"is not an AdditionalMeasureProducer");
      }
    }
  
    /**
     * Sets the dataset that results will be obtained for.
     *
     * @param instances a value of type 'Instances'.
     */
    public void setInstances(Instances instances) {
      
      m_Instances = instances;
    }
  
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String lowerSizeTipText() {
      return "Set the minmum number of instances in a dataset. Setting zero "
        + "here will actually use <stepSize> number of instances at the first "
        + "step (since it makes no sense to use zero instances :-))";
    }
  
    /**
     * Get the value of LowerSize.
     *
     * @return Value of LowerSize.
     */
    public int getLowerSize() {
      
      return m_LowerSize;
    }
    
    /**
     * Set the value of LowerSize.
     *
     * @param newLowerSize Value to assign to
     * LowerSize.
     */
    public void setLowerSize(int newLowerSize) {
      
      m_LowerSize = newLowerSize;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String upperSizeTipText() {
      return "Set the maximum number of instances in a dataset. Setting -1 "
        + "sets no upper limit (other than the total number of instances "
        + "in the full dataset)";
    }
  
    /**
     * Get the value of UpperSize.
     *
     * @return Value of UpperSize.
     */
    public int getUpperSize() {
      
      return m_UpperSize;
    }
    
    /**
     * Set the value of UpperSize.
     *
     * @param newUpperSize Value to assign to
     * UpperSize.
     */
    public void setUpperSize(int newUpperSize) {
      
      m_UpperSize = newUpperSize;
    }
  
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String stepSizeTipText() {
      return "Set the number of instances to add at each step.";
    }
  
    /**
     * Get the value of StepSize.
     *
     * @return Value of StepSize.
     */
    public int getStepSize() {
      
      return m_StepSize;
    }
    
    /**
     * Set the value of StepSize.
     *
     * @param newStepSize Value to assign to
     * StepSize.
     */
    public void setStepSize(int newStepSize) {
      
      m_StepSize = newStepSize;
    }
  
    /**
     * Sets the object to send results of each run to.
     *
     * @param listener a value of type 'ResultListener'
     */
    public void setResultListener(ResultListener listener) {
  
      m_ResultListener = listener;
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String resultProducerTipText() {
      return "Set the resultProducer for which learning rate results should be "
        + "generated.";
    }
  
    /**
     * Get the ResultProducer.
     *
     * @return the ResultProducer.
     */
    public ResultProducer getResultProducer() {
      
      return m_ResultProducer;
    }
    
    /**
     * Set the ResultProducer.
     *
     * @param newResultProducer new ResultProducer to use.
     */
    public void setResultProducer(ResultProducer newResultProducer) {
  
      m_ResultProducer = newResultProducer;
      m_ResultProducer.setResultListener(this);
    }
  
    /**
     * Gets a text descrption of the result producer.
     *
     * @return a text description of the result producer.
     */
    public String toString() {
  
      String result = "LearningRateResultProducer: ";
      result += getCompatibilityState();
      if (m_Instances == null) {
        result += ": <null Instances>";
      } else {
        result += ": " + Utils.backQuoteChars(m_Instances.relationName());
      }
      return result;
    }
  
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 6426 $");
    }
  } // LearningRateResultProducer
  