JBUILDER5 :"sample.java": Error #: 750 : initialization error: com.borland"

Hi there,
I am trying to compile a few sample with new release of jdk1.4.0. It does with DOS-Promt. I get an error like ""sample.java": Error #: 750 : initialization error: com.borland.compiler.symtab.LoadError: class file has wrong version 48.0" when I try it with Jbuilder5. Any idea to work around this is apprecited.
Thanks
SY.

Forget employing jdk 1.4 with JB 5, it dosen't support it; too many changes in the jdk. If you want to employ it you'll have to undate to JB 6.0. Sorry.
Abraham.

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    String password = args[2];
    // 1. Login to the Data Mining Engine
    m_dmeConnFactory = new OraConnectionFactory();
    ConnectionSpec connSpec = m_dmeConnFactory.getConnectionSpec();
    connSpec.setURI("jdbc:oracle:thin:@"+uri);
    connSpec.setName(name);
    connSpec.setPassword(password);
    m_dmeConn = m_dmeConnFactory.getConnection(connSpec);
    // 2. Clean up all previuosly created demo objects
    clean();
    // 3. Initialize factories for mining objects
    initFactories();
    m_costMatrixName = createCostMatrix();
    // 4. Build model with supplied cost matrix
    buildModel();
    // 5. Test model - To compute accuracy and confusion matrix, lift result
    // and ROC for the model from apply output data.
    // Please see dnnbdemo.java to see how to test the model
    // with a test input data and cost matrix.
    // Test the model with cost matrix
    computeTestMetrics("DT_TEST_APPLY_OUTPUT_COST_JDM",
    "dtTestMetricsWithCost_jdm", m_costMatrixName);
    // Test the model without cost matrix
    computeTestMetrics("DT_TEST_APPLY_OUTPUT_JDM",
    "dtTestMetrics_jdm", null);
    // 6. Apply the model
    applyModel();
    } catch(Exception anyExp) {
    anyExp.printStackTrace(System.out);
    } finally {
    try {
    //6. Logout from the Data Mining Engine
    m_dmeConn.close();
    } catch(Exception anyExp1) { }//Ignore
    * Initialize all object factories used in the demo program.
    * @exception JDMException if factory initalization failed
    public static void initFactories() throws JDMException
    m_pdsFactory = (PhysicalDataSetFactory)m_dmeConn.getFactory(
    "javax.datamining.data.PhysicalDataSet");
    m_paFactory = (PhysicalAttributeFactory)m_dmeConn.getFactory(
    "javax.datamining.data.PhysicalAttribute");
    m_clasFactory = (ClassificationSettingsFactory)m_dmeConn.getFactory(
    "javax.datamining.supervised.classification.ClassificationSettings");
    m_treeFactory = (TreeSettingsFactory) m_dmeConn.getFactory(
    "javax.datamining.algorithm.tree.TreeSettings");
    m_buildFactory = (BuildTaskFactory)m_dmeConn.getFactory(
    "javax.datamining.task.BuildTask");
    m_dsApplyFactory = (DataSetApplyTaskFactory)m_dmeConn.getFactory(
    "javax.datamining.task.apply.DataSetApplyTask");
    m_testFactory = (ClassificationTestTaskFactory)m_dmeConn.getFactory(
    "javax.datamining.supervised.classification.ClassificationTestTask");
    m_applySettingsFactory = (ClassificationApplySettingsFactory)m_dmeConn.getFactory(
    "javax.datamining.supervised.classification.ClassificationApplySettings");
    m_costMatrixFactory = (CostMatrixFactory)m_dmeConn.getFactory(
    "javax.datamining.supervised.classification.CostMatrix");
    m_catSetFactory = (CategorySetFactory)m_dmeConn.getFactory(
    "javax.datamining.data.CategorySet" );
    m_testMetricsTaskFactory = (ClassificationTestMetricsTaskFactory)m_dmeConn.getFactory(
    "javax.datamining.supervised.classification.ClassificationTestMetricsTask");
    * This method illustrates how to build a mining model using the
    * MINING_DATA_BUILD_V dataset and classification settings with
    * DT algorithm.
