/*
 *    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.
 */

/*
 *    Experiment.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */


package weka.experiment;

import weka.core.AdditionalMeasureProducer;
import weka.core.FastVector;
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 weka.core.converters.AbstractFileLoader;
import weka.core.converters.ConverterUtils;
import weka.core.xml.KOML;
import weka.core.xml.XMLOptions;
import weka.experiment.xml.XMLExperiment;

import java.beans.PropertyDescriptor;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import java.lang.reflect.Array;
import java.lang.reflect.Method;
import java.util.Enumeration;
import java.util.Vector;

import javax.swing.DefaultListModel;

/**
 * Holds all the necessary configuration information for a standard
 * type experiment. This object is able to be serialized for storage
 * on disk.
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -L &lt;num&gt;
 *  The lower run number to start the experiment from.
 *  (default 1)</pre>
 * 
 * <pre> -U &lt;num&gt;
 *  The upper run number to end the experiment at (inclusive).
 *  (default 10)</pre>
 * 
 * <pre> -T &lt;arff file&gt;
 *  The dataset to run the experiment on.
 *  (required, may be specified multiple times)</pre>
 * 
 * <pre> -P &lt;class name&gt;
 *  The full class name of a ResultProducer (required).
 *  eg: weka.experiment.RandomSplitResultProducer</pre>
 * 
 * <pre> -D &lt;class name&gt;
 *  The full class name of a ResultListener (required).
 *  eg: weka.experiment.CSVResultListener</pre>
 * 
 * <pre> -N &lt;string&gt;
 *  A string containing any notes about the experiment.
 *  (default none)</pre>
 * 
 * <pre> 
 * Options specific to result producer weka.experiment.RandomSplitResultProducer:
 * </pre>
 * 
 * <pre> -P &lt;percent&gt;
 *  The percentage of instances to use for training.
 *  (default 66)</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> -R
 *  Set when data is not to be randomized and the data sets' size.
 *  Is not to be determined via probabilistic rounding.</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. <p>
 *
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 5399 $
 */
public class Experiment 
  implements Serializable, OptionHandler, RevisionHandler {
  
  /** for serialization */
  static final long serialVersionUID = 44945596742646663L;
  
  /** The filename extension that should be used for experiment files */
  public static String FILE_EXTENSION = ".exp";

  /** Where results will be sent */
  protected ResultListener m_ResultListener = new InstancesResultListener();
  
  /** The result producer */
  protected ResultProducer m_ResultProducer = new RandomSplitResultProducer();

  /** Lower run number */
  protected int m_RunLower = 1;

  /** Upper run number */
  protected int m_RunUpper = 10;

  /** An array of dataset files */
  protected DefaultListModel m_Datasets = new DefaultListModel();

  /** True if the exp should also iterate over a property of the RP */
  protected boolean m_UsePropertyIterator = false;
  
  /** The path to the iterator property */
  protected PropertyNode [] m_PropertyPath;
  
  /** The array of values to set the property to */
  protected Object m_PropertyArray;

  /** User notes about the experiment */
  protected String m_Notes = "";

  /** Method names of additional measures of objects contained in the 
      custom property iterator. Only methods names beginning with "measure"
      and returning doubles are recognised */
  protected String [] m_AdditionalMeasures = null;

  /** True if the class attribute is the first attribute for all
      datasets involved in this experiment. */
  protected boolean m_ClassFirst = false;

  /** If true an experiment will advance the current data set befor
      any custom itererator */
  protected boolean m_AdvanceDataSetFirst = true;

  /**
   * Sets whether the first attribute is treated as the class
   * for all datasets involved in the experiment. This information
   * is not output with the result of the experiments!
   * 
   * @param flag	whether the class attribute is the first and not the last
   */
  public void classFirst(boolean flag) {
    
    m_ClassFirst = flag;
  }
  
  /**
   * Get the value of m_DataSetFirstFirst.
   *
   * @return Value of m_DataSetFirstFirst.
   */
  public boolean getAdvanceDataSetFirst() {
    
    return m_AdvanceDataSetFirst;
  }
  
  /**
   * Set the value of m_AdvanceDataSetFirst.
   *
   * @param newAdvanceDataSetFirst Value to assign to m_AdvanceRunFirst.
   */
  public void setAdvanceDataSetFirst(boolean newAdvanceDataSetFirst) {
    
