Example usage for com.google.common.collect HashBasedTable create

Introduction

In this page you can find the example usage for com.google.common.collect HashBasedTable create.

Prototype

public static <R, C, V> HashBasedTable<R, C, V> create() 

Source Link

Document

Creates an empty HashBasedTable .

Usage

From source file:librec.rating.GPLSA.java

@Override
protected void initModel() throws Exception {

    // Pz_u//from   w  ww.j a v  a2 s. c o m
    Puk = new DenseMatrix(numUsers, numFactors);
    for (int u = 0; u < numUsers; u++) {
        double[] probs = Randoms.randProbs(numFactors);
        for (int k = 0; k < numFactors; k++) {
            Puk.set(u, k, probs[k]);
        }
    }

    // normalize ratings
    double mean = globalMean;
    double sd = Stats.sd(trainMatrix.getData(), mean);

    q = algoOptions.getFloat("-q");
    b = algoOptions.getFloat("-b", 1.0f);

    mu = new DenseVector(numUsers);
    sigma = new DenseVector(numUsers);
    for (int u = 0; u < numUsers; u++) {
        SparseVector ru = trainMatrix.row(u);
        int Nu = ru.size();
        if (Nu < 1)
            continue;

        // compute mu_u
        double mu_u = (ru.sum() + q * mean) / (Nu + q);
        mu.set(u, mu_u);

        // compute sigma_u
        double sum = 0;
        for (VectorEntry ve : ru) {
            sum += Math.pow(ve.get() - mu_u, 2);
        }
        sum += q * Math.pow(sd, 2);
        double sigma_u = Math.sqrt(sum / (Nu + q));
        sigma.set(u, sigma_u);
    }

    // initialize Q
    Q = HashBasedTable.create();

    for (MatrixEntry me : trainMatrix) {
        int u = me.row();
        int i = me.column();
        double rate = me.get();

        double r = (rate - mu.get(u)) / sigma.get(u); // continuous ratings
        me.set(r);

        Q.put(u, i, new HashMap<Integer, Double>());
    }

    // initialize Mu, Sigma
    Mu = new DenseMatrix(numItems, numFactors);
    Sigma = new DenseMatrix(numItems, numFactors);
    for (int i = 0; i < numItems; i++) {
        SparseVector ci = trainMatrix.column(i);
        int Ni = ci.size();

        if (Ni < 1)
            continue;

        double mu_i = ci.mean();

        double sum = 0;
        for (VectorEntry ve : ci) {
            sum += Math.pow(ve.get() - mu_i, 2);
        }
        double sd_i = Math.sqrt(sum / Ni);

        for (int z = 0; z < numFactors; z++) {
            Mu.set(i, z, mu_i + smallValue * Math.random());
            Sigma.set(i, z, sd_i + smallValue * Math.random());
        }
    }
}

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From source file:de.uni_potsdam.hpi.asg.delaymatch.setup.MeasureRecordGenerator.java

public MeasureRecordGenerator(Map<String, DelayMatchModule> modules, File file, VerilogModule rootModule) {
    this.modules = modules;
    this.file = file;
    this.rootModule = rootModule;
    this.transtable = HashBasedTable.create();
}

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From source file:com.continuuity.weave.internal.appmaster.RunningContainers.java

RunningContainers(String appId, WeaveRunResources appMasterResources) {
    containers = HashBasedTable.create();
    runnableInstances = Maps.newHashMap();
    startSequence = Lists.newLinkedList();
    containerLock = new ReentrantLock();
    containerChange = containerLock.newCondition();
    resourceReport = new DefaultResourceReport(appId, appMasterResources);
}

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From source file:com.przemo.etl.dataproviders.CSVDataProvider.java

