List of usage examples for java.util ArrayList contains
public boolean contains(Object o)
From source file:by.stub.yaml.stubs.StubRequest.java
@VisibleForTesting boolean arraysIntersect(final ArrayList<String> dataStoreArray, final ArrayList<String> thisAssertingArray) { if (dataStoreArray.isEmpty()) { return true; } else if (!thisAssertingArray.isEmpty()) { for (final String entry : thisAssertingArray) { if (dataStoreArray.contains(entry)) { return true; }//from w w w. j av a2s . c o m } } return false; }
From source file:matrix.CreateUserList.java
public void tweetsToUserList() throws FileNotFoundException, UnsupportedEncodingException, IOException, ParseException { File fout = new File(userListPathOutput); FileOutputStream fos = new FileOutputStream(fout); BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(fos)); BufferedReader inputTW = new BufferedReader( new InputStreamReader(new FileInputStream(tweetsJsonInput), "ISO-8859-9")); ArrayList userList = new ArrayList(); JSONParser jsonParser = new JSONParser(); JSONArray jsonArray = (JSONArray) jsonParser.parse(inputTW); int sayac = 0; for (Object obj : jsonArray) { JSONObject tweet = (JSONObject) obj; JSONObject user = (JSONObject) tweet.get("user"); // String userID = user.get("id").toString(); // String userName = user.get("name").toString(); String userID = user.get("id").toString(); String userName = user.get("name").toString(); if (userList.contains(userID) == false) { userList.add(userID);//from w ww . j a va 2 s .com bw.write(userID + "," + userName); bw.newLine(); sayac++; } } System.out.println(sayac); }
From source file:com.marklogic.dom.AttrImpl.java
@Override protected int getPrefixID(int uriAtom) { int parentNodeRepID = tree.nodeParentNodeRepID[node]; if (parentNodeRepID == -1) parentNodeRepID = node;// w ww. j av a 2 s.c om ArrayList<Integer> ubp = new ArrayList<Integer>(); long sum_ordinal = tree.ordinal + tree.nodeOrdinal[parentNodeRepID]; for (int ns = getNSNodeID(sum_ordinal); ns >= 0; ns = nextNSNodeID(ns, 0)) { int uri = tree.nsNodeUriAtom[ns]; int prefix = tree.nsNodePrefixAtom[ns]; if (tree.atomString(uri) == null) { ubp.add(prefix); continue; } if (uri != uriAtom) continue; if (ubp.contains(prefix)) continue; if (tree.atomString(prefix) == null) continue; return prefix; } return -1; }
From source file:userinterface.StateNetworkAdminRole.StateReportsJPanel.java
private CategoryDataset createDataSetForPatientsReports() { ArrayList<String> cityList = new ArrayList<>(); ArrayList<Integer> yearList = new ArrayList<>(); DefaultCategoryDataset barChartData = new DefaultCategoryDataset(); for (Patient patient : patientList.getPatientDirectory()) { if (!cityList.contains(patient.getPatientLocation())) { cityList.add(patient.getPatientLocation()); }//from ww w. j av a 2 s . c o m } for (Patient patient : patientList.getPatientDirectory()) { Date reqDate = patient.getTransplantRequestDate(); Calendar cal = Calendar.getInstance(); cal.setTime(reqDate); int year = cal.get(Calendar.YEAR); if (!yearList.contains(year)) { yearList.add(year); } } for (String city : cityList) { Map<Integer, Double> yearPatientMap = new HashMap<>(); for (int reqYear : yearList) { double sum = 0; int count = 0; double avg = 0.0; for (Patient patient2 : patientList.getPatientDirectory()) { if (patient2.getPatientLocation().equals(city)) { //if patient ka add == city //if patient ka reg year = year Date compDate = patient2.getTransplantCompletionDate(); if (compDate != null) { Date reqDate = patient2.getTransplantRequestDate(); Calendar cal = Calendar.getInstance(); cal.setTime(reqDate); int year = cal.get(Calendar.YEAR); double diff = 0; if (year == reqYear) { // diff = (patient2.getTransplantCompletionDate().getTime() - patient2.getTransplantRequestDate().getTime()) / ((1000 * 60 * 60 * 24 * 30)); // sum = sum+diff; // count++; Calendar startCalendar = new GregorianCalendar(); startCalendar.setTime(reqDate); Calendar endCalendar = new GregorianCalendar(); endCalendar.setTime(patient2.getTransplantCompletionDate()); int diffyear = endCalendar.get(Calendar.YEAR) - startCalendar.get(Calendar.YEAR); int monthDiff = diffyear * 12 + endCalendar.get(Calendar.MONTH) - startCalendar.get(Calendar.MONTH); sum += monthDiff; count++; } } } } avg = sum / count; yearPatientMap.put(reqYear, avg); } //putting in data set for (Map.Entry<Integer, Double> entryset : yearPatientMap.entrySet()) { barChartData.addValue(entryset.getValue(), city, entryset.getKey()); } } return barChartData; }