    m_AdvanceDataSetFirst = newAdvanceDataSetFirst;
  }
  
  /**
   * Gets whether the custom property iterator should be used.
   *
   * @return true if so
   */
  public boolean getUsePropertyIterator() {
    
    return m_UsePropertyIterator;
  }

  /**
   * Sets whether the custom property iterator should be used.
   *
   * @param newUsePropertyIterator true if so
   */
  public void setUsePropertyIterator(boolean newUsePropertyIterator) {
    
    m_UsePropertyIterator = newUsePropertyIterator;
  }

  /**
   * Gets the path of properties taken to get to the custom property
   * to iterate over.
   *
   * @return an array of PropertyNodes
   */
  public PropertyNode [] getPropertyPath() {
    
    return m_PropertyPath;
  }
  
  /**
   * Sets the path of properties taken to get to the custom property
   * to iterate over.
   *
   * @param newPropertyPath an array of PropertyNodes
   */
  public void setPropertyPath(PropertyNode [] newPropertyPath) {
    
    m_PropertyPath = newPropertyPath;
  }
  
  /**
   * Sets the array of values to set the custom property to.
   *
   * @param newPropArray a value of type Object which should be an
   * array of the appropriate values.
   */
  public void setPropertyArray(Object newPropArray) {

    m_PropertyArray = newPropArray;
  }

  /**
   * Gets the array of values to set the custom property to.
   *
   * @return a value of type Object which should be an
   * array of the appropriate values.
   */
  public Object getPropertyArray() {

    return m_PropertyArray;
  }

  /**
   * Gets the number of custom iterator values that have been defined
   * for the experiment.
   *
   * @return the number of custom property iterator values.
   */
  public int getPropertyArrayLength() {

    return Array.getLength(m_PropertyArray);
  }

  /**
   * Gets a specified value from the custom property iterator array.
   *
   * @param index the index of the value wanted
   * @return the property array value
   */
  public Object getPropertyArrayValue(int index) {

    return Array.get(m_PropertyArray, index);
  }
  
  /* These may potentially want to be made un-transient if it is decided
   * that experiments may be saved mid-run and later resumed
   */
  /** The current run number when the experiment is running */
  protected transient int m_RunNumber;
  /** The current dataset number when the experiment is running */
  protected transient int m_DatasetNumber;
  /** The current custom property value index when the experiment is running */
  protected transient int m_PropertyNumber;
  /** True if the experiment has finished running */
  protected transient boolean m_Finished = true;
  /** The dataset currently being used */
  protected transient Instances m_CurrentInstances;
  /** The custom property value that has actually been set */
  protected transient int m_CurrentProperty;

  /**
   * When an experiment is running, this returns the current run number.
   *
   * @return the current run number.
   */
  public int getCurrentRunNumber() {
    return m_RunNumber;
  }

  /**
   * When an experiment is running, this returns the current dataset number.
   *
   * @return the current dataset number.
   */
  public int getCurrentDatasetNumber() {
    return m_DatasetNumber;
  }

  /**
   * When an experiment is running, this returns the index of the
   * current custom property value.
   *
   * @return the index of the current custom property value.
   */
  public int getCurrentPropertyNumber() {
    return m_PropertyNumber;
  }
  
  /**
   * Prepares an experiment for running, initializing current iterator
   * settings.
   *
   * @throws Exception if an error occurs
   */
  public void initialize() throws Exception {
    
    m_RunNumber = getRunLower();
    m_DatasetNumber = 0;
    m_PropertyNumber = 0;
    m_CurrentProperty = -1;
    m_CurrentInstances = null;
    m_Finished = false;
    if (m_UsePropertyIterator && (m_PropertyArray == null)) {
      throw new Exception("Null array for property iterator");
    }
    if (getRunLower() > getRunUpper()) {
      throw new Exception("Lower run number is greater than upper run number");
    }
    if (getDatasets().size() == 0) {
      throw new Exception("No datasets have been specified");
    }
    if (m_ResultProducer == null) {
      throw new Exception("No ResultProducer set");
    }
    if (m_ResultListener == null) {
      throw new Exception("No ResultListener set");
    }

    //    if (m_UsePropertyIterator && (m_PropertyArray != null)) {
    determineAdditionalResultMeasures();
      //    }

    m_ResultProducer.setResultListener(m_ResultListener);
    m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
    m_ResultProducer.preProcess();