/**
 * Reads data from a given file. Headers array gets populated either with default ones or with read in from the file, if so requested.
 * @return//from  ww  w.  j a va 2 s.  c  o m
 * @throws Exception
 */
@Override
public Table readData() {
    Table data = HashBasedTable.create();
    if (file.exists() && file.canRead()) {
        try {
            BufferedReader r = new BufferedReader(new FileReader(file));
            String line;
            boolean dhReadIn = false;
            int rowNumber = 0;
            while ((line = r.readLine()) != null) {
                String[] lineArray = line.split(sep);
                if (!dhReadIn) {
                    if (dh) {
                        headers = lineArray;
                    } else {
                        headers = defaultHeaders(lineArray.length);
                    }
                    dhReadIn = true;
                } else {
                    for (int c = 0; c < lineArray.length; c++) {
                        data.put(rowNumber, headers[c], lineArray[c]);
                    }
                }
                rowNumber++;
            }
        } catch (FileNotFoundException ex) {
            Logger.getLogger(CSVDataProvider.class.getName()).log(Level.SEVERE, null, ex);
        } catch (IOException ex) {
            Logger.getLogger(CSVDataProvider.class.getName()).log(Level.SEVERE, null, ex);
        } catch (Exception ex) {
            Logger.getLogger(CSVDataProvider.class.getName()).log(Level.SEVERE, null, ex);
        }

    }
    return data;
}

---

From source file:i5.las2peer.services.recommender.librec.data.CSVDataDAO.java

/**
 * Read data from the data file. Note that we didn't take care of the duplicated lines.
 * /* w  ww  .  j  a  va2s.  co  m*/
 * @param cols
 *            the indexes of the relevant columns in the data file: {user, item, [rating, timestamp] (optional)}
 * @param binThold
 *            the threshold to binarize a rating. If a rating is greater than the threshold, the value will be 1;
 *            otherwise 0. To disable this feature, i.e., keep the original rating value, set the threshold a
 *            negative value
 * @return a sparse matrix storing all the relevant data
 */
public SparseMatrix[] readData(int[] cols, double binThold) throws Exception {

    Logs.info(String.format("Dataset: %s", Strings.last(dataPath, 38)));

    // Table {row-id, col-id, rate}
    Table<Integer, Integer, Double> dataTable = HashBasedTable.create();
    // Table {row-id, col-id, timestamp}
    Table<Integer, Integer, Long> timeTable = null;
    // Map {col-id, multiple row-id}: used to fast build a rating matrix
    Multimap<Integer, Integer> colMap = HashMultimap.create();

    BufferedReader br = FileIO.getReader(dataPath);
    String line = null;
    minTimestamp = Long.MAX_VALUE;
    maxTimestamp = Long.MIN_VALUE;
    while ((line = br.readLine()) != null) {
        if (isHeadline()) {
            setHeadline(false);
            continue;
        }

        String[] data = line.trim().split("[ \t,]+");

        if (data.length < 2) {
            Logs.error(String.format("Dataset: Cannot read line \"%s\"", line));
            continue;
        }

        String user = data[cols[0]];
        String item = data[cols[1]];
        Double rate = (cols.length >= 3 && data.length >= 3) ? Double.valueOf(data[cols[2]]) : 1.0;

        // binarize the rating for item recommendation task
        if (binThold >= 0)
            rate = rate > binThold ? 1.0 : 0.0;

        scaleDist.add(rate);

        // inner id starting from 0
        int row = userIds.containsKey(user) ? userIds.get(user) : userIds.size();
        userIds.put(user, row);

        int col = itemIds.containsKey(item) ? itemIds.get(item) : itemIds.size();
        itemIds.put(item, col);

        dataTable.put(row, col, rate);
        colMap.put(col, row);

        // record rating's issuing time
        if (cols.length >= 4 && data.length >= 4) {
            if (timeTable == null)
                timeTable = HashBasedTable.create();