    * @exception JDMException if model build failed
    public static void buildModel() throws JDMException
    System.out.println("---------------------------------------------------");
    System.out.println("--- Build Model - using cost matrix ---");
    System.out.println("---------------------------------------------------");
    // 1. Create & save PhysicalDataSpecification
    PhysicalDataSet buildData =
    m_pdsFactory.create("MINING_DATA_BUILD_V", false);
    PhysicalAttribute pa = m_paFactory.create("CUST_ID",
    AttributeDataType.integerType, PhysicalAttributeRole.caseId );
    buildData.addAttribute(pa);
    m_dmeConn.saveObject("treeBuildData_jdm", buildData, true);
    //2. Create & save Mining Function Settings
    // Create tree algorithm settings
    TreeSettings treeAlgo = m_treeFactory.create();
    // By default, tree algorithm will have the following settings:
    // treeAlgo.setBuildHomogeneityMetric(TreeHomogeneityMetric.gini);
    // treeAlgo.setMaxDepth(7);
    // ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(0.1, SizeUnit.percentage);
    // treeAlgo.setMinNodeSize( 0.05, SizeUnit.percentage );
    // treeAlgo.setMinNodeSize( 10, SizeUnit.count );
    // ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(20, SizeUnit.count);
    // Set cost matrix. A cost matrix is used to influence the weighting of
    // misclassification during model creation (and scoring).
    // See Oracle Data Mining Concepts Guide for more details.
    String costMatrixName = m_costMatrixName;
    // Create ClassificationSettings
    ClassificationSettings buildSettings = m_clasFactory.create();
    buildSettings.setAlgorithmSettings(treeAlgo);
    buildSettings.setCostMatrixName(costMatrixName);
    buildSettings.setTargetAttributeName("AFFINITY_CARD");
    m_dmeConn.saveObject("treeBuildSettings_jdm", buildSettings, true);
    // 3. Create, save & execute Build Task
    BuildTask buildTask = m_buildFactory.create(
    "treeBuildData_jdm", // Build data specification
    "treeBuildSettings_jdm", // Mining function settings name
    "treeModel_jdm" // Mining model name
    buildTask.setDescription("treeBuildTask_jdm");
    executeTask(buildTask, "treeBuildTask_jdm");
    //4. Restore the model from the DME and explore the details of the model
    ClassificationModel model =
    (ClassificationModel)m_dmeConn.retrieveObject(
    "treeModel_jdm", NamedObject.model);
    // Display model build settings
    ClassificationSettings retrievedBuildSettings =
    (ClassificationSettings)model.getBuildSettings();
    if(buildSettings == null)
    System.out.println("Failure to restore build settings.");
    else
    displayBuildSettings(retrievedBuildSettings, "treeBuildSettings_jdm");
    // Display model signature
    displayModelSignature((Model)model);
    // Display model detail
    TreeModelDetail treeModelDetails = (TreeModelDetail) model.getModelDetail();
    displayTreeModelDetailsExtensions(treeModelDetails);
    * Create and save cost matrix.
    * Consider an example where it costs $10 to mail a promotion to a
    * prospective customer and if the prospect becomes a customer, the
    * typical sale including the promotion, is worth $100. Then the cost
    * of missing a customer (i.e. missing a $100 sale) is 10x that of
    * incorrectly indicating that a person is good prospect (spending
    * $10 for the promo). In this case, all prediction errors made by
    * the model are NOT equal. To act on what the model determines to
    * be the most likely (probable) outcome may be a poor choice.
    * Suppose that the probability of a BUY reponse is 10% for a given
    * prospect. Then the expected revenue from the prospect is:
    * .10 * $100 - .90 * $10 = $1.
    * The optimal action, given the cost matrix, is to simply mail the
    * promotion to the customer, because the action is profitable ($1).
    * In contrast, without the cost matrix, all that can be said is
    * that the most likely response is NO BUY, so don't send the
    * promotion. This shows that cost matrices can be very important.
    * The caveat in all this is that the model predicted probabilities
    * may NOT be accurate. For binary targets, a systematic approach to
    * this issue exists. It is ROC, illustrated below.
    * With ROC computed on a test set, the user can see how various model
    * predicted probability thresholds affect the action of mailing a promotion.