    // constrain the additional measures to be only those allowable
    // by the ResultListener
    String [] columnConstraints = m_ResultListener.
      determineColumnConstraints(m_ResultProducer);

    if (columnConstraints != null) {
      m_ResultProducer.setAdditionalMeasures(columnConstraints);
    }
  }

  /**
   * Iterate over the objects in the property array to determine what
   * (if any) additional measures they support
   * 
   * @throws Exception 	if additional measures don't comply to the naming 
   * 			convention (starting with "measure")
   */
  private void determineAdditionalResultMeasures() throws Exception {
    m_AdditionalMeasures = null;
    FastVector measureNames = new FastVector();

    // first try the result producer, then property array if applicable
    if (m_ResultProducer instanceof AdditionalMeasureProducer) {
      Enumeration am = ((AdditionalMeasureProducer)m_ResultProducer).
	enumerateMeasures();
      while (am.hasMoreElements()) {
	String mname = (String)am.nextElement();
	if (mname.startsWith("measure")) {
	  if (measureNames.indexOf(mname) == -1) {
	    measureNames.addElement(mname);
	  }
	} else {
	  throw new Exception ("Additional measures in "
			       + m_ResultProducer.getClass().getName()
			       +" must obey the naming convention"
			       +" of starting with \"measure\"");
	}
      }
    }

    if (m_UsePropertyIterator && (m_PropertyArray != null)) {
      for (int i = 0; i < Array.getLength(m_PropertyArray); i++) {
	Object current = Array.get(m_PropertyArray, i);

	if (current instanceof AdditionalMeasureProducer) {
	  Enumeration am = ((AdditionalMeasureProducer)current).
	    enumerateMeasures();
	  while (am.hasMoreElements()) {
	    String mname = (String)am.nextElement();
	    if (mname.startsWith("measure")) {
	      if (measureNames.indexOf(mname) == -1) {
		measureNames.addElement(mname);
	      }
	    } else {
	      throw new Exception ("Additional measures in "
				   + current.getClass().getName()
				   +" must obey the naming convention"
				   +" of starting with \"measure\"");
	    }
	  }
	}
      }
    }
    if (measureNames.size() > 0) {
      m_AdditionalMeasures = new String [measureNames.size()];
      for (int i=0;i<measureNames.size();i++) {
	m_AdditionalMeasures[i] = (String)measureNames.elementAt(i);
      }
    }
  }

  
  /**
   * Recursively sets the custom property value, by setting all values
   * along the property path.
   *
   * @param propertyDepth the current position along the property path
   * @param origValue the value to set the property to
   * @throws Exception if an error occurs
   */
  protected void setProperty(int propertyDepth, Object origValue)
    throws Exception {
    
    PropertyDescriptor current = m_PropertyPath[propertyDepth].property;
    Object subVal = null;
    if (propertyDepth < m_PropertyPath.length - 1) {
      Method getter = current.getReadMethod();
      Object getArgs [] = { };
      subVal = getter.invoke(origValue, getArgs);
      setProperty(propertyDepth + 1, subVal);
    } else {
      subVal = Array.get(m_PropertyArray, m_PropertyNumber);
    }
    Method setter = current.getWriteMethod();
    Object [] args = { subVal };
    setter.invoke(origValue, args);
  }

  /**
   * Returns true if there are more iterations to carry out in the experiment.
   *
   * @return true if so
   */
  public boolean hasMoreIterations() {

    return !m_Finished;
  }
  
  /**
   * Carries out the next iteration of the experiment.
   *
   * @throws Exception if an error occurs
   */
  public void nextIteration() throws Exception {
    
    if (m_UsePropertyIterator) {
      if (m_CurrentProperty != m_PropertyNumber) {
	setProperty(0, m_ResultProducer);
	m_CurrentProperty = m_PropertyNumber;
      }
    }
    
    if (m_CurrentInstances == null) {
      File currentFile = (File) getDatasets().elementAt(m_DatasetNumber);
      AbstractFileLoader loader = ConverterUtils.getLoaderForFile(currentFile);
      loader.setFile(currentFile);
      Instances data = new Instances(loader.getDataSet());
      // only set class attribute if not already done by loader
      if (data.classIndex() == -1) {
	if (m_ClassFirst) {
	  data.setClassIndex(0);
	} else {
	  data.setClassIndex(data.numAttributes() - 1);
	}
      }
      m_CurrentInstances = data;
      m_ResultProducer.setInstances(m_CurrentInstances);
    }
    