            // convert to million-seconds
            long mms = 0L;
            try {
                mms = Long.parseLong(data[cols[3]]); // cannot format "9.7323480e+008"
            } catch (NumberFormatException e) {
                mms = (long) Double.parseDouble(data[cols[3]]);
            }
            long timestamp = timeUnit.toMillis(mms);

            if (minTimestamp > timestamp)
                minTimestamp = timestamp;

            if (maxTimestamp < timestamp)
                maxTimestamp = timestamp;

            timeTable.put(row, col, timestamp);
        }

    }
    br.close();

    numRatings = scaleDist.size();
    ratingScale = new ArrayList<>(scaleDist.elementSet());
    Collections.sort(ratingScale);

    int numRows = numUsers(), numCols = numItems();

    // if min-rate = 0.0, shift upper a scale
    double minRate = ratingScale.get(0).doubleValue();
    double epsilon = minRate == 0.0 ? ratingScale.get(1).doubleValue() - minRate : 0;
    if (epsilon > 0) {
        // shift upper a scale
        for (int i = 0, im = ratingScale.size(); i < im; i++) {
            double val = ratingScale.get(i);
            ratingScale.set(i, val + epsilon);
        }
        // update data table
        for (int row = 0; row < numRows; row++) {
            for (int col = 0; col < numCols; col++) {
                if (dataTable.contains(row, col))
                    dataTable.put(row, col, dataTable.get(row, col) + epsilon);
            }
        }
    }

    String dateRange = "";
    if (cols.length >= 4)
        dateRange = String.format(", Timestamps = {%s, %s}", Dates.toString(minTimestamp),
                Dates.toString(maxTimestamp));

    Logs.debug("With Specs: {Users, {}} = {{}, {}, {}}, Scale = {{}}{}",
            (isItemAsUser ? "Users, Links" : "Items, Ratings"), numRows, numCols, numRatings,
            Strings.toString(ratingScale), dateRange);

    // build rating matrix
    rateMatrix = new SparseMatrix(numRows, numCols, dataTable, colMap);

    if (timeTable != null)
        timeMatrix = new SparseMatrix(numRows, numCols, timeTable, colMap);

    // release memory of data table
    dataTable = null;
    timeTable = null;

    return new SparseMatrix[] { rateMatrix, timeMatrix };
}

---

From source file:librec.undefined.TimeSVDPlusPlus.java

@Override
protected void initModel() throws Exception {
    super.initModel();

    userBias = new DenseVector(numUsers);
    userBias.init();//from ww  w  .  j  a  v  a2  s  . co  m

    itemBias = new DenseVector(numItems);
    itemBias.init();

    userAlpha = new DenseVector(numUsers);
    userAlpha.init();

    Bit = new DenseMatrix(numItems, numBins);
    Bit.init();

    userScaling = new DenseVector(numUsers); // cu
    userScaling.init();

    Y = new DenseMatrix(numItems, numFactors);
    Y.init();

    Auf = new DenseMatrix(numUsers, numFactors);
    Auf.init();

    But = HashBasedTable.create();
    Cut = HashBasedTable.create();
    Puft = new HashMap<>();
}

---

From source file:hu.ppke.itk.nlpg.purepos.decoder.BeamedViterbi.java

public List<Pair<List<Integer>, Double>> beamedSearch(final NGram<Integer> start,
        final List<String> observations, int resultsNumber) {
    HashMap<NGram<Integer>, Node> beam = new HashMap<NGram<Integer>, Node>();

    beam.put(start, startNode(start));//from   ww  w  . j  a v  a 2s . c o m
    boolean isFirst = true;
    int pos = 0;
    for (String obs : observations) {
        // System.err.println(obs);

        // logger.trace("Current observation " + obs);
        // logger.trace("\tCurrent states:");
        // for (Entry<NGram<Integer>, Node> entry : beam.entrySet()) {
        // logger.trace("\t\t" + entry.getKey() + " - " + entry.getValue());
        // }

        HashMap<NGram<Integer>, Node> newBeam = new HashMap<NGram<Integer>, Node>();