From source file:edu.oregonstate.eecs.mcplan.ml.ClusterContingencyTable.java
public ClusterContingencyTable(final ArrayList<Set<RealVector>> U, final ArrayList<Set<RealVector>> V) { R = U.size();/*w ww . j a va 2 s. co m*/ C = V.size(); int N = 0; a = new int[R]; b = new int[C]; n = new int[R][C]; for (int i = 0; i < R; ++i) { final Set<RealVector> u = U.get(i); for (int j = 0; j < C; ++j) { final Set<RealVector> v = V.get(j); for (final RealVector uu : u) { if (v.contains(uu)) { a[i] += 1; b[j] += 1; n[i][j] += 1; N += 1; } } } } this.N = N; }
From source file:com.clustercontrol.agent.filecheck.FileCheck.java
/** * ?<BR>//www . j a va 2s . c o m * */ public void run() { m_log.debug("check start. directory=" + m_directory); ArrayList<JobFileCheck> kickList = new ArrayList<JobFileCheck>(); // 1. File directory = new File(m_directory); if (!directory.isDirectory()) { m_log.warn(m_directory + " is not directory"); return; } File[] files = directory.listFiles(); if (files == null) { m_log.warn(m_directory + " does not have a reference permission"); return; } ArrayList<File> fileList = new ArrayList<File>(); for (File file : files) { if (!file.isFile()) { m_log.debug(file.getName() + " is not file"); continue; } fileList.add(file); } // 2. ?? ArrayList<String> filenameList = new ArrayList<String>(); for (File file : fileList) { filenameList.add(file.getName()); } for (String filename : fileTimestampCache.keySet()) { if (!filenameList.contains(filename)) { fileTimestampCache.remove(filename); fileTimestampFlagCache.remove(filename); fileSizeCache.remove(filename); fileSizeFlagCache.remove(filename); for (JobFileCheck check : m_jobFileCheckList) { if (check.getEventType() == FileCheckConstant.TYPE_DELETE && matchFile(check, filename)) { m_log.info("kickList.add [" + filename + "] (delete)"); JobFileCheck kick = getCopy(check); kick.setFileName(filename); kickList.add(kick); } } } } // 3. ?? for (File file : fileList) { String filename = file.getName(); Long newTimestamp = file.lastModified(); Long oldTimestamp = fileTimestampCache.get(filename); if (oldTimestamp == null) { fileTimestampCache.put(filename, newTimestamp); fileTimestampFlagCache.put(filename, false); for (JobFileCheck check : m_jobFileCheckList) { if (check.getEventType() == FileCheckConstant.TYPE_CREATE && matchFile(check, filename)) { m_log.info("kickList.add [" + filename + "] (create)"); JobFileCheck kick = getCopy(check); kick.setFileName(filename); kickList.add(kick); } } } else if (!oldTimestamp.equals(newTimestamp)) { m_log.info("timestamp : " + oldTimestamp + "->" + newTimestamp + " (" + filename + ")"); fileTimestampCache.put(filename, newTimestamp); fileTimestampFlagCache.put(filename, true); } else { if (fileTimestampFlagCache.get(filename) != null && fileTimestampFlagCache.get(filename)) { // ????????? for (JobFileCheck check : m_jobFileCheckList) { if (check.getEventType() == FileCheckConstant.TYPE_MODIFY && check.getModifyType() == FileCheckConstant.TYPE_MODIFY_TIMESTAMP && matchFile(check, filename)) { m_log.info("kickList.add [" + filename + "] (timestamp)"); JobFileCheck kick = getCopy(check); kick.setFileName(filename); kickList.add(kick); } } } fileTimestampFlagCache.put(filename, false); } } // 4. ?? for (File file : fileList) { String filename = file.getName(); RandomAccessFileWrapper fr = null; try { fr = new RandomAccessFileWrapper(file, "r"); Long newSize = fr.length(); Long oldSize = fileSizeCache.get(filename); if (oldSize == null) { fileSizeCache.put(filename, newSize); fileSizeFlagCache.put(filename, false); } else if (!oldSize.equals(newSize)) { m_log.info("size : " + oldSize + "->" + newSize + " (" + filename + ")"); fileSizeCache.put(filename, newSize); fileSizeFlagCache.put(filename, true); } else { if (fileSizeFlagCache.get(filename) != null && fileSizeFlagCache.get(filename)) { // ????????? for (JobFileCheck check : m_jobFileCheckList) { if (check.getEventType() == FileCheckConstant.TYPE_MODIFY && check.getModifyType() == FileCheckConstant.TYPE_MODIFY_FILESIZE && matchFile(check, filename)) { m_log.info("kickList.add [" + filename + "] (filesize)"); JobFileCheck kick = getCopy(check); kick.setFileName(filename); kickList.add(kick); } } } fileSizeFlagCache.put(filename, false); } } catch (IOException e) { m_log.info("run() : IOException: " + e.getMessage()); } catch (Exception e) { m_log.warn("run() : IOException: " + e.getMessage()); } finally { if (fr != null) { try { fr.close(); } catch (final Exception e) { m_log.debug("run() : " + e.getMessage()); } } } } // 1?????? if (initFlag) { initFlag = false; return; } // 5. Job? for (JobFileCheck jobFileCheck : kickList) { m_log.info("kick " + jobFileCheck.getId()); String calendarId = jobFileCheck.getCalendarId(); CalendarInfo calendarInfo = jobFileCheck.getCalendarInfo(); boolean run = true; if (calendarId != null && calendarInfo == null) { m_log.info("unknown error : id=" + jobFileCheck.getId() + "calendarId=" + calendarId); } if (calendarInfo != null) { run = CalendarWSUtil.isRun(calendarInfo); } if (!run) { m_log.info("not exec(calendar) : id=" + jobFileCheck.getId() + "calendarId=" + calendarId); continue; } try { String sessionId = jobFileCheckResultRetry(jobFileCheck); String jobunitId = jobFileCheck.getJobunitId(); String jobId = jobFileCheck.getJobId(); m_log.info("jobFileCheckResult sessionId=" + sessionId + ", jobunitId=" + jobunitId + ", jobId=" + jobId); } catch (Exception e) { m_log.warn("run(jobFileCheckResult) : " + e.getClass().getSimpleName() + ", " + e.getMessage(), e); } } }
From source file:de.ks.idnadrev.expimp.xls.XlsxExporterTest.java
@Test public void testExportThoughts() throws Exception { File tempFile = File.createTempFile("thoughtExport", ".xlsx"); EntityExportSource<Thought> source = new EntityExportSource<>(getAllIds(), Thought.class); XlsxExporter exporter = new XlsxExporter(); exporter.export(tempFile, source);//from ww w .j a v a 2 s . com Workbook wb = WorkbookFactory.create(tempFile); Sheet sheet = wb.getSheetAt(0); assertEquals(Thought.class.getName(), sheet.getSheetName()); int lastRowNum = sheet.getLastRowNum(); assertEquals(COUNT, lastRowNum); Row firstRow = sheet.getRow(0); ArrayList<String> titles = new ArrayList<>(); firstRow.cellIterator().forEachRemaining(col -> titles.add(col.getStringCellValue())); assertThat(titles.size(), greaterThanOrEqualTo(3)); log.info("Found titles {}", titles); String creationTime = PropertyPath.property(Thought.class, t -> t.getCreationTime()); String name = PropertyPath.property(Thought.class, t -> t.getName()); String description = PropertyPath.property(Thought.class, t -> t.getDescription()); assertTrue(titles.contains(creationTime)); assertTrue(titles.contains(name)); assertTrue(titles.contains(description)); int nameColumn = titles.indexOf(name); ArrayList<String> names = new ArrayList<String>(COUNT); for (int i = 1; i <= COUNT; i++) { Row row = sheet.getRow(i); names.add(row.getCell(nameColumn).getStringCellValue()); } Collections.sort(names); assertEquals("Thought000", names.get(0)); assertEquals("Thought141", names.get(COUNT - 1)); Date excelDate = sheet.getRow(1).getCell(titles.indexOf(creationTime)).getDateCellValue(); Thought thought = PersistentWork.forName(Thought.class, "Thought000"); Timestamp timestamp = java.sql.Timestamp.valueOf(thought.getCreationTime()); Date creationDate = new Date(timestamp.getTime()); assertEquals(creationDate, excelDate); }