    m_ResultProducer.doRun(m_RunNumber);

    advanceCounters();
  }

  /**
   * Increments iteration counters appropriately.
   */
  public void advanceCounters() {

    if (m_AdvanceDataSetFirst) {
      m_RunNumber ++;
      if (m_RunNumber > getRunUpper()) {
	m_RunNumber = getRunLower();
	m_DatasetNumber ++;
	m_CurrentInstances = null;
	if (m_DatasetNumber >= getDatasets().size()) {
	  m_DatasetNumber = 0;
	  if (m_UsePropertyIterator) {
	    m_PropertyNumber ++;
	    if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) {
	      m_Finished = true;
	    }
	  } else {
	    m_Finished = true;
	  }
	}
      }
    } else { // advance by custom iterator before data set
      m_RunNumber ++;
      if (m_RunNumber > getRunUpper()) {
	m_RunNumber = getRunLower();
	if (m_UsePropertyIterator) {
	  m_PropertyNumber ++;
	  if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) {
	    m_PropertyNumber = 0;
	    m_DatasetNumber ++;
	    m_CurrentInstances = null;
	    if (m_DatasetNumber >= getDatasets().size()) {
	      m_Finished = true;
	    } 
	  }
	} else {
	  m_DatasetNumber ++;
	  m_CurrentInstances = null;
	  if (m_DatasetNumber >= getDatasets().size()) {
	    m_Finished = true;
	  }
	}
      }
    }
  }

  /**
   * Runs all iterations of the experiment, continuing past errors.
   */
  public void runExperiment() {

    while (hasMoreIterations()) {
      try {
	nextIteration();
      } catch (Exception ex) {
	ex.printStackTrace();
	System.err.println(ex.getMessage());
	advanceCounters(); // Try to keep plowing through
      }
    }
  }

  /**
   * Signals that the experiment is finished running, so that cleanup
   * can be done.
   *
   * @throws Exception if an error occurs
   */
  public void postProcess() throws Exception {

    m_ResultProducer.postProcess();
  }
  
  /**
   * Gets the datasets in the experiment.
   *
   * @return the datasets in the experiment.
   */
  public DefaultListModel getDatasets() {
    return m_Datasets;
  }

  /**
   * Set the datasets to use in the experiment
   * @param ds the list of datasets to use
   */
  public void setDatasets(DefaultListModel ds) {
    m_Datasets = ds;
  }

  /**
   * Gets the result listener where results will be sent.
   *
   * @return the result listener where results will be sent.
   */
  public ResultListener getResultListener() {
    
    return m_ResultListener;
  }
  
  /**
   * Sets the result listener where results will be sent.
   *
   * @param newResultListener the result listener where results will be sent.
   */
  public void setResultListener(ResultListener newResultListener) {
    
    m_ResultListener = newResultListener;
  }
  
  /**
   * Get the result producer used for the current experiment.
   *
   * @return the result producer used for the current experiment.
   */
  public ResultProducer getResultProducer() {
    
    return m_ResultProducer;
  }
  
  /**
   * Set the result producer used for the current experiment.
   *
   * @param newResultProducer result producer to use for the current 
   * experiment.
   */
  public void setResultProducer(ResultProducer newResultProducer) {
    
    m_ResultProducer = newResultProducer;
  }
  
  /**
   * Get the upper run number for the experiment.
   *
   * @return the upper run number for the experiment.
   */
  public int getRunUpper() {
    
    return m_RunUpper;
  }
  
  /**
   * Set the upper run number for the experiment.
   *
   * @param newRunUpper the upper run number for the experiment.
   */
  public void setRunUpper(int newRunUpper) {
    
    m_RunUpper = newRunUpper;
  }
  
  /**
   * Get the lower run number for the experiment.
   *
   * @return the lower run number for the experiment.
   */
  public int getRunLower() {
    
    return m_RunLower;
  }
  
  /**
   * Set the lower run number for the experiment.
   *
   * @param newRunLower the lower run number for the experiment.
   */
  public void setRunLower(int newRunLower) {
    
    m_RunLower = newRunLower;
  }

  
  /**
   * Get the user notes.
   *
   * @return User notes associated with the experiment.
   */
  public String getNotes() {
    
