List of usage examples for weka.experiment RegressionSplitEvaluator RegressionSplitEvaluator
public RegressionSplitEvaluator()
From source file:ExperimentDemo.java
License:Open Source License
/** * Expects the following parameters: /*ww w . j a v a2 s. com*/ * <ul> * <li>-classifier "classifier incl. parameters"</li> * <li>-exptype "classification|regression"</li> * <li>-splittype "crossvalidation|randomsplit"</li> * <li>-runs "# of runs"</li> * <li>-folds "# of cross-validation folds"</li> * <li>-percentage "percentage for randomsplit"</li> * <li>-result "arff file for storing the results"</li> * <li>-t "dataset" (can be supplied multiple times)</li> * </ul> * * @param args the commandline arguments * @throws Exception if something goes wrong */ public static void main(String[] args) throws Exception { // parameters provided? if (args.length == 0) { System.out.println("\nUsage: weka.examples.experiment.ExperimentDemo\n" + "\t -classifier <classifier incl. parameters>\n" + "\t -exptype <classification|regression>\n" + "\t -splittype <crossvalidation|randomsplit>\n" + "\t -runs <# of runs>\n" + "\t -folds <folds for CV>\n" + "\t -percentage <percentage for randomsplit>\n" + "\t -result <ARFF file for storing the results>\n" + "\t -t dataset (can be supplied multiple times)\n"); System.exit(1); } // 1. setup the experiment System.out.println("Setting up..."); Experiment exp = new Experiment(); exp.setPropertyArray(new Classifier[0]); exp.setUsePropertyIterator(true); String option; // classification or regression option = Utils.getOption("exptype", args); if (option.length() == 0) throw new IllegalArgumentException("No experiment type provided!"); SplitEvaluator se = null; /* * Interface to objects able to generate a fixed set of results for a particular split of a dataset. * The set of results should contain fields related to any settings of the SplitEvaluator (not including the dataset name. * For example, one field for the classifier used to get the results, another for the classifier options, etc). * Possible implementations of SplitEvaluator: StdClassification results, StdRegression results. */ Classifier sec = null; boolean classification = false; if (option.equals("classification")) { classification = true; se = new ClassifierSplitEvaluator(); /* * A SplitEvaluator that produces results for a classification scheme on a nominal class attribute. */ sec = ((ClassifierSplitEvaluator) se).getClassifier(); } else if (option.equals("regression")) { se = new RegressionSplitEvaluator(); sec = ((RegressionSplitEvaluator) se).getClassifier(); } else { throw new IllegalArgumentException("Unknown experiment type '" + option + "'!"); } // crossvalidation or randomsplit option = Utils.getOption("splittype", args); if (option.length() == 0) throw new IllegalArgumentException("No split type provided!"); if (option.equals("crossvalidation")) { CrossValidationResultProducer cvrp = new CrossValidationResultProducer(); /* * Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results. * If the class attribute is nominal, the dataset is stratified. Results for each fold are generated, so you may wish to use * this in addition with an AveragingResultProducer to obtain averages for each run. */ option = Utils.getOption("folds", args); if (option.length() == 0) throw new IllegalArgumentException("No folds provided!"); cvrp.setNumFolds(Integer.parseInt(option)); cvrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; /* * Stores information on a property of an object: the class of the object with the property; * the property descriptor, and the current value. */ try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class), CrossValidationResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(cvrp); exp.setPropertyPath(propertyPath); } else if (option.equals("randomsplit")) { RandomSplitResultProducer rsrp = new RandomSplitResultProducer(); rsrp.setRandomizeData(true); option = Utils.getOption("percentage", args); if (option.length() == 0) throw new IllegalArgumentException("No percentage provided!"); rsrp.setTrainPercent(Double.parseDouble(option)); rsrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class), RandomSplitResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(rsrp); exp.setPropertyPath(propertyPath); } else { throw new IllegalArgumentException("Unknown split type '" + option + "'!"); } // runs option = Utils.getOption("runs", args); if (option.length() == 0) throw new IllegalArgumentException("No runs provided!"); exp.setRunLower(1); exp.setRunUpper(Integer.parseInt(option)); // classifier option = Utils.getOption("classifier", args); if (option.length() == 0) throw new IllegalArgumentException("No