From source file:com.mythesis.userbehaviouranalysis.ProfileAnalysis.java
/** * a method that returns a number of random queries * @param path SWebRank output directory * @param numOfQueries number of random queries * @return a list of paths for the queries *//*from www . j a v a2 s .c om*/ private ArrayList<String> getQueries(String path, int numOfQueries) { //Find output paths File root = new File(path); File[] contents = root.listFiles(); List<String> sWebRanklevels = new ArrayList<>(); for (File f : contents) { if (f.getAbsolutePath().contains("level")) sWebRanklevels.add(f.getAbsolutePath()); } //Find all query paths ArrayList<String> queries = new ArrayList<>(); for (String s : sWebRanklevels) { File level = new File(s); File[] queriesFiles = level.listFiles(); for (File f : queriesFiles) { if (!f.getAbsolutePath().contains("txt")) queries.add(f.getAbsolutePath()); } } if (numOfQueries > queries.size()) { return queries; } //Select a number of random queries int totalQueries = queries.size() - 1; Random randomQuery = new Random(); ArrayList<String> randomQueries = new ArrayList<>(); int count = 0; while (count < numOfQueries) { String val = queries.get(randomQuery.nextInt(totalQueries)); if (!randomQueries.contains(val)) { randomQueries.add(val); count++; } } return randomQueries; }
From source file:com.timtripcony.AbstractDocumentMapModel.java
/** * Loads the object with settings based on the passed UNID: * <ul>// w ww . j a v a 2 s . co m * <ol> * Whether or not it is a new Document * </ol> * <ol> * Whether or not the Document is deleted * </ol> * <ol> * Whether or not the Document is read only * </ol> * <ol> * Sets wrappedDoc as a DominoDocument object * </ol> * <ol> * Loads all fields into the object, omitting named fields, anything prefixed with "$", or with type "Error" * </ol> * </ul> * * Extended by PW to allow specific fields to be ignored and to ensure empty fields are loaded as "" (empty String), * not "[]" (String version of empty Vector) * * @param unid * String UNID or Note ID relating to this Document (empty = new Document) */ public void load(final String unid) { setUnid(unid); Document doc = null; setNewNote(true); // Default to true setDeleted(false); // Default to false setReadOnly(false); // Default to false try { if (StringUtil.isNotEmpty(unid)) { try { doc = AppUtils.getDocumentByNoteID_Or_UNID(unid); setWrappedDoc(DominoDocument.wrap(AppUtils.getDataDbPath(), // database name doc, // Document null, // computeWithForm null, // concurrency Mode false, // allowDeletedDocs null, // saveLinksAs null)); // webQuerySaveAgent for (Object eachItem : doc.getItems()) { if (eachItem instanceof Item) { Item item = (Item) eachItem; String itemName = item.getName(); // Certainly not a comprehensive list of items to skip ArrayList<String> ignoreList = ArrayListUtil.stringToArrayList("MIME_Version"); String firstChar = StringUtils.left(itemName, 1); if (!ignoreList.contains(itemName) && !StringUtils.equals(firstChar, "$")) { // Item may be of type "Error" if (item.getType() != Type.ERRORITEM.getValue()) { Object itemValue = wrappedDoc.getValue(itemName); setValue(itemName, itemValue); if (itemValue instanceof Vector) { if ("[]".equals(itemValue.toString())) { setValue(itemName, new String("")); } } } } } } if (doc.isDeleted() || !doc.isValid()) { setDeleted(true); } setNewNote(false); } catch (Throwable t) { AppUtils.handleException(t); } } } catch (Throwable t) { AppUtils.handleException(t); } }
From source file:de.tudarmstadt.tk.statistics.importer.ExternalResultsReader.java