    * Suppose I promote when the probability to BUY exceeds 5, 10, 15%, etc.
    * what return can I expect? Note that the answer to this question does
    * not rely on the predicted probabilities being accurate, only that
    * they are in approximately the correct rank order.
    * Assuming that the predicted probabilities are accurate, provide the
    * cost matrix table name as input to the RANK_APPLY procedure to get
    * appropriate costed scoring results to determine the most appropriate
    * action.
    * In this demo, we will create the following cost matrix
    * ActualTarget PredictedTarget Cost
    * 0 0 0
    * 0 1 1
    * 1 0 8
    * 1 1 0
    private static String createCostMatrix() throws JDMException
    String costMatrixName = "treeCostMatrix";
    // Create categorySet
    CategorySet catSet = m_catSetFactory.create(AttributeDataType.integerType);
    // Add category values
    catSet.addCategory(new Integer(0), CategoryProperty.valid);
    catSet.addCategory(new Integer(1), CategoryProperty.valid);
    // Create cost matrix
    CostMatrix costMatrix = m_costMatrixFactory.create(catSet);
    // ActualTarget PredictedTarget Cost
    costMatrix.setValue(new Integer(0), new Integer(0), 0);
    costMatrix.setValue(new Integer(0), new Integer(1), 1);
    costMatrix.setValue(new Integer(1), new Integer(0), 8);
    costMatrix.setValue(new Integer(1), new Integer(1), 0);
    //save cost matrix
    m_dmeConn.saveObject(costMatrixName, costMatrix, true);
    return costMatrixName;
    * This method illustrates how to compute test metrics using
    * an apply output table that has actual and predicted target values. Here the
    * apply operation is done on the MINING_DATA_TEST_V dataset. It creates
    * an apply output table with actual and predicted target values. Using
    * ClassificationTestMetricsTask test metrics are computed. This produces
    * the same test metrics results as ClassificationTestTask.
    * @param applyOutputName apply output table name
    * @param testResultName test result name
    * @param costMatrixName table name of the supplied cost matrix
    * @exception JDMException if model test failed
    public static void computeTestMetrics(String applyOutputName,
    String testResultName, String costMatrixName) throws JDMException
    if (costMatrixName != null) {
    System.out.println("---------------------------------------------------");
    System.out.println("--- Test Model - using apply output table ---");
    System.out.println("--- - using cost matrix table ---");
    System.out.println("---------------------------------------------------");
    else {
    System.out.println("---------------------------------------------------");
    System.out.println("--- Test Model - using apply output table ---");
    System.out.println("--- - using no cost matrix table ---");
    System.out.println("---------------------------------------------------");
    // 1. Do the apply on test data to create an apply output table
    // Create & save PhysicalDataSpecification
    PhysicalDataSet applyData =
    m_pdsFactory.create( "MINING_DATA_TEST_V", false );
    PhysicalAttribute pa = m_paFactory.create("CUST_ID",
    AttributeDataType.integerType, PhysicalAttributeRole.caseId );
    applyData.addAttribute( pa );
    m_dmeConn.saveObject( "treeTestApplyData_jdm", applyData, true );
    // 2 Create & save ClassificationApplySettings
    ClassificationApplySettings clasAS = m_applySettingsFactory.create();
    HashMap sourceAttrMap = new HashMap();
    sourceAttrMap.put( "AFFINITY_CARD", "AFFINITY_CARD" );
    clasAS.setSourceDestinationMap( sourceAttrMap );
    m_dmeConn.saveObject( "treeTestApplySettings_jdm", clasAS, true);
    // 3 Create, store & execute apply Task
    DataSetApplyTask applyTask = m_dsApplyFactory.create(
    "treeTestApplyData_jdm",
    "treeModel_jdm",
    "treeTestApplySettings_jdm",
    applyOutputName);
    if(executeTask(applyTask, "treeTestApplyTask_jdm"))
    // Compute test metrics on new created apply output table
    // 4. Create & save PhysicalDataSpecification
    PhysicalDataSet applyOutputData = m_pdsFactory.create(
    applyOutputName, false );
    applyOutputData.addAttribute( pa );
    m_dmeConn.saveObject( "treeTestApplyOutput_jdm", applyOutputData, true );
    // 5. Create a ClassificationTestMetricsTask
    ClassificationTestMetricsTask testMetricsTask =
    m_testMetricsTaskFactory.create( "treeTestApplyOutput_jdm", // apply output data used as input
    "AFFINITY_CARD", // actual target column
    "PREDICTION", // predicted target column
    testResultName // test metrics result name
    testMetricsTask.computeMetric( // enable confusion matrix computation
    ClassificationTestMetricOption.confusionMatrix, true );
    testMetricsTask.computeMetric( // enable lift computation
    ClassificationTestMetricOption.lift, true );
    testMetricsTask.computeMetric( // enable ROC computation
    ClassificationTestMetricOption.receiverOperatingCharacteristics, true );
    testMetricsTask.setPositiveTargetValue( new Integer(1) );
    testMetricsTask.setNumberOfLiftQuantiles( 10 );
    testMetricsTask.setPredictionRankingAttrName( "PROBABILITY" );
    if (costMatrixName != null) {
    testMetricsTask.setCostMatrixName(costMatrixName);
    displayTable(costMatrixName, "", "order by ACTUAL_TARGET_VALUE, PREDICTED_TARGET_VALUE");
    // Store & execute the task
    boolean isTaskSuccess = executeTask(testMetricsTask, "treeTestMetricsTask_jdm");
    if( isTaskSuccess ) {
    // Restore & display test metrics
    ClassificationTestMetrics testMetrics = (ClassificationTestMetrics)
    m_dmeConn.retrieveObject( testResultName, NamedObject.testMetrics );
    // Display classification test metrics
    displayTestMetricDetails(testMetrics);
    * This method illustrates how to apply the mining model on the
    * MINING_DATA_APPLY_V dataset to predict customer
    * response. After completion of the task apply output table with the
    * predicted results is created at the user specified location.
    * @exception JDMException if model apply failed
    public static void applyModel() throws JDMException
    System.out.println("---------------------------------------------------");
    System.out.println("--- Apply Model ---");
    System.out.println("---------------------------------------------------");
    System.out.println("---------------------------------------------------");
    System.out.println("--- Business case 1 ---");
    System.out.println("--- Find the 10 customers who live in Italy ---");
    System.out.println("--- that are least expensive to be convinced to ---");
    System.out.println("--- use an affinity card. ---");
    System.out.println("---------------------------------------------------");
    // 1. Create & save PhysicalDataSpecification
    PhysicalDataSet applyData =
    m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
    PhysicalAttribute pa = m_paFactory.create("CUST_ID",
    AttributeDataType.integerType, PhysicalAttributeRole.caseId );
    applyData.addAttribute( pa );
    m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
    // 2. Create & save ClassificationApplySettings
    ClassificationApplySettings clasAS = m_applySettingsFactory.create();
    // Add source attributes
    HashMap sourceAttrMap = new HashMap();
    sourceAttrMap.put( "COUNTRY_NAME", "COUNTRY_NAME" );
    clasAS.setSourceDestinationMap( sourceAttrMap );
    // Add cost matrix
    clasAS.setCostMatrixName( m_costMatrixName );
    m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
    // 3. Create, store & execute apply Task
    DataSetApplyTask applyTask = m_dsApplyFactory.create(
    "treeApplyData_jdm", "treeModel_jdm",
    "treeApplySettings_jdm", "TREE_APPLY_OUTPUT1_JDM");
    executeTask(applyTask, "treeApplyTask_jdm");
    // 4. Display apply result -- Note that APPLY results do not need to be
    // reverse transformed, as done in the case of model details. This is
    // because class values of a classification target were not (required to
    // be) binned or normalized.