classifier provided!"); String[] options = Utils.splitOptions(option); String classname = options[0]; options[0] = ""; Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options); exp.setPropertyArray(new Classifier[] { c }); // datasets boolean data = false; DefaultListModel model = new DefaultListModel(); do { option = Utils.getOption("t", args); if (option.length() > 0) { File file = new File(option); if (!file.exists()) throw new IllegalArgumentException("File '" + option + "' does not exist!"); data = true; model.addElement(file); } } while (option.length() > 0); if (!data) throw new IllegalArgumentException("No data files provided!"); exp.setDatasets(model); // result option = Utils.getOption("result", args); if (option.length() == 0) throw new IllegalArgumentException("No result file provided!"); InstancesResultListener irl = new InstancesResultListener(); irl.setOutputFile(new File(option)); exp.setResultListener(irl); // 2. run experiment System.out.println("Initializing..."); exp.initialize(); System.out.println("Running..."); exp.runExperiment(); System.out.println("Finishing..."); exp.postProcess(); // 3. calculate statistics and output them System.out.println("Evaluating..."); PairedTTester tester = new PairedCorrectedTTester(); /* * Calculates T-Test statistics on data stored in a set of instances. */ Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile()))); tester.setInstances(result); tester.setSortColumn(-1); tester.setRunColumn(result.attribute("Key_Run").index()); if (classification) tester.setFoldColumn(result.attribute("Key_Fold").index()); tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1))); tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + "," + (result.attribute("Key_Scheme_options").index() + 1) + "," + (result.attribute("Key_Scheme_version_ID").index() + 1))); tester.setResultMatrix(new ResultMatrixPlainText()); tester.setDisplayedResultsets(null); tester.setSignificanceLevel(0.05); tester.setShowStdDevs(true); // fill result matrix (but discarding the output) if (classification) tester.multiResultsetFull(0, result.attribute("Percent_correct").index()); else tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index()); // output results for reach dataset System.out.println("\nResult:"); ResultMatrix matrix = tester.getResultMatrix(); for (int i = 0; i < matrix.getColCount(); i++) { System.out.println(matrix.getColName(i)); System.out.println(" Perc. correct: " + matrix.getMean(i, 0)); System.out.println(" StdDev: " + matrix.getStdDev(i, 0)); } }
From source file:adams.flow.sink.WekaExperimentGenerator.java
License:Open Source License
/** * Executes the flow item./* ww w . j a v a 2s.co m*/ * * @return null if everything is fine, otherwise error message */ @Override protected String doExecute() { String result; Experiment exp; SplitEvaluator se; Classifier sec; CrossValidationResultProducer cvrp; RandomSplitResultProducer rsrp; PropertyNode[] propertyPath; DefaultListModel model; InstancesResultListener irl; CSVResultListener crl; result = null; if (m_ResultFile.isDirectory()) result = "Result file points to a directory: " + m_ResultFile; else if (m_OutputFile.isDirectory()) result = "Output file points to a directory: " + m_OutputFile; if (result == null) { exp = new Experiment(); exp.setPropertyArray(new Classifier[0]); exp.setUsePropertyIterator(true); // classification or regression se = null; sec = null; if (m_ExperimentType == ExperimentType.CLASSIFICATION) { se = new ClassifierSplitEvaluator(); sec = ((ClassifierSplitEvaluator) se).getClassifier(); } else if (m_ExperimentType == ExperimentType.REGRESSION) { se = new RegressionSplitEvaluator(); sec = ((RegressionSplitEvaluator) se).getClassifier(); } else { throw new IllegalStateException("Unhandled experiment type: " + m_ExperimentType); } // crossvalidation or train/test split if (m_EvaluationType == EvaluationType.CROSS_VALIDATION) { cvrp = new CrossValidationResultProducer(); cvrp.setNumFolds(m_Folds); cvrp.setSplitEvaluator(se); propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class), CrossValidationResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(cvrp); exp.setPropertyPath(propertyPath); } else if ((m_EvaluationType == EvaluationType.TRAIN_TEST_SPLIT_RANDOMIZED) || (m_EvaluationType == EvaluationType.TRAIN_TEST_SPLIT_ORDER_PRESERVED)) { rsrp = new RandomSplitResultProducer(); rsrp.setRandomizeData(m_EvaluationType == EvaluationType.TRAIN_TEST_SPLIT_RANDOMIZED); rsrp.setTrainPercent(m_SplitPercentage); rsrp.setSplitEvaluator(se); propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class), RandomSplitResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(rsrp); exp.setPropertyPath(propertyPath); } else { throw new IllegalStateException("Unhandled evaluation type: " + m_EvaluationType); } // runs exp.setRunLower(1); exp.setRunUpper(m_Runs); // classifier exp.setPropertyArray((Classifier[]) m_InputToken.getPayload()); // datasets (empty for the template) model = new DefaultListModel(); exp.setDatasets(model); // result if (m_ResultFormat == ResultFormat.ARFF) { irl = new InstancesResultListener(); irl.setOutputFile(new File(m_ResultFile.getAbsolutePath())); exp.setResultListener(irl); } else if (m_ResultFormat == ResultFormat.CSV) { crl = new CSVResultListener(); crl.setOutputFile(new File(m_ResultFile.getAbsolutePath())); exp.setResultListener(crl); } else { throw new IllegalStateException("Unhandled result format: " + m_ResultFormat); } // save template try { Experiment.write(m_OutputFile.getAbsolutePath(), exp); } catch (Exception e) { result = handleException("Failed to save experiment to '" + m_OutputFile + "': ", e); } } return result; }
From source file:com.emar.recsys.user.model.WekaExperiment.java
License:Open Source License
/** * Expects the following parameters://from ww w. j av a2 s . c o m * <ul> * <li>-classifier "classifier incl. parameters"</li> * <li>-exptype "classification|regression"</li> * <li>-splittype "crossvalidation|randomsplit"</li> * <li>-runs "# of runs"</li> * <li>-folds "# of cross-validation folds"</li> * <li>-percentage "percentage for randomsplit"</li> * <li>-result "arff file for storing the results"</li> * <li>-t "dataset" (can be supplied multiple times)</li> * </ul> * * @param args * the commandline arguments * @throws Exception * if something goes wrong */ public static void main(String[] args) throws Exception { // parameters provided? if (args.length == 0) { System.out.println("\nUsage: ExperimentDemo\n" + "\t -classifier <classifier incl. parameters>\n" + "\t -exptype <classification|regression>\n" + "\t -splittype <crossvalidation|randomsplit>\n" + "\t -runs <# of runs>\n" + "\t -folds <folds for CV>\n" + "\t -percentage <percentage for randomsplit>\n" + "\t -result <ARFF file for storing the results>\n" + "\t -t dataset (can be supplied multiple times)\n"); System.exit(1); } // 1. setup the experiment System.out.println("Setting up..."); Experiment exp = new Experiment(); exp.setPropertyArray(new Classifier[0]); exp.setUsePropertyIterator(true); String option; // classification or regression option = Utils.getOption("exptype", args); if (option.length() == 0) throw new IllegalArgumentException("No experiment type provided!"); SplitEvaluator se = null; Classifier sec = null; boolean classification = false; if (option.equals("classification")) { classification = true; se = new ClassifierSplitEvaluator(); sec = ((ClassifierSplitEvaluator) se).getClassifier(); } else if (option.equals("regression")) { se = new RegressionSplitEvaluator(); sec = ((RegressionSplitEvaluator) se).getClassifier(); } else { throw new IllegalArgumentException("Unknown experiment type '" + option + "'!"); } // crossvalidation or randomsplit option = Utils.getOption("splittype", args); if (option.length() == 0) throw new IllegalArgumentException("No split type provided!"); if (option.equals("crossvalidation")) { CrossValidationResultProducer cvrp = new CrossValidationResultProducer(); option = Utils.getOption("folds", args); if (option.length() == 0) throw new IllegalArgumentException("No folds provided!"); cvrp.setNumFolds(Integer.parseInt(option)); cvrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class), CrossValidationResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(cvrp); exp.setPropertyPath(propertyPath); } else if (option.equals("randomsplit")) { RandomSplitResultProducer rsrp = new RandomSplitResultProducer(); rsrp.setRandomizeData(true); option = Utils.getOption("percentage", args); if (option.length() == 0) throw new IllegalArgumentException("No percentage provided!"); rsrp.setTrainPercent(Double.parseDouble(option)); rsrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class), RandomSplitResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(rsrp); exp.setPropertyPath(propertyPath); } else { throw new IllegalArgumentException("Unknown split type '" + option + "'!"); } // runs option = Utils.getOption("runs", args); if (option.length() == 0) throw new IllegalArgumentException("No runs provided!"); exp.setRunLower(1); exp.setRunUpper(Integer.parseInt(option)); // classifier option = Utils.getOption("classifier", args); if (option.length() == 0) throw new IllegalArgumentException("No classifier provided!"); String[] options = Utils.splitOptions(option); String classname = options[0]; options[0] = ""; Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options); exp.setPropertyArray(new