public static SampleData interpretCSV(StatsConfig config, List<String[]> rows, ReportTypes pipelineType, HashMap<String, Integer> pipelineMetadata) { HashMap<Integer, ArrayList<ArrayList<Double>>> samplesPerMeasure = new HashMap<Integer, ArrayList<ArrayList<Double>>>(); //Only remove first line if it is a header line if (rows.size() > 0 && rows.get(0)[6].equals("IsBaseline")) { rows.remove(0);//ww w.j ava 2 s . c o m } if (rows.size() > 1) { logger.log(Level.INFO, "Extracting samples and metadata from imported data."); int selectBestN = config.getSelectBestN(); String selectByMeasure = config.getSelectByMeasure(); // Preprocessing: Parse different models (classifier + feature set column) and measures ArrayList<String> measures = new ArrayList<String>(); ArrayList<Pair<String, String>> datasets = new ArrayList<Pair<String, String>>(); ArrayList<Pair<String, String>> models = new ArrayList<Pair<String, String>>(); ArrayList<Pair<String, String>> baselineModels = new ArrayList<Pair<String, String>>(); for (int i = 0; i < rows.size(); i++) { String[] columns = rows.get(i); String classifier = columns[2]; if (classifier.equals("0")) { classifier = "Aggregated"; } String featureSets = columns[3]; Pair<String, String> model = Pair.of(classifier, featureSets); if (!models.contains(model)) { models.add(model); if (!baselineModels.contains(model) && Integer.parseInt(columns[6]) == 1) { baselineModels.add(model); } } if (!measures.contains(columns[4])) { measures.add(columns[4]); } } //Check: Baseline only allowed when > 2 models are evaluated if (models.size() <= 2 && baselineModels.size() > 0) { logger.log(Level.WARN, "At least three models are required to make an evaluation against a baseline meaningful. In the dataset, a baseline was specified for only two models. The baseline indicator will be ignored."); System.err.println( "At least three models are required to make an evaluation against a baseline meaningful. In the dataset, a baseline was specified for only two models. The baseline indicator will be ignored."); baselineModels.clear(); } // Now sort samples according to data Collections.sort(rows, new Helpers.LexicographicArrayComparator()); for (int i = 0; i < rows.size(); i++) { String[] columns = rows.get(i); Pair<String, String> data = null; String trainData = columns[0].trim(); String testData = columns[1].trim(); //If this is a CV, numbers after a dot indicate fold UUIDS, they thus have to be splitted to retain the original dataset name if (pipelineType == ReportTypes.CV) { trainData = trainData.split("\\.")[0]; testData = testData.split("\\.")[0]; } if (trainData.equals(testData)) { data = Pair.of(trainData, null); } else { //columns[1] = columns[1].split(".")[0]; data = Pair.of(trainData, testData); } if (!datasets.contains(data)) { datasets.add(data); } } // Preprocessing: Initialize sample container per measure/model for (int i = 0; i < measures.size(); i++) { ArrayList<ArrayList<Double>> samplesPerModel = new ArrayList<ArrayList<Double>>(); for (int j = 0; j < models.size(); j++) { samplesPerModel.add(new ArrayList<Double>()); } samplesPerMeasure.put(i, samplesPerModel); } // Assign samples to different models for (int i = 0; i < rows.size(); i++) { String[] columns = rows.get(i); String classifier = columns[2]; if (classifier.equals("0")) { classifier = "Aggregated"; } String featureSet = columns[3]; String measure = columns[4]; double value = Double.parseDouble(columns[5]); int measureIndex = measures.indexOf(measure); int modelIndex = models.indexOf(Pair.of(classifier, featureSet)); ArrayList<ArrayList<Double>> sPMeasure = samplesPerMeasure.get(measureIndex); sPMeasure.get(modelIndex).add(value); } // Transform into data format required by the statistical evaluation HashMap<String, ArrayList<ArrayList<Double>>> indexedSamples = new HashMap<String, ArrayList<ArrayList<Double>>>(); HashMap<String, ArrayList<Double>> indexedSamplesAverage = new HashMap<String, ArrayList<Double>>(); Iterator<Integer> it = samplesPerMeasure.keySet().iterator(); while (it.hasNext()) { int measureIndex = it.next(); ArrayList<ArrayList<Double>> samplesPerModel = samplesPerMeasure.get(measureIndex); ArrayList<Double> sampleAverages = new ArrayList<Double>(models.size()); for (int modelIndex = 0; modelIndex < models.size(); modelIndex++) { ArrayList<Double> sample = samplesPerModel.get(modelIndex); double average = 0; for (int j = 0; j < sample.size(); j++) { average += sample.get(j); } average /= sample.size(); sampleAverages.add(average); } indexedSamplesAverage.put(measures.get(measureIndex), sampleAverages); indexedSamples.put(measures.get(measureIndex), samplesPerMeasure.get(measureIndex)); } // Check if data fulfills general requirements: > 5 samples for each model, same number of samples per model it = samplesPerMeasure.keySet().iterator(); while (it.hasNext()) { Integer measureIndex = it.next(); ArrayList<ArrayList<Double>> samplesPerModel = samplesPerMeasure.get(measureIndex); int s = samplesPerModel.get(0).size(); for (int i = 1; i < samplesPerModel.size(); i++) { if (samplesPerModel.get(i).size() < 5) { logger.log(Level.ERROR, "More than 5 samples are needed per model and measure. Aborting."); System.err.println("More than 5 samples are needed per model and measure. Aborting."); System.exit(1); } if (samplesPerModel.get(i).size() != s) { logger.log(Level.ERROR, "Different models are not represented by the same number of samples. Aborting."); System.err.println( "Different models are not represented by the same number of samples. Aborting."); System.exit(1); } } } // Collect remaining data required for creating a SampleData object // Check if data fulfills requirements of the specific PipelineTypes int nFolds = 1; int nRepetitions = 1; switch (pipelineType) { case CV: if (datasets.size() > 1) { System.err.println( "Input data corrupted. More than one dataset specified for Single-Domain Cross-Validation."); logger.log(Level.ERROR, "Input data corrupted. More than one dataset specified for Single-Domain Cross-Validation."); return null; } else if (datasets.get(0).getValue() != null) { System.err.println( "Input data corrupted. Training and Test dataset must be same for Cross-Validation."); logger.log(Level.ERROR, "Input data corrupted. Training and Test dataset must be same for Cross-Validation."); return null; } nFolds = indexedSamples.get(measures.get(0)).get(0).size(); nRepetitions = 1; break; case MULTIPLE_CV: if (datasets.size() > 1) { System.err.println( "Input data corrupted. More than one dataset specified for Single-Domain Cross-Validation."); logger.log(Level.ERROR, "Input data corrupted. More than one dataset specified for Single-Domain Cross-Validation."); return null; } else if (datasets.get(0).getValue() != null) { System.err.println( "Input data corrupted. Training and Test dataset must be same for Cross-Validation."); logger.log(Level.ERROR, "Input data corrupted. Training and Test dataset must be same for Cross-Validation."); return null; } nFolds = pipelineMetadata.get("nFolds"); nRepetitions = indexedSamples.get(measures.get(0)).get(0).size(); break; case CV_DATASET_LVL: nFolds = pipelineMetadata.get("nFolds"); nRepetitions = 1; break; case MULTIPLE_CV_DATASET_LVL: nFolds = pipelineMetadata.get("nFolds"); nRepetitions = pipelineMetadata.get("nRepetitions"); break; case TRAIN_TEST_DATASET_LVL: nFolds = 1; nRepetitions = 1; break; default: System.err.println("Unknown PipelineType. Aborting."); logger.log(Level.ERROR, "Unknown PipelineType. Aborting."); return null; } //Reorder data in case of a baseline evaluation (baseline first) if (baselineModels.size() == 1) { Pair<String, String> baselineModel = baselineModels.get(0); int modelIndex = models.indexOf(baselineModel); models.remove(modelIndex); models.add(0, baselineModel); for (String measure : indexedSamples.keySet()) { ArrayList<Double> s = indexedSamples.get(measure).get(modelIndex); indexedSamples.get(measure).remove(modelIndex); indexedSamples.get(measure).add(0, s); double a = indexedSamplesAverage.get(measure).get(modelIndex); indexedSamplesAverage.get(measure).remove(modelIndex); indexedSamplesAverage.get(measure).add(0, a); } } SampleData sampleData = new SampleData(null, indexedSamples, indexedSamplesAverage, datasets, models, baselineModels, pipelineType, nFolds, nRepetitions); sampleData = Helpers.truncateData(sampleData, selectBestN, selectByMeasure); return sampleData; } return null; }