    return m_Notes;
  }
  
  /**
   * Set the user notes.
   *
   * @param newNotes New user notes.
   */
  public void setNotes(String newNotes) {
    
    m_Notes = newNotes;
  }
  
  /**
   * Returns an enumeration describing the available options..
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(6);

    newVector.addElement(new Option(
	     "\tThe lower run number to start the experiment from.\n"
	      +"\t(default 1)", 
	     "L", 1, 
	     "-L <num>"));
    newVector.addElement(new Option(
	     "\tThe upper run number to end the experiment at (inclusive).\n"
	      +"\t(default 10)", 
	     "U", 1, 
	     "-U <num>"));
    newVector.addElement(new Option(
	     "\tThe dataset to run the experiment on.\n"
	     + "\t(required, may be specified multiple times)", 
	     "T", 1, 
	     "-T <arff file>"));
    newVector.addElement(new Option(
	     "\tThe full class name of a ResultProducer (required).\n"
	      +"\teg: weka.experiment.RandomSplitResultProducer", 
	     "P", 1, 
	     "-P <class name>"));
    newVector.addElement(new Option(
	     "\tThe full class name of a ResultListener (required).\n"
	      +"\teg: weka.experiment.CSVResultListener", 
	     "D", 1, 
	     "-D <class name>"));
    newVector.addElement(new Option(
	     "\tA string containing any notes about the experiment.\n"
	      +"\t(default none)", 
	     "N", 1, 
	     "-N <string>"));

    if ((m_ResultProducer != null) &&
	(m_ResultProducer instanceof OptionHandler)) {
      newVector.addElement(new Option(
	     "",
	     "", 0, "\nOptions specific to result producer "
	     + m_ResultProducer.getClass().getName() + ":"));
      Enumeration enm = ((OptionHandler)m_ResultProducer).listOptions();
      while (enm.hasMoreElements()) {
	newVector.addElement(enm.nextElement());
      }
    }
    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -L &lt;num&gt;
   *  The lower run number to start the experiment from.
   *  (default 1)</pre>
   * 
   * <pre> -U &lt;num&gt;
   *  The upper run number to end the experiment at (inclusive).
   *  (default 10)</pre>
   * 
   * <pre> -T &lt;arff file&gt;
   *  The dataset to run the experiment on.
   *  (required, may be specified multiple times)</pre>
   * 
   * <pre> -P &lt;class name&gt;
   *  The full class name of a ResultProducer (required).
   *  eg: weka.experiment.RandomSplitResultProducer</pre>
   * 
   * <pre> -D &lt;class name&gt;
   *  The full class name of a ResultListener (required).
   *  eg: weka.experiment.CSVResultListener</pre>
   * 
   * <pre> -N &lt;string&gt;
   *  A string containing any notes about the experiment.
   *  (default none)</pre>
   * 
   * <pre> 
   * Options specific to result producer weka.experiment.RandomSplitResultProducer:
   * </pre>
   * 
   * <pre> -P &lt;percent&gt;
   *  The percentage of instances to use for training.
   *  (default 66)</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> -R
   *  Set when data is not to be randomized and the data sets' size.
   *  Is not to be determined via probabilistic rounding.</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. <p>
   *
   * @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 lowerString = Utils.getOption('L', options);
    if (lowerString.length() != 0) {
      setRunLower(Integer.parseInt(lowerString));
    } else {
      setRunLower(1);
    }
    String upperString = Utils.getOption('U', options);
    if (upperString.length() != 0) {
      setRunUpper(Integer.parseInt(upperString));
    } else {
      setRunUpper(10);
    }
    if (getRunLower() > getRunUpper()) {
      throw new Exception("Lower (" + getRunLower() 
			  + ") is greater than upper (" 
			  + getRunUpper() + ")");
    }
    
    setNotes(Utils.getOption('N', options));
    
    getDatasets().removeAllElements();
    String dataName;
    do {
      dataName = Utils.getOption('T', options);
      if (dataName.length() != 0) {
	File dataset = new File(dataName);
	getDatasets().addElement(dataset);
      }
    } while (dataName.length() != 0);
    if (getDatasets().size() == 0) {
      throw new Exception("Required: -T <arff file name>");
    }