Classifier[] { c }); // datasets boolean data = false; DefaultListModel model = new DefaultListModel(); do { option = Utils.getOption("t", args); if (option.length() > 0) { File file = new File(option); if (!file.exists()) throw new IllegalArgumentException("File '" + option + "' does not exist!"); data = true; model.addElement(file); } } while (option.length() > 0); if (!data) throw new IllegalArgumentException("No data files provided!"); exp.setDatasets(model); // result option = Utils.getOption("result", args); if (option.length() == 0) throw new IllegalArgumentException("No result file provided!"); InstancesResultListener irl = new InstancesResultListener(); irl.setOutputFile(new File(option)); exp.setResultListener(irl); // 2. run experiment System.out.println("Initializing..."); exp.initialize(); System.out.println("Running..."); exp.runExperiment(); System.out.println("Finishing..."); exp.postProcess(); // 3. calculate statistics and output them System.out.println("Evaluating..."); PairedTTester tester = new PairedCorrectedTTester(); Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile()))); tester.setInstances(result); tester.setSortColumn(-1); tester.setRunColumn(result.attribute("Key_Run").index()); if (classification) tester.setFoldColumn(result.attribute("Key_Fold").index()); tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1))); tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + "," + (result.attribute("Key_Scheme_options").index() + 1) + "," + (result.attribute("Key_Scheme_version_ID").index() + 1))); tester.setResultMatrix(new ResultMatrixPlainText()); tester.setDisplayedResultsets(null); tester.setSignificanceLevel(0.05); tester.setShowStdDevs(true); // fill result matrix (but discarding the output) if (classification) tester.multiResultsetFull(0, result.attribute("Percent_correct").index()); else tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index()); // output results for reach dataset System.out.println("\nResult:"); ResultMatrix matrix = tester.getResultMatrix(); for (int i = 0; i < matrix.getColCount(); i++) { System.out.println(matrix.getColName(i)); System.out.println(" Perc. correct: " + matrix.getMean(i, 0)); System.out.println(" StdDev: " + matrix.getStdDev(i, 0)); } }
From source file:examples.ExperimentDemo.java
License:Open Source License
/** * Expects the following parameters://from w w w . j a v a 2 s . co m * <ul> * <li>-classifier "classifier incl. parameters"</li> * <li>-exptype "classification|regression"</li> * <li>-splittype "crossvalidation|randomsplit"</li> * <li>-runs "# of runs"</li> * <li>-folds "# of cross-validation folds"</li> * <li>-percentage "percentage for randomsplit"</li> * <li>-result "arff file for storing the results"</li> * <li>-t "dataset" (can be supplied multiple times)</li> * </ul> * * @param args * the commandline arguments * @throws Exception * if something goes wrong */ // ref: http://weka.wikispaces.com/Using+the+Experiment+API public static void main(String[] args) throws Exception { // @xr: my modification of args, output to download folder // @xr: direct args not working, has to put paras in run-configuration-paras // String[] args = { // "weka.classifiers.trees.J48", // "classification", // "crossvalidation", // "10", // "10", // "/Users/renxin/Downloads/output.arff", // "vote.arff", // "iris.arff" }; // String[] args = { // "-classifier weka.classifiers.trees.J48", // "-exptype classification", // "-splittype crossvalidation", // "-runs 10", // "-folds 10", // "-result /some/where/results.arff", // "-t vote.arff", // "-t iris.arff" // }; // parameters provided? if (args.length == 0) { System.out.println("\nUsage: ExperimentDemo\n" + "\t -classifier <classifier incl. parameters>\n" + "\t -exptype <classification|regression>\n" + "\t -splittype <crossvalidation|randomsplit>\n" + "\t -runs <# of runs>\n" + "\t -folds <folds for CV>\n" + "\t -percentage <percentage for randomsplit>\n" + "\t -result <ARFF file for storing the results>\n" + "\t -t dataset (can be supplied multiple times)\n"); System.exit(1); } // 1. setup the experiment System.out.println("Setting up..."); Experiment exp = new Experiment(); exp.setPropertyArray(new Classifier[0]); exp.setUsePropertyIterator(true); String option; // classification or regression option = Utils.getOption("exptype", args); if (option.length() == 0) throw new IllegalArgumentException("No experiment type provided!"); SplitEvaluator se = null; Classifier sec = null; boolean classification = false; if (option.equals("classification")) { classification = true; se = new ClassifierSplitEvaluator(); sec = ((ClassifierSplitEvaluator) se).getClassifier(); } else