    // Find the 10 customers who live in Italy that are least expensive to be
    // convinced to use an affinity card.
    displayTable("TREE_APPLY_OUTPUT1_JDM",
    "where COUNTRY_NAME='Italy' and ROWNUM < 11 ",
    "order by COST");
    System.out.println("---------------------------------------------------");
    System.out.println("--- Business case 2 ---");
    System.out.println("--- List ten customers (ordered by their id) ---");
    System.out.println("--- along with likelihood and cost to use or ---");
    System.out.println("--- reject the affinity card. ---");
    System.out.println("---------------------------------------------------");
    // 1. Create & save PhysicalDataSpecification
    applyData =
    m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
    pa = m_paFactory.create("CUST_ID",
    AttributeDataType.integerType, PhysicalAttributeRole.caseId );
    applyData.addAttribute( pa );
    m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
    // 2. Create & save ClassificationApplySettings
    clasAS = m_applySettingsFactory.create();
    // Add cost matrix
    clasAS.setCostMatrixName( m_costMatrixName );
    m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
    // 3. Create, store & execute apply Task
    applyTask = m_dsApplyFactory.create(
    "treeApplyData_jdm", "treeModel_jdm",
    "treeApplySettings_jdm", "TREE_APPLY_OUTPUT2_JDM");
    executeTask(applyTask, "treeApplyTask_jdm");
    // 4. Display apply result -- Note that APPLY results do not need to be
    // reverse transformed, as done in the case of model details. This is
    // because class values of a classification target were not (required to
    // be) binned or normalized.
    // List ten customers (ordered by their id) along with likelihood and cost
    // to use or reject the affinity card (Note: while this example has a
    // binary target, such a query is useful in multi-class classification -
    // Low, Med, High for example).
    displayTable("TREE_APPLY_OUTPUT2_JDM",
    "where ROWNUM < 21",
    "order by CUST_ID, PREDICTION");
    System.out.println("---------------------------------------------------");
    System.out.println("--- Business case 3 ---");
    System.out.println("--- Find the customers who work in Tech support ---");
    System.out.println("--- and are under 25 who is going to response ---");
    System.out.println("--- to the new affinity card program. ---");
    System.out.println("---------------------------------------------------");
    // 1. Create & save PhysicalDataSpecification
    applyData =
    m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
    pa = m_paFactory.create("CUST_ID",
    AttributeDataType.integerType, PhysicalAttributeRole.caseId );
    applyData.addAttribute( pa );
    m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
    // 2. Create & save ClassificationApplySettings
    clasAS = m_applySettingsFactory.create();
    // Add source attributes
    sourceAttrMap = new HashMap();
    sourceAttrMap.put( "AGE", "AGE" );
    sourceAttrMap.put( "OCCUPATION", "OCCUPATION" );
    clasAS.setSourceDestinationMap( sourceAttrMap );
    m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
    // 3. Create, store & execute apply Task
    applyTask = m_dsApplyFactory.create(
    "treeApplyData_jdm", "treeModel_jdm",
    "treeApplySettings_jdm", "TREE_APPLY_OUTPUT3_JDM");
    executeTask(applyTask, "treeApplyTask_jdm");
    // 4. Display apply result -- Note that APPLY results do not need to be
    // reverse transformed, as done in the case of model details. This is
    // because class values of a classification target were not (required to
    // be) binned or normalized.
    // Find the customers who work in Tech support and are under 25 who is
    // going to response to the new affinity card program.
    displayTable("TREE_APPLY_OUTPUT3_JDM",
    "where OCCUPATION = 'TechSup' " +
    "and AGE < 25 " +
    "and PREDICTION = 1 ",
    "order by CUST_ID");
    * This method stores the given task with the specified name in the DMS
    * and submits the task for asynchronous execution in the DMS. After
    * completing the task successfully it returns true. If there is a task
    * failure, then it prints error description and returns false.
    * @param taskObj task object
    * @param taskName name of the task
    * @return boolean returns true when the task is successful
    * @exception JDMException if task execution failed
    public static boolean executeTask(Task taskObj, String taskName)
    throws JDMException
    boolean isTaskSuccess = false;
    m_dmeConn.saveObject(taskName, taskObj, true);
    ExecutionHandle execHandle = m_dmeConn.execute(taskName);
    System.out.print(taskName + " is started, please wait. ");
    //Wait for completion of the task
    ExecutionStatus status = execHandle.waitForCompletion(Integer.MAX_VALUE);
    //Check the status of the task after completion
    isTaskSuccess = status.getState().equals(ExecutionState.success);
    if( isTaskSuccess ) //Task completed successfully
    System.out.println(taskName + " is successful.");
    else //Task failed
    System.out.println(taskName + " failed.\nFailure Description: " +
    status.getDescription() );
    return isTaskSuccess;
    private static void displayBuildSettings(
    ClassificationSettings clasSettings, String buildSettingsName)
    System.out.println("BuildSettings Details from the "
    + buildSettingsName + " table:");
    displayTable(buildSettingsName, "", "order by SETTING_NAME");
    System.out.println("BuildSettings Details from the "
    + buildSettingsName + " model build settings object:");
    String objName = clasSettings.getName();
    if(objName != null)
    System.out.println("Name = " + objName);
    String objDescription = clasSettings.getDescription();
    if(objDescription != null)
    System.out.println("Description = " + objDescription);
    java.util.Date creationDate = clasSettings.getCreationDate();
    String creator = clasSettings.getCreatorInfo();
    String targetAttrName = clasSettings.getTargetAttributeName();
    System.out.println("Target attribute name = " + targetAttrName);
    AlgorithmSettings algoSettings = clasSettings.getAlgorithmSettings();
    if(algoSettings == null)
    System.out.println("Failure: clasSettings.getAlgorithmSettings() returns null");
    MiningAlgorithm algo = algoSettings.getMiningAlgorithm();
    if(algo == null) System.out.println("Failure: algoSettings.getMiningAlgorithm() returns null");
    System.out.println("Algorithm Name: " + algo.name());
    MiningFunction function = clasSettings.getMiningFunction();
    if(function == null) System.out.println("Failure: clasSettings.getMiningFunction() returns null");
    System.out.println("Function Name: " + function.name());
    try {
    String costMatrixName = clasSettings.getCostMatrixName();
    if(costMatrixName != null) {
    System.out.println("Cost Matrix Details from the " + costMatrixName
    + " table:");
    displayTable(costMatrixName, "", "order by ACTUAL_TARGET_VALUE, PREDICTED_TARGET_VALUE");
    } catch(Exception jdmExp)
    System.out.println("Failure: clasSettings.getCostMatrixName()throws exception");
    jdmExp.printStackTrace();
    // List of DT algorithm settings
    // treeAlgo.setBuildHomogeneityMetric(TreeHomogeneityMetric.gini);
    // treeAlgo.setMaxDepth(7);
    // ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(0.1, SizeUnit.percentage);
    // treeAlgo.setMinNodeSize( 0.05, SizeUnit.percentage );
    // treeAlgo.setMinNodeSize( 10, SizeUnit.count );
    // ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(20, SizeUnit.count);
    TreeHomogeneityMetric homogeneityMetric = ((OraTreeSettings)algoSettings).getBuildHomogeneityMetric();
    System.out.println("Homogeneity Metric: " + homogeneityMetric.name());
    int intValue = ((OraTreeSettings)algoSettings).getMaxDepth();
    System.out.println("Max Depth: " + intValue);
    double doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSizeForSplit(SizeUnit.percentage);
    System.out.println("MinNodeSizeForSplit (percentage): " + m_df.format(doubleValue));
    doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSizeForSplit(SizeUnit.count);
    System.out.println("MinNodeSizeForSplit (count): " + m_df.format(doubleValue));
    doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize();
    SizeUnit unit = ((OraTreeSettings)algoSettings).getMinNodeSizeUnit();
    System.out.println("Min Node Size (" + unit.name() +"): " + m_df.format(doubleValue));
    doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize( SizeUnit.count );
    System.out.println("Min Node Size (" + SizeUnit.count.name() +"): " + m_df.format(doubleValue));
    doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize( SizeUnit.percentage );
    System.out.println("Min Node Size (" + SizeUnit.percentage.name() +"): " + m_df.format(doubleValue));
    * This method displayes DT model signature.
    * @param model model object
    * @exception JDMException if failed to retrieve model signature
    public static void displayModelSignature(Model model) throws JDMException
    String modelName = model.getName();
    System.out.println("Model Name: " + modelName);
    ModelSignature modelSignature = model.getSignature();
    System.out.println("ModelSignature Deatils: ( Attribute Name, Attribute Type )");
    MessageFormat mfSign = new MessageFormat(" ( {0}, {1} )");
    String[] vals = new String[3];
    Collection sortedSet = modelSignature.getAttributes();
    Iterator attrIterator = sortedSet.iterator();
    while(attrIterator.hasNext())
    SignatureAttribute attr = (SignatureAttribute)attrIterator.next();
    vals[0] = attr.getName();
    vals[1] = attr.getDataType().name();
    System.out.println( mfSign.format(vals) );
    * This method displayes DT model details.
