long start, stop;    	Executor pe=ExecutorSinglet.getExecutor();	for (int iter= 0; iter < iterNo	&& !m_learningTree.boosterIsFinished(); iter++) {	    if (Monitor.logLevel > 1) {		m_monitor.logIteration(				       iter,				       m_learningTree.getCombinedPredictor(),				       m_learningTree.getLastBasePredictor());	    }	    start= System.currentTimeMillis();	    Vector candidates= m_learningTree.getCandidates();	    stop= System.currentTimeMillis();	    if (Monitor.logLevel > 3) {		Monitor.log("Learning iteration " + iter + " candidates are:");		Monitor.log(candidates.toString());		Monitor.log("It took " + (stop - start) / 1000.0 + " seconds to generate candidates Vector for iteration "			    + iter);	    }	    // This piece should be replaced by a more general tool to	    // measure the goodness of a split.      	    // Create a synchronization barrier that counts the number	    // of processed splitter builders	    CountDown candidateCount=new CountDown(candidates.size());	    // an array to record losses in	    double[] losses=new double[candidates.size()];      	    int i=0;	    for(Iterator ci=candidates.iterator();ci.hasNext();) {		CandidateSplit candidate=(CandidateSplit)ci.next();		SplitEvaluatorWorker sew=new SplitEvaluatorWorker(candidate,losses,i,candidateCount);		try {		    pe.execute(sew);		}  catch (InterruptedException ie) {		    System.err.println("exception ocurred while handing off the candidate job to the pool: "+ie.getMessage());		    ie.printStackTrace();		}		i++;	    }      	    try {		candidateCount.acquire();	    } catch(InterruptedException ie) {		if(candidateCount.currentCount()!=0) {		    System.err.println("interrupted exception occurred, but the candidateCount is "+candidateCount.currentCount());		}	    };      	    // run through the losses results to determine the best split	    int best=0;	    if (losses.length==0) {		System.err.println("ERROR: There are no candidate weak hypotheses to add to the tree.");		System.err.println("This is likely a bug in JBoost; please report to JBoost developers.");		System.exit(2);	    }	    double bestLoss=losses[best];	    double tmpLoss;	    for(int li=1;li<losses.length;li++) {		if((tmpLoss=losses[li]) < bestLoss) {		    bestLoss=tmpLoss;		    best=li;		}	    }	    if (Monitor.logLevel > 3)		Monitor.log("Best candidate is: " + (CandidateSplit) candidates.get(best) + "\n");	    // add the candidate with lowest loss	    start= System.currentTimeMillis();	    m_learningTree.addCandidate((CandidateSplit) candidates.get(best));	    stop= System.currentTimeMillis();	    if (Monitor.logLevel > 3) {		Monitor.log(			    "It took "			    + (stop - start) / 1000.0			    + " seconds to add candidate for iteration "			    + iter);	    }	    System.out.println("Finished learning iteration " + iter);	    if(m_booster instanceof BrownBoost){		iterNo++;	    }	}	System.out.println();	if (Monitor.logLevel > 3)	    m_monitor.logIteration(				   iterNo,				   m_learningTree.getCombinedPredictor(),				   m_learningTree.getLastBasePredictor());    }      /**     * @param cp a predictor     * @throws IncompAttException     */    private void test(Predictor cp)	throws NotSupportedException, InstrumentException {	int size= m_config.getTestSet().getExampleNo();	int i= 0;	Example ex= null;	Monitor.log("Testing rule.");	LabelDescription labelDescription= m_exampleDescription.getLabelDescription();	try {	    for (i= 0; i < size; i++) {		ex= m_config.getTestSet().getExample(i);		Prediction prediction= cp.predict(ex.getInstance());		Label label= ex.getLabel();		if (!prediction.getBestClass().equals(label)) {		    Monitor.log("Test Example " + i + "   -----------------------------");		    Monitor.log(ex);		    Monitor.log("------------------------------------------");		    Monitor.log(prediction);		    Monitor.log(				"Explanation: " + ((AlternatingTree) cp).explain(ex.getInstance()));		}	    }	} catch (IncompAttException e) {	    // TODO add something here?	}    }      /**     * build the array of splitterBuilders     * @throws IncompAttException     */    private void buildSplitterBuilderArray() throws IncompAttException {	Vector sbf= SplitterBuilderFamily.factory(m_config);	m_splitterBuilderVector= new Vector();    	for (int i= 0; i < sbf.size(); i++) {	    m_splitterBuilderVector.addAll(((SplitterBuilderFamily) sbf.get(i)).build(										      m_exampleDescription,										      m_config,										      m_booster));	}    	if (Monitor.logLevel > 3) {	    Monitor.log("The initial array of splitter Builders is:");	    for (int i= 0; i < m_splitterBuilderVector.size(); i++) {		Monitor.log("builder " + i + m_splitterBuilderVector.get(i));	    }	}    }    /**     * Determine the private weight for this example     * If the margin weight of the example is less than the threshold,     * then we accept the example with probability margin/threshold and     * set the private weight to 1/margin     * Otherwise we accept the example and set the private weight to 1/threshold      * @param data     * @param threshold     * @return     */    private double calculateExampleWeight(Example data, double threshold) {	double weight= -1; 	Label label= data.getLabel();	Instance instance= data.getInstance();	double[] weights= m_serializedTree.predict(instance).getMargins(label);	double margin=0;	//  for now, just handle the binary prediction case	if (weights.length == 1) {	    margin= m_booster.calculateWeight(weights[0]);        	} // else handle multi-label    	if (margin < threshold) {	    double prob= margin/threshold;	    if (Math.random() <= prob) { 		weight= 1/margin;	    }	} else {	    weight= 1/threshold;	}	return weight;    }       /**     * Add example to booster and training set.      * Update SplitterBuilder vector for this example     * @param counter     * @param example     * @param exampleWeight     */    private void addTrainingExample(int counter, Example example, double exampleWeight) {    	if (Monitor.logLevel > 0) {	    m_config.getTrainSet().addExample(counter, example);	}    	m_booster.addExample(counter, example.getLabel(), exampleWeight);    	for (int i= 0; i < m_splitterBuilderVector.size(); i++) {	    if (Monitor.logLevel > 5) {		Monitor.log("the class of splitterBuilder " + i + " is "			    + m_splitterBuilderVector.get(i).getClass());	    }	    ((SplitterBuilder) m_splitterBuilderVector.get(i)).addExample(counter, example);	}        }        /**     * Read the training file and initialize the booster and the splitterBuilders     * with its content     * @throws BadLabelException     * @throws IncompAttException     * @throws ParseException     */    private void readTrainData()	throws IncompAttException, ParseException {    	long start, stop;	m_config.setTrainSet(new ExampleSet(m_exampleDescription));    	Example example= null;	int counter= 0;	start= System.currentTimeMillis();	boolean sampling= m_config.getBool(ControllerConfiguration.SAMPLE_TRAINING_DATA, false);	double threshold= m_config.getDouble(ControllerConfiguration.SAMPLE_THRESHOLD_WEIGHT, 0.500);	while ((example= m_trainStream.getExample()) != null) {      	    double exampleWeight= example.getWeight();    	    boolean accepted= true;	    // if we are sampling, then calculate the weight of this example	    // if the exampleWeight is zero, then don't accept this example	    if (sampling) {		exampleWeight= calculateExampleWeight(example, threshold);		if (exampleWeight < 0) {		    accepted= false;		}	    }	    // The default behavior is to accept each example. An	    // example is only refused if we are sampling and its	    // weight is set to zero.	    if (accepted) {		addTrainingExample(counter, example, exampleWeight);		counter++;	    }	    if ((counter % 100) == 0) {		System.out.print("Read " + counter + " training examples\n");	    }	}    	stop= System.currentTimeMillis();	System.out.println("Read " + counter + " training examples in " + (stop - start) / 1000.0			   + " seconds.");	m_config.getTrainSet().finalizeData();	m_booster.finalizeData();	for (int i= 0; i < m_splitterBuilderVector.size(); i++) {	    ((SplitterBuilder) m_splitterBuilderVector.get(i)).finalizeData();	}	m_trainSetIndices= new int[counter];	for (int i= 0; i < counter; i++)	    m_trainSetIndices[i]= i;    }      /**     * initialize the tokenizer      * @throws Exception      */    private void startTokenizer() throws Exception {	DataStream ds= null;	ds= new jboost_DataStream(m_config.getSpecFileName(), m_config.getTrainFileName());	m_trainStream= new ExampleStream(ds);	ds= new jboost_DataStream(m_config.getSpecFileName(), m_config.getTestFileName());	m_testStream= new ExampleStream(ds);    }        /** read the test data file */    private void readTestData()	throws BadLabelException, IncompAttException, ParseException {    	long start, stop;	m_config.setTestSet(new ExampleSet(m_exampleDescription));	Example example= null;	int counter= 0;	start= System.currentTimeMillis();	while ((example= m_testStream.getExample()) != null) {	    m_config.getTestSet().addExample(counter, example);	    counter++;	    if ((counter % 100) == 0) {		System.out.print("read " + counter + " test examples\n");	    }	}	stop= System.currentTimeMillis();	System.out.println("read " + counter + " test examples in " + (stop - start) / 1000.0			   + " seconds.");	m_config.getTestSet().finalizeData();    }        private void reportResults() {	try {	    PrintWriter resultOutputStream=		new PrintWriter(new BufferedWriter(new FileWriter(m_config.getResultOutputFileName())));	    resultOutputStream.println(m_learningTree);	    resultOutputStream.close();	} catch (Exception e) {	    System.err.println("Exception occured while attempting to write result");	    e.printStackTrace();	}    }    private void generateCode(			      WritablePredictor predictor,			      String language,			      String codeOutputFileName,			      String procedureName) {	try {	    String code= null;	    if (language.equals("C"))		code= predictor.toC(procedureName, m_exampleDescription);	    else if (language.equals("MatLab"))		code= predictor.toMatlab(procedureName, m_exampleDescription);	    else if (language.equals("java"))		code=		    predictor.toJava(				     procedureName,				     m_config.getString("javaOutputMethod", "predict"),				     (m_config.getBool("javaStandAlone", false) ? null : m_config.getSpecFileName()),				     m_exampleDescription);	    else		throw new RuntimeException(					   "Controller.generateCode: Unrecognized language:" + language);	    PrintWriter codeOutputStream=		new PrintWriter(new BufferedWriter(new FileWriter(codeOutputFileName)));	    codeOutputStream.println(code);	    codeOutputStream.close();	} catch (Exception e) {	    System.err.println(			       "Exception occured while attempting to write " + language + " code");	    System.err.println("Message:" + e);	    e.printStackTrace();	}    }}