        Table<NGram<Integer>, Integer, Double> nextProbs = HashBasedTable.create();
        Map<NGram<Integer>, Double> obsProbs = new HashMap<NGram<Integer>, Double>();
        Set<NGram<Integer>> contexts = beam.keySet();

        Map<NGram<Integer>, Map<Integer, Pair<Double, Double>>> nexts = getNextProbs(contexts, obs, pos,
                isFirst);

        for (Map.Entry<NGram<Integer>, Map<Integer, Pair<Double, Double>>> nextsEntry : nexts.entrySet()) {
            NGram<Integer> context = nextsEntry.getKey();
            Map<Integer, Pair<Double, Double>> nextContextProbs = nextsEntry.getValue();
            for (Map.Entry<Integer, Pair<Double, Double>> entry : nextContextProbs.entrySet()) {
                Integer tag = entry.getKey();
                nextProbs.put(context, tag, entry.getValue().getLeft());
                obsProbs.put(context.add(tag), entry.getValue().getRight());
            }
        }
        // for (Integer t : nextProbs.keySet()) {
        // logger.trace("\t\tNext node:" + context + t);
        // logger.trace("\t\tnode currentprob:"
        // + (beam.get(context) + nextProbs.get(t).getLeft()));
        // logger.trace("\t\tnode emissionprob:"
        // + nextProbs.get(t).getRight());
        // logger.trace("\n");
        // // logger.trace("\t\tNext node:" + context + t);
        // }
        for (Cell<NGram<Integer>, Integer, Double> cell : nextProbs.cellSet()) {
            Integer nextTag = cell.getColumnKey();
            NGram<Integer> context = cell.getRowKey();
            Double transVal = cell.getValue();
            NGram<Integer> newState = context.add(nextTag);
            Node from = beam.get(context);
            double newVal = transVal + beam.get(context).getWeight();
            update(newBeam, newState, newVal, from);
        }
        // adding observation probabilities
        // logger.trace("beam" + newBeam);
        if (nextProbs.size() > 1)
            for (NGram<Integer> tagSeq : newBeam.keySet()) {
                // Integer tag = tagSeq.getLast();
                Node node = newBeam.get(tagSeq);
                // Double prevVal = node.getWeight();

                Double obsProb = obsProbs.get(tagSeq);
                // logger.trace("put to beam: " + context + "(from) "
                // + tagSeq + " " + prevVal + "+" + obsProb);
                node.setWeight(obsProb + node.getWeight());
            }

        beam = prune(newBeam);
        isFirst = false;
        // for (Entry<NGram<Integer>, Node> e : beam.entrySet()) {
        // logger.trace("\t\tNode state: " + e.getKey() + " "
        // + e.getValue());
        // }
        ++pos;
    }
    return findMax(beam, resultsNumber);
}

---

From source file:com.sk89q.worldedit.world.block.BlockState.java

private void populate(Map<Map<Property<?>, Object>, BlockState> stateMap) {
    final Table<Property<?>, Object, BlockState> states = HashBasedTable.create();

    for (final Map.Entry<Property<?>, Object> entry : this.values.entrySet()) {
        final Property<Object> property = (Property<Object>) entry.getKey();

        property.getValues().forEach(value -> {
            if (value != entry.getValue()) {
                BlockState modifiedState = stateMap.get(this.withValue(property, value));
                if (modifiedState != null) {
                    states.put(property, value, modifiedState);
                } else {
                    System.out.println(stateMap);
                    WorldEdit.logger.warn("Found a null state at " + this.withValue(property, value));
                }//from w w  w .  ja v a2 s  .  com
            }
        });
    }

    this.states = states.isEmpty() ? states : ArrayTable.create(states);
}

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From source file:es.usc.citius.composit.core.composition.network.AbstractServiceMatchNetwork.java

public Map<Operation<E>, Table<E, E, T>> