List of usage examples for com.google.common.collect HashBasedTable create
public static <R, C, V> HashBasedTable<R, C, V> create()
From source file:edu.mit.streamjit.impl.compiler2.Compiler2.java
public Compiler2(Set<Worker<?, ?>> workers, Configuration config, int maxNumCores, DrainData initialState, Input<?> input, Output<?> output) { this.workers = ImmutableSet.copyOf(workers); Map<Class<?>, ActorArchetype> archetypesBuilder = new HashMap<>(); Map<Worker<?, ?>, WorkerActor> workerActors = new HashMap<>(); for (Worker<?, ?> w : workers) { @SuppressWarnings("unchecked") Class<? extends Worker<?, ?>> wClass = (Class<? extends Worker<?, ?>>) w.getClass(); if (archetypesBuilder.get(wClass) == null) archetypesBuilder.put(wClass, new ActorArchetype(wClass, module)); WorkerActor actor = new WorkerActor(w, archetypesBuilder.get(wClass)); workerActors.put(w, actor);//from www .ja v a 2s. com } this.archetypes = ImmutableSet.copyOf(archetypesBuilder.values()); Map<Token, TokenActor> tokenActors = new HashMap<>(); Table<Actor, Actor, Storage> storageTable = HashBasedTable.create(); int[] inputTokenId = new int[] { Integer.MIN_VALUE }, outputTokenId = new int[] { Integer.MAX_VALUE }; for (WorkerActor a : workerActors.values()) a.connect(ImmutableMap.copyOf(workerActors), tokenActors, storageTable, inputTokenId, outputTokenId); this.actors = new TreeSet<>(); this.actors.addAll(workerActors.values()); this.actors.addAll(tokenActors.values()); this.storage = new HashSet<>(storageTable.values()); this.config = config; this.maxNumCores = maxNumCores; this.initialState = initialState; ImmutableMap.Builder<Token, ImmutableList<Object>> initialStateDataMapBuilder = ImmutableMap.builder(); if (initialState != null) { for (Table.Cell<Actor, Actor, Storage> cell : storageTable.cellSet()) { Token tok; if (cell.getRowKey() instanceof TokenActor) tok = ((TokenActor) cell.getRowKey()).token(); else if (cell.getColumnKey() instanceof TokenActor) tok = ((TokenActor) cell.getColumnKey()).token(); else tok = new Token(((WorkerActor) cell.getRowKey()).worker(), ((WorkerActor) cell.getColumnKey()).worker()); ImmutableList<Object> data = initialState.getData(tok); if (data != null && !data.isEmpty()) { initialStateDataMapBuilder.put(tok, data); cell.getValue().initialData().add(Pair.make(data, IndexFunction.identity())); } } } this.initialStateDataMap = initialStateDataMapBuilder.build(); this.overallInput = input; this.overallOutput = output; }
From source file:co.turnus.profiling.impl.ProfilingWeightsImpl.java
@Override public Table<Actor, Action, ActionProfilingWeights> asTable() { Table<Actor, Action, ActionProfilingWeights> table = HashBasedTable.create(); for (ActorProfilingWeights actorWeights : getActorsWeights()) { for (ActionProfilingWeights actionWeights : actorWeights.getActionsWeights()) { table.put(actorWeights.getActor(), actionWeights.getAction(), actionWeights); }//from w w w .j a v a2 s .c o m } return table; }
From source file:i5.las2peer.services.recommender.librec.rating.TimeComNeighSVD.java
@Override protected void initModel() throws Exception { super.initModel(); minTrainTimestamp = Long.MAX_VALUE; maxTrainTimestamp = Long.MIN_VALUE; for (MatrixEntry e : trainMatrix) { long t = (long) timeMatrix.get(e.row(), e.column()); if (t < minTrainTimestamp) minTrainTimestamp = t;//from w w w .ja va2 s. c om if (t > maxTrainTimestamp) maxTrainTimestamp = t; } numDays = days(maxTrainTimestamp, minTrainTimestamp) + 1; userBias = new DenseVector(numUsers); userBias.init(initMean, initStd); itemBias = new DenseVector(numItems); itemBias.init(initMean, initStd); Alpha = new DenseVector(numUsers); Alpha.init(initMean, initStd); Bit = new DenseMatrix(numItems, numBins); Bit.init(initMean, initStd); Bipt = new DenseMatrix(numItems, 7); Bipt.init(initMean, initStd); Y = new DenseMatrix(numItems, numFactors); Y.init(initMean, initStd); Auk = new DenseMatrix(numUsers, numFactors); Auk.init(initMean, initStd); But = HashBasedTable.create(); Bupt = new DenseMatrix(numUsers, 7); Bupt.init(initMean, initStd); Pukt = new HashMap<>(); Cu = new DenseVector(numUsers); Cu.init(initMean, initStd); Cut = new DenseMatrix(numUsers, numDays); Cut.init(initMean, initStd); W = new DenseMatrix(numItems, numItems); W.init(initMean, initStd); C = new DenseMatrix(numItems, numItems); C.init(initMean, initStd); Phi = new DenseVector(numUsers); Phi.init(0.01); // cache userItemsCache = trainMatrix.rowColumnsCache(cacheSpec); // global average date double sum = 0; int cnt = 0; for (MatrixEntry me : trainMatrix) { int u = me.row(); int i = me.column(); double rui = me.get(); if (rui <= 0) continue; sum += days((long) timeMatrix.get(u, i), minTrainTimestamp); cnt++; } globalMeanDate = sum / cnt; // compute users' mean rating timestamps userMeanDate = new DenseVector(numUsers); List<Integer> Ru = null; for (int u = 0; u < numUsers; u++) { sum = 0; Ru = userItemsCache.get(u); for (int i : Ru) { sum += days((long) timeMatrix.get(u, i), minTrainTimestamp); } double mean = (Ru.size() > 0) ? (sum + 0.0) / Ru.size() : globalMeanDate; userMeanDate.set(u, mean); } // build user and item graphs Logs.info("{}{} build user and item graphs ...", new Object[] { algoName, foldInfo }); SparseMatrix[] userMatrix = new SparseMatrix[numCBins + 1]; SparseMatrix[] itemMatrix = new SparseMatrix[numCBins + 1]; GraphBuilder gb = new GraphBuilder(); gb.setMethod(graphMethod); gb.setK(knn); gb.setSimilarityMeasure(sim); gb.setTaggingData(userTagTable, itemTagTable); gb.setRatingData(trainMatrix); gb.buildGraphs(); userMatrix[0] = gb.getUserAdjacencyMatrix(); itemMatrix[0] = gb.getItemAdjacencyMatrix(); if (numCBins > 1) { SparseMatrix[] trainMatrixCBin = trainDataCBins(); List<Table<Integer, Integer, Set<Long>>> userTagTableCBin = null; List<Table<Integer, Integer, Set<Long>>> itemTagTableCBin = null; if (graphMethod == GraphConstructionMethod.TAGS) { userTagTableCBin = tagDataCBins(userTagTable); itemTagTableCBin = tagDataCBins(itemTagTable); } for (int cbin = 1; cbin <= numCBins; cbin++) { if (graphMethod == GraphConstructionMethod.TAGS) { gb.setTaggingData(userTagTableCBin.get(cbin - 1), itemTagTableCBin.get(cbin - 1)); } else { gb.setRatingData(trainMatrixCBin[cbin - 1]); } gb.buildGraphs(); userMatrix[cbin] = gb.getUserAdjacencyMatrix(); itemMatrix[cbin] = gb.getItemAdjacencyMatrix(); } } gb = null; // detect communities Logs.info("{}{} detect communities ...", new Object[] { algoName, foldInfo }); userMemberships = new SparseMatrix[numCBins + 1]; itemMemberships = new SparseMatrix[numCBins + 1]; userCommunitiesCache = new ArrayList<LoadingCache<Integer, List<Integer>>>(numCBins + 1); itemCommunitiesCache = new ArrayList<LoadingCache<Integer, List<Integer>>>(numCBins + 1); numUserCommunities = new int[numCBins + 1]; numItemCommunities = new int[numCBins + 1]; CommunityDetector cd = new CommunityDetector(); cd.setAlgorithm(cdAlgo); if (cdAlgo == CommunityDetectionAlgorithm.WALKTRAP) cd.setWalktrapParameters(wtSteps); for (int cbin = 0; cbin <= numCBins; cbin++) { if (numCBins == 1 && cbin == 1) { // if we use only one bin no need to detect communities again userMemberships[cbin] = userMemberships[0]; itemMemberships[cbin] = itemMemberships[0]; } else { cd.setGraph(userMatrix[cbin]); cd.detectCommunities(); userMemberships[cbin] = cd.getMemberships(); cd.setGraph(itemMatrix[cbin]); cd.detectCommunities(); itemMemberships[cbin] = cd.getMemberships(); } if (maxOC > 0) { Logs.info("{}{} reduce community memberships to max. {} communities per user/item ...", new Object[] { algoName, foldInfo, maxOC }); userMemberships[cbin] = Communities.limitOverlappingCommunities(userMemberships[cbin], maxOC); itemMemberships[cbin] = Communities.limitOverlappingCommunities(itemMemberships[cbin], maxOC); } userCommunitiesCache.add(cbin, userMemberships[cbin].rowColumnsCache(cacheSpec)); numUserCommunities[cbin] = userMemberships[cbin].numColumns(); itemCommunitiesCache.add(cbin, itemMemberships[cbin].rowColumnsCache(cacheSpec)); numItemCommunities[cbin] = itemMemberships[cbin].numColumns(); } userMatrix = null; itemMatrix = null; cd = null; logCommunityInfo(); // compute user communities' average ratings for each item communityRatingsMatrix = new SparseMatrix[numCBins + 1]; communityTimeMatrix = new SparseMatrix[numCBins + 1]; communityMeanDate = new DenseVector[numCBins + 1]; for (int cbin = 0; cbin <= numCBins; cbin++) { communityMeanDate[cbin] = new DenseVector(numUserCommunities[cbin]); Table<Integer, Integer, Double> communityRatingsTable = HashBasedTable.create(); Table<Integer, Integer, Double> communityTimeTable = HashBasedTable.create(); for (int community = 0; community < numUserCommunities[cbin]; community++) { // each user's membership level for the community SparseVector communityUsersVector = userMemberships[cbin].column(community); // build set of items that have been rated by members of the community HashSet<Integer> items = new HashSet<Integer>(); for (VectorEntry e : communityUsersVector) { int user = e.index(); List<Integer> userItems = userItemsCache.get(user); for (int item : userItems) items.add(item); } // to compute mean rating times for each community keep track of time and number of ratings given double communityTimeSum = 0; int ratingsCount = 0; for (int item : items) { // Sum of ratings given by users of the community to the item, weighted by the users community membership levels double ratingsSum = 0; double communityItemTimeSum = 0; double membershipsSum = 0; // Each user's rating for the item SparseVector itemUsersVector = trainMatrix.column(item); for (VectorEntry e : communityUsersVector) { int user = e.index(); if (itemUsersVector.contains(user)) { double muc = userMemberships[cbin].get(user, community); double rui = itemUsersVector.get(user); double tui = timeMatrix.get(user, item); ratingsSum += rui * muc; communityItemTimeSum += tui * muc; membershipsSum += muc; communityTimeSum += days((long) timeMatrix.get(user, item), minTrainTimestamp); ratingsCount++; } } if (membershipsSum > 0) { double communityRating = ratingsSum / membershipsSum; double communityTime = communityItemTimeSum / membershipsSum; communityRatingsTable.put(community, item, communityRating); communityTimeTable.put(community, item, communityTime); } } double meanTime = (ratingsCount > 0) ? (communityTimeSum) / ratingsCount : globalMeanDate; communityMeanDate[cbin].set(community, meanTime); } communityRatingsMatrix[cbin] = new SparseMatrix(numUserCommunities[cbin], numItems, communityRatingsTable); communityTimeMatrix[cbin] = new SparseMatrix(numUserCommunities[cbin], numItems, communityTimeTable); int numRatingsPerCommunity = communityRatingsMatrix[cbin].size() / communityRatingsMatrix[cbin].numRows(); Logs.info("{}{} Community Ratings: Number of communities: {}, Avg. number of ratings per community: {}", algoName, foldInfo, communityRatingsMatrix[cbin].numRows(), numRatingsPerCommunity); } // compute each user's communities' average rating for each item userCommunitiesRatingsMatrix = new SparseMatrix[numCBins + 1]; userCommunitiesTimeMatrix = new SparseMatrix[numCBins + 1]; userCommunitiesItemsCache = new ArrayList<LoadingCache<Integer, List<Integer>>>(numCBins + 1); for (int cbin = 0; cbin <= numCBins; cbin++) { Table<Integer, Integer, Double> userCommunitiesRatingsTable = HashBasedTable.create(); Table<Integer, Integer, Double> userCommunitiesTimeTable = HashBasedTable.create(); for (int user = 0; user < numUsers; user++) { List<Integer> userCommunities; userCommunities = userCommunitiesCache.get(cbin).get(user); int[] topKItems = new int[communitiesItemsK]; double[] topKItemsMemberships = new double[communitiesItemsK]; double[] topKItemsRatings = new double[communitiesItemsK]; double[] topKItemsTime = new double[communitiesItemsK]; for (int i = 0; i < communitiesItemsK; i++) { topKItems[i] = -1; } // position of the item with the lowest membership level in the top-k array int minItemPos = 0; // membership level of that item double minMembership = 0; for (int item = 0; item < numItems; item++) { double ratingsSum = 0; double timeSum = 0; double membershipsSum = 0; for (int community : userCommunities) { double communityRating = communityRatingsMatrix[cbin].get(community, item); double communityTime = communityTimeMatrix[cbin].get(community, item); double userMembership = userMemberships[cbin].get(user, community); ratingsSum += communityRating * userMembership; timeSum += communityTime * userMembership; membershipsSum += userMembership; } if (ratingsSum > 0 && membershipsSum > minMembership) { topKItems[minItemPos] = item; topKItemsMemberships[minItemPos] = membershipsSum; topKItemsRatings[minItemPos] = ratingsSum; topKItemsTime[minItemPos] = timeSum; // find item with lowest membership level in the array minMembership = membershipsSum; for (int i = 0; i < communitiesItemsK; i++) { if (topKItemsMemberships[i] < minMembership) { minItemPos = i; minMembership = topKItemsMemberships[i]; } } } } // fill top-k items into table for (int i = 0; i < communitiesItemsK; i++) { if (topKItems[i] >= 0) { int item = topKItems[i]; double userCommunitiesRating = topKItemsRatings[i] / topKItemsMemberships[i]; double userCommunitiesTime = topKItemsTime[i] / topKItemsMemberships[i]; userCommunitiesRatingsTable.put(user, item, userCommunitiesRating); userCommunitiesTimeTable.put(user, item, userCommunitiesTime); } } } userCommunitiesRatingsMatrix[cbin] = new SparseMatrix(numUsers, numItems, userCommunitiesRatingsTable); userCommunitiesTimeMatrix[cbin] = new SparseMatrix(numUsers, numItems, userCommunitiesTimeTable); userCommunitiesItemsCache.add(cbin, userCommunitiesRatingsMatrix[cbin].rowColumnsCache(cacheSpec)); int numRatingsPerUser = userCommunitiesRatingsMatrix[cbin].size() / userCommunitiesRatingsMatrix[cbin].numRows(); Logs.info( "{}{} User Communities Ratings: Number of users: {}, Avg. number of community ratings per user: {}", algoName, foldInfo, userCommunitiesRatingsMatrix[cbin].numRows(), numRatingsPerUser); } // initialize community-related model parameters AlphaC = new DenseVector(numUserCommunities[0]); AlphaC.init(initMean, initStd); D = new DenseMatrix(numItems, numItems); D.init(initMean, initStd); Psi = new DenseVector(numUsers); Psi.init(0.01); BCu = new DenseVector[numCBins + 1]; BCut = new ArrayList<Table<Integer, Integer, Double>>(numCBins + 1); BCi = new DenseVector[numCBins + 1]; BCit = new DenseMatrix[numCBins + 1]; OCi = new DenseMatrix[numCBins + 1]; OCu = new DenseMatrix[numCBins + 1]; OCut = new ArrayList<Map<Integer, Table<Integer, Integer, Double>>>(numCBins + 1); ACu = new DenseMatrix(numUserCommunities[0], numFactors); ACu.init(initMean, initStd); Z = new DenseMatrix(numItems, numItems); Z.init(initMean, initStd); for (int cbin = 0; cbin <= numCBins; cbin++) { BCu[cbin] = new DenseVector(numUserCommunities[cbin]); BCu[cbin].init(initMean, initStd); BCut.add(cbin, HashBasedTable.create()); BCi[cbin] = new DenseVector(numItemCommunities[cbin]); BCi[cbin].init(initMean, initStd); BCit[cbin] = new DenseMatrix(numItemCommunities[cbin], numBins); BCit[cbin].init(initMean, initStd); OCi[cbin] = new DenseMatrix(numItemCommunities[cbin], numFactors); OCi[cbin].init(initMean, initStd); OCu[cbin] = new DenseMatrix(numUserCommunities[cbin], numFactors); OCu[cbin].init(initMean, initStd); OCut.add(cbin, new HashMap<>()); } }
From source file:org.opennms.netmgt.bsm.vaadin.adminpage.BusinessServiceTreeTable.java
private com.google.common.collect.Table<Long, Optional<Long>, Boolean> getCurrentExpandState() { // Gather the current collapse state final com.google.common.collect.Table<Long, Optional<Long>, Boolean> collapseState = HashBasedTable .create();// ww w. java 2 s .c om for (Object itemId : getItemIds()) { final BusinessServiceRow row = getItem(itemId).getBean(); collapseState.put(row.getBusinessService().getId(), Optional.ofNullable(row.getParentBusinessServiceId()), isCollapsed(itemId)); } return collapseState; }
From source file:org.mousephenotype.dcc.exportlibrary.exporter.dbloading.Loader.java
public CentreSpecimenSet getSingleColonyID() throws ConfigurationException, HibernateException { String printFile = FileReader.printFile(SINGLE_COLONYID); CentreSpecimenSet centreSpecimenSet = new CentreSpecimenSet(); List<Specimen> specimens = this.hibernateManager.nativeQuery(printFile, Specimen.class); logger.trace("{} specimens retrieved", specimens.size()); if (specimens != null && !specimens.isEmpty()) { CentreSpecimen aux = null;/*from w w w . jav a 2s.co m*/ Table<String, Class, Object> parameters = HashBasedTable.create(); Map<String, org.hibernate.type.Type> scalars = ImmutableMap.<String, org.hibernate.type.Type>builder() .put("centreID", StringType.INSTANCE).build(); logger.trace("linking to "); for (Specimen specimen : specimens) { parameters.put("specimenHJID", Long.class, specimen.getHjid()); List<String> nativeQuery = this.hibernateManager.nativeQuery( "select CENTRESPECIMEN.CENTREID as centreID from phenodcc_raw.CENTRESPECIMEN join phenodcc_raw.SPECIMEN on CENTRESPECIMEN.HJID = SPECIMEN.MOUSEOREMBRYO_CENTRESPECIMEN_0 where SPECIMEN.HJID = :specimenHJID", scalars, parameters); if (nativeQuery != null && !nativeQuery.isEmpty()) { logger.trace("{} centre for specimenID {}", nativeQuery.get(0), specimen.getSpecimenID()); aux = this.getCentreSpecimen(centreSpecimenSet, CentreILARcode.valueOf(nativeQuery.get(0))); if (aux == null) { aux = new CentreSpecimen(); aux.setCentreID(CentreILARcode.valueOf(nativeQuery.get(0))); centreSpecimenSet.getCentre().add(aux); } aux.getMouseOrEmbryo().add(specimen); } else { logger.error("specimen HJID {} is not part of a centreSpecimen", specimen.getHjid()); } } } return centreSpecimenSet; }
From source file:i5.las2peer.services.recommender.librec.data.DataSplitter.java
/** * Split ratings into two parts where one rating per user is preserved as the test set and the remaining data as the * training set//from www . java 2s . c o m * */ public SparseMatrix[] getLOOByUser(boolean isByDate, SparseMatrix timestamps) throws Exception { SparseMatrix trainMatrix = new SparseMatrix(rateMatrix); // for building test matrix Table<Integer, Integer, Double> dataTable = HashBasedTable.create(); Multimap<Integer, Integer> colMap = HashMultimap.create(); for (int u = 0, um = rateMatrix.numRows(); u < um; u++) { List<Integer> items = rateMatrix.getColumns(u); int i = -1; if (!isByDate) { // by random int randIdx = (int) (items.size() * Math.random()); i = items.get(randIdx); } else { // by date List<RatingContext> rcs = new ArrayList<>(); for (int j : items) { rcs.add(new RatingContext(u, j, (long) timestamps.get(u, j))); } Collections.sort(rcs); i = rcs.get(rcs.size() - 1).getItem(); // most recent item } trainMatrix.set(u, i, 0); // remove from training dataTable.put(u, i, rateMatrix.get(u, i)); colMap.put(i, u); } // remove zero entries SparseMatrix.reshape(trainMatrix); // build test matrix SparseMatrix testMatrix = new SparseMatrix(rateMatrix.numRows, rateMatrix.numColumns, dataTable, colMap); debugInfo(trainMatrix, testMatrix, -1); return new SparseMatrix[] { trainMatrix, testMatrix }; }
From source file:eu.lp0.cursus.xml.scores.XMLScores.java
private void extractRaceResults() { for (ScoresXMLRaceResults raceResult : scoresXML.getRaceResults()) { Race race = dereference(raceResult); resultsRaces.put(raceResult, race); Table<Pilot, Race, ScoresXMLRaceScore> raceScores = HashBasedTable.create(); for (ScoresXMLRaceScore raceScore : raceResult.getRacePilots()) { raceScores.row(dereference(raceScore)).put(race, raceScore); }//from w w w . j a v a 2s.co m resultsPilotRaceScores.put(raceResult, raceScores); for (ScoresXMLOverallScore overallScore : raceResult.getOverallPilots()) { Pilot pilot = dereference(overallScore); resultsPilotOverallScores.row(raceResult).put(pilot, overallScore); resultsPilots.put(raceResult, pilot); } } }
From source file:net.librec.data.convertor.ArffDataConvertor.java
/** * Build the {@link #oneHotFeatureMatrix} * and {@link #oneHotRatingVector}//from ww w.j a v a2 s. c o m */ public void oneHotEncoding() { Table<Integer, Integer, Double> dataTable = HashBasedTable.create(); Multimap<Integer, Integer> colMap = HashMultimap.create(); int numRows = instances.size(); int numCols = 0; int numAttrs = attributes.size(); double[] ratings = new double[numRows]; // set numCols for (int i = 0; i < attributes.size(); i++) { // skip rating column if (i == ratingCol) continue; ArffAttribute attr = attributes.get(i); numCols += attr.getColumnSet().size() == 0 ? 1 : attr.getColumnSet().size(); } // build one-hot encoding matrix for (int row = 0; row < numRows; row++) { ArffInstance instance = instances.get(row); int colPrefix = 0; int col = 0; for (int i = 0; i < numAttrs; i++) { String type = attrTypes.get(i); Object val = instance.getValueByIndex(i); // rating column if (i == ratingCol) { ratings[row] = (double) val; continue; } // appender column switch (type) { case "NUMERIC": case "REAL": case "INTEGER": col = colPrefix; dataTable.put(row, col, (double) val); colMap.put(col, row); colPrefix += 1; break; case "STRING": col = colPrefix + columnIds.get(i).get(val); dataTable.put(row, col, 1d); colMap.put(col, row); colPrefix += columnIds.get(i).size(); break; case "NOMINAL": for (String v : (ArrayList<String>) val) { col = colPrefix + columnIds.get(i).get(v); colMap.put(col, row); dataTable.put(row, col, 1d); } colPrefix += columnIds.get(i).size(); break; } } } oneHotFeatureMatrix = new SparseMatrix(numRows, numCols, dataTable, colMap); oneHotRatingVector = new DenseVector(ratings); // release memory dataTable = null; colMap = null; }
From source file:es.upm.dit.xsdinferencer.extraction.extractorImpl.TypesExtractorImpl.java
/** * Method that actually initializes data. This is the code which normally should go on constructors. * However, we have separated it to allow subclasses to initialize parameters after having performed * some other own tasks. So, if a constructor on any subclass calls {@link TypesExtractorImpl#TypesExtractorImpl()} empty * constructor, it must call this method in any moment. * //from w w w . ja v a2 s . com * @param xmlDocuments A list of all the input XML Documents, as JDOM2 {@link Document} objects. * @param configuration the inference configuration * @param inferencersFactory {@link InferencersFactory} used to build {@link AttributeListInferencer} and {@link SimpleTypeInferencer} objects used. */ protected void initializeData(List<Document> xmlDocuments, XSDInferenceConfiguration configuration, InferencersFactory inferencersFactory) { checkNotNull(xmlDocuments, "'xmlDocuments' must not be null"); checkNotNull(configuration, "'configuration' must not be null"); this.xmlDocuments = xmlDocuments; this.configuration = configuration; this.simpleTypeInferencersOfComplexTypes = new HashMap<String, SimpleTypeInferencer>(); this.attributeListInferencers = new HashMap<String, AttributeListInferencer>(); this.automatons = new HashMap<String, ExtendedAutomaton>(); this.statistics = new Statistics(xmlDocuments.size()); this.elements = HashBasedTable.create(); this.complexTypes = new HashMap<>(); this.simpleTypes = new HashMap<String, SimpleType>(); this.elements = HashBasedTable.create(); this.attributes = HashBasedTable.create(); this.prefixNamespaceMapping = new TreeMap<String, SortedSet<String>>(); this.inferencersFactory = inferencersFactory; }
From source file:co.turnus.profiling.impl.ProfilingDataImpl.java
@Override public Table<Actor, Action, ActionProfilingData> asTable() { Table<Actor, Action, ActionProfilingData> table = HashBasedTable.create(); for (ActorProfilingData actorData : getActorsData()) { for (ActionProfilingData actionData : actorData.getActionsData()) { table.put(actorData.getActor(), actionData.getAction(), actionData); }//from www . j av a2 s . c o m } return table; }