if (option.equals("regression")) { se = new RegressionSplitEvaluator(); sec = ((RegressionSplitEvaluator) se).getClassifier(); } else { throw new IllegalArgumentException("Unknown experiment type '" + option + "'!"); } // crossvalidation or randomsplit option = Utils.getOption("splittype", args); if (option.length() == 0) throw new IllegalArgumentException("No split type provided!"); if (option.equals("crossvalidation")) { CrossValidationResultProducer cvrp = new CrossValidationResultProducer(); option = Utils.getOption("folds", args); if (option.length() == 0) throw new IllegalArgumentException("No folds provided!"); cvrp.setNumFolds(Integer.parseInt(option)); cvrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", CrossValidationResultProducer.class), CrossValidationResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(cvrp); exp.setPropertyPath(propertyPath); } else if (option.equals("randomsplit")) { RandomSplitResultProducer rsrp = new RandomSplitResultProducer(); rsrp.setRandomizeData(true); option = Utils.getOption("percentage", args); if (option.length() == 0) throw new IllegalArgumentException("No percentage provided!"); rsrp.setTrainPercent(Double.parseDouble(option)); rsrp.setSplitEvaluator(se); PropertyNode[] propertyPath = new PropertyNode[2]; try { propertyPath[0] = new PropertyNode(se, new PropertyDescriptor("splitEvaluator", RandomSplitResultProducer.class), RandomSplitResultProducer.class); propertyPath[1] = new PropertyNode(sec, new PropertyDescriptor("classifier", se.getClass()), se.getClass()); } catch (IntrospectionException e) { e.printStackTrace(); } exp.setResultProducer(rsrp); exp.setPropertyPath(propertyPath); } else { throw new IllegalArgumentException("Unknown split type '" + option + "'!"); } // runs option = Utils.getOption("runs", args); if (option.length() == 0) throw new IllegalArgumentException("No runs provided!"); exp.setRunLower(1); exp.setRunUpper(Integer.parseInt(option)); // classifier option = Utils.getOption("classifier", args); if (option.length() == 0) throw new IllegalArgumentException("No classifier provided!"); String[] options = Utils.splitOptions(option); String classname = options[0]; options[0] = ""; Classifier c = (Classifier) Utils.forName(Classifier.class, classname, options); exp.setPropertyArray(new Classifier[] { c }); // datasets boolean data = false; DefaultListModel model = new DefaultListModel(); do { option = Utils.getOption("t", args); if (option.length() > 0) { File file = new File(option); if (!file.exists()) throw new IllegalArgumentException("File '" + option + "' does not exist!"); data = true; model.addElement(file); } } while (option.length() > 0); if (!data) throw new IllegalArgumentException("No data files provided!"); exp.setDatasets(model); // result option = Utils.getOption("result", args); if (option.length() == 0) throw new IllegalArgumentException("No result file provided!"); InstancesResultListener irl = new InstancesResultListener(); irl.setOutputFile(new File(option)); exp.setResultListener(irl); // 2. run experiment System.out.println("Initializing..."); exp.initialize(); System.out.println("Running..."); exp.runExperiment(); System.out.println("Finishing..."); exp.postProcess(); // 3. calculate statistics and output them System.out.println("Evaluating..."); PairedTTester tester = new PairedCorrectedTTester(); Instances result = new Instances(new BufferedReader(new FileReader(irl.getOutputFile()))); tester.setInstances(result); tester.setSortColumn(-1); tester.setRunColumn(result.attribute("Key_Run").index()); if (classification) tester.setFoldColumn(result.attribute("Key_Fold").index()); tester.setResultsetKeyColumns(new Range("" + (result.attribute("Key_Dataset").index() + 1))); tester.setDatasetKeyColumns(new Range("" + (result.attribute("Key_Scheme").index() + 1) + "," + (result.attribute("Key_Scheme_options").index() + 1) + "," + (result.attribute("Key_Scheme_version_ID").index() + 1))); tester.setResultMatrix(new ResultMatrixPlainText()); tester.setDisplayedResultsets(null); tester.setSignificanceLevel(0.05); tester.setShowStdDevs(true); // fill result matrix (but discarding the output) if (classification) tester.multiResultsetFull(0, result.attribute("Percent_correct").index()); else tester.multiResultsetFull(0, result.attribute("Correlation_coefficient").index()); // output results for reach dataset System.out.println("\nResult:"); ResultMatrix matrix = tester.getResultMatrix(); for (int i = 0; i < matrix.getColCount(); i++) { System.out.println(matrix.getColName(i)); System.out.println(" Perc. correct: " + matrix.getMean(i, 0)); System.out.println(" StdDev: " + matrix.getStdDev(i, 0)); } }