    String rlName = Utils.getOption('D', options);
    if (rlName.length() == 0) {
      throw new Exception("Required: -D <ResultListener class name>");
    }
    rlName = rlName.trim();
    // split off any options
    int breakLoc = rlName.indexOf(' ');
    String clName = rlName;
    String rlOptionsString = "";
    String [] rlOptions = null;
    if (breakLoc != -1) {
      clName = rlName.substring(0, breakLoc);
      rlOptionsString = rlName.substring(breakLoc).trim();
      rlOptions = Utils.splitOptions(rlOptionsString);
    }
    setResultListener((ResultListener)Utils.forName(ResultListener.class,
						    clName, rlOptions));

    String rpName = Utils.getOption('P', options);
    if (rpName.length() == 0) {
      throw new Exception("Required: -P <ResultProducer class name>");
    }
    // Do it first without options, so if an exception is thrown during
    // the option setting, listOptions will contain options for the actual
    // RP.
    //GHF -- nice idea, but it prevents you from using result producers that
    //       have *required* parameters
    setResultProducer((ResultProducer)Utils.forName(
		      ResultProducer.class,
		      rpName,
		      Utils.partitionOptions(options) )); //GHF
    //GHF if (getResultProducer() instanceof OptionHandler) {
    //GHF  ((OptionHandler) getResultProducer())
    //GHF  .setOptions(Utils.partitionOptions(options));
    //GHF }
  }

  /**
   * Gets the current settings of the experiment iterator.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    // Currently no way to set custompropertyiterators from the command line

    m_UsePropertyIterator = false;
    m_PropertyPath = null;
    m_PropertyArray = null;
    
    String [] rpOptions = new String [0];
    if ((m_ResultProducer != null) && 
	(m_ResultProducer instanceof OptionHandler)) {
      rpOptions = ((OptionHandler)m_ResultProducer).getOptions();
    }
    
    String [] options = new String [rpOptions.length 
				   + getDatasets().size() * 2
				   + 11];
    int current = 0;

    options[current++] = "-L"; options[current++] = "" + getRunLower();
    options[current++] = "-U"; options[current++] = "" + getRunUpper();
    if (getDatasets().size() != 0) {
      for (int i = 0; i < getDatasets().size(); i++) {
	options[current++] = "-T";
	options[current++] = getDatasets().elementAt(i).toString();
      }
    }
    if (getResultListener() != null) {
      options[current++] = "-D";
      options[current++] = getResultListener().getClass().getName();
    }
    if (getResultProducer() != null) {
      options[current++] = "-P";
      options[current++] = getResultProducer().getClass().getName();
    }
    if (!getNotes().equals("")) {
      options[current++] = "-N"; options[current++] = getNotes();
    }
    options[current++] = "--";

    System.arraycopy(rpOptions, 0, options, current, 
		     rpOptions.length);
    current += rpOptions.length;
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Gets a string representation of the experiment configuration.
   *
   * @return a value of type 'String'
   */
  public String toString() {

    String result = "Runs from: " + m_RunLower + " to: " + m_RunUpper + '\n';
    result += "Datasets:";
    for (int i = 0; i < m_Datasets.size(); i ++) {
      result += " " + m_Datasets.elementAt(i);
    }
    result += '\n';
    result += "Custom property iterator: "
      + (m_UsePropertyIterator ? "on" : "off")
      + "\n";
    if (m_UsePropertyIterator) {
      if (m_PropertyPath == null) {
	throw new Error("*** null propertyPath ***");
      }
      if (m_PropertyArray == null) {
	throw new Error("*** null propertyArray ***");
      }
      if (m_PropertyPath.length > 1) {
	result += "Custom property path:\n";
	for (int i = 0; i < m_PropertyPath.length - 1; i++) {
	  PropertyNode pn = m_PropertyPath[i];
	  result += "" + (i + 1) + "  " + pn.parentClass.getName()
	    + "::" + pn.toString()
	    + ' ' + pn.value.toString() + '\n';
	}
      }
      result += "Custom property name:"
	+ m_PropertyPath[m_PropertyPath.length - 1].toString() + '\n';
      result += "Custom property values:\n";
      for (int i = 0; i < Array.getLength(m_PropertyArray); i++) {
	Object current = Array.get(m_PropertyArray, i);
	result += " " + (i + 1)
	  + " " + current.getClass().getName()
	  + " " + current.toString() + '\n';
      }
    }
    result += "ResultProducer: " + m_ResultProducer + '\n';
    result += "ResultListener: " + m_ResultListener + '\n';
    if (!getNotes().equals("")) {
      result += "Notes: " + getNotes();
    }
    return result;
  }