    * @param treeModelDetails tree model details object
    * @exception JDMException if failed to retrieve model details
    public static void displayTreeModelDetailsExtensions(TreeModelDetail treeModelDetails)
    throws JDMException
    System.out.println( "\nTreeModelDetail: Model name=" + "treeModel_jdm" );
    TreeNode root = treeModelDetails.getRootNode();
    System.out.println( "\nRoot node: " + root.getIdentifier() );
    // get the info for the tree model
    int treeDepth = ((OraTreeModelDetail) treeModelDetails).getTreeDepth();
    System.out.println( "Tree depth: " + treeDepth );
    int totalNodes = ((OraTreeModelDetail) treeModelDetails).getNumberOfNodes();
    System.out.println( "Total number of nodes: " + totalNodes );
    int totalLeaves = ((OraTreeModelDetail) treeModelDetails).getNumberOfLeafNodes();
    System.out.println( "Total number of leaf nodes: " + totalLeaves );
    Stack nodeStack = new Stack();
    nodeStack.push( root);
    while( !nodeStack.empty() )
    TreeNode node = (TreeNode) nodeStack.pop();
    // display this node
    int nodeId = node.getIdentifier();
    long caseCount = node.getCaseCount();
    Object prediction = node.getPrediction();
    int level = node.getLevel();
    int children = node.getNumberOfChildren();
    TreeNode parent = node.getParent();
    System.out.println( "\nNode id=" + nodeId + " at level " + level );
    if( parent != null )
    System.out.println( "parent: " + parent.getIdentifier() +
    ", children=" + children );
    System.out.println( "Case count: " + caseCount + ", prediction: " + prediction );
    Predicate predicate = node.getPredicate();
    System.out.println( "Predicate: " + predicate.toString() );
    Predicate[] surrogates = node.getSurrogates();
    if( surrogates != null )
    for( int i=0; i<surrogates.length; i++ )
    System.out.println( "Surrogate[" + i + "]: " + surrogates[i] );
    // add child nodes in the stack
    if( children > 0 )
    TreeNode[] childNodes = node.getChildren();
    for( int i=0; i<childNodes.length; i++ )
    nodeStack.push( childNodes[i] );
    TreeNode[] allNodes = treeModelDetails.getNodes();
    System.out.print( "\nNode identifiers by getNodes():" );
    for( int i=0; i<allNodes.length; i++ )
    System.out.print( " " + allNodes.getIdentifier() );
    System.out.println();
    // display the node identifiers
    int[] nodeIds = treeModelDetails.getNodeIdentifiers();
    System.out.print( "Node identifiers by getNodeIdentifiers():" );
    for( int i=0; i<nodeIds.length; i++ )
    System.out.print( " " + nodeIds[i] );
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    System.out.println("Confusion Matrix: Error = " + m_df.format(confusionMatrix.getError()));
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    liftVals[4] = m_df.format(lift.getCumulativeLift(iQuantile).doubleValue());//CUMULATIVE_LIFT
    liftVals[5] = m_df.format(lift.getCumulativeTargetDensity(iQuantile).doubleValue());//CUMULATIVE_TARGET_DENSITY
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    java.sql.Connection dbConn =
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    try
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    try
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    finally
    try
    stmt.close();
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    try
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    try
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    Hi
    I am not sure whether this will help but someone else was getting an error with a java.sql.SQLexception: Unsupported feature. Here is a link to the fix: http://saloon.javaranch.com/cgi-bin/ubb/ultimatebb.cgi?ubb=get_topic&f=3&t=007947
    Best wishes
    Michael

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  • Java.lang.NoClassDefFoundError:Couldnot initialize class com.sap.mw.jco.JCO

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    Subhankar

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    MW_HOME/user_projects/domains/base_domain/lib
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    Regards
    Subhankar

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