  /**
   * Loads an experiment from a file.
   * 
   * @param filename	the file to load the experiment from
   * @return		the experiment
   * @throws Exception	if loading fails
   */
  public static Experiment read(String filename) throws Exception {
    Experiment	result;
    
    // KOML?
    if ( (KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION)) ) {
      result = (Experiment) KOML.read(filename);
    }
    // XML?
    else if (filename.toLowerCase().endsWith(".xml")) {
      XMLExperiment xml = new XMLExperiment(); 
      result = (Experiment) xml.read(filename);
    }
    // binary
    else {
      FileInputStream fi = new FileInputStream(filename);
      ObjectInputStream oi = new ObjectInputStream(
	  new BufferedInputStream(fi));
      result = (Experiment)oi.readObject();
      oi.close();
    }
    
    return result;
  }
  
  /**
   * Writes the experiment to disk.
   * 
   * @param filename	the file to write to
   * @param exp		the experiment to save
   * @throws Exception	if writing fails
   */
  public static void write(String filename, Experiment exp) throws Exception {
    // KOML?
    if ( (KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION)) ) {
      KOML.write(filename, exp);
    }
    // XML?
    else if (filename.toLowerCase().endsWith(".xml")) {
      XMLExperiment xml = new XMLExperiment(); 
      xml.write(filename, exp);
    }
    // binary
    else {
      FileOutputStream fo = new FileOutputStream(filename);
      ObjectOutputStream oo = new ObjectOutputStream(
	  new BufferedOutputStream(fo));
      oo.writeObject(exp);
      oo.close();
    }
  }
  
  /**
   * Configures/Runs the Experiment from the command line.
   *
   * @param args command line arguments to the Experiment.
   */
  public static void main(String[] args) {

    try {
      Experiment exp = null;
      // get options from XML?
      String xmlOption = Utils.getOption("xml", args);
      if (!xmlOption.equals(""))
         args = new XMLOptions(xmlOption).toArray();
      
      String expFile = Utils.getOption('l', args);
      String saveFile = Utils.getOption('s', args);
      boolean runExp = Utils.getFlag('r', args);
      if (expFile.length() == 0) {
	exp = new Experiment();
	try {
	  exp.setOptions(args);
	  Utils.checkForRemainingOptions(args);
	} catch (Exception ex) {
	  ex.printStackTrace();
	  String result = "Usage:\n\n"
	    + "-l <exp|xml file>\n"
	    + "\tLoad experiment from file (default use cli options).\n"
      + "\tThe type is determined, based on the extension (" 
        + FILE_EXTENSION + " or .xml)\n"
	    + "-s <exp|xml file>\n"
	    + "\tSave experiment to file after setting other options.\n"
      + "\tThe type is determined, based on the extension (" 
        + FILE_EXTENSION + " or .xml)\n"
	    + "\t(default don't save)\n"
	    + "-r\n"
	    + "\tRun experiment (default don't run)\n"
	    + "-xml <filename | xml-string>\n"
	    + "\tget options from XML-Data instead from parameters\n"
            + "\n";
	  Enumeration enm = ((OptionHandler)exp).listOptions();
	  while (enm.hasMoreElements()) {
	    Option option = (Option) enm.nextElement();
	    result += option.synopsis() + "\n";
	    result += option.description() + "\n";
	  }
	  throw new Exception(result + "\n" + ex.getMessage());
	}
      } else {
	exp = read(expFile);

	// allow extra datasets to be added to pre-loaded experiment from command line
	String dataName;
	do {
	  dataName = Utils.getOption('T', args);
	  if (dataName.length() != 0) {
	    File dataset = new File(dataName);
	    exp.getDatasets().addElement(dataset);
	  }
	} while (dataName.length() != 0);
	
      }
      System.err.println("Experiment:\n" + exp.toString());

      if (saveFile.length() != 0)
	write(saveFile, exp);
      
      if (runExp) {
	System.err.println("Initializing...");
	exp.initialize();
	System.err.println("Iterating...");
	exp.runExperiment();
	System.err.println("Postprocessing...");
	exp.postProcess();
      }
      
    } catch (Exception ex) {
      System.err.println(ex.getMessage());
    }
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5399 $");
  }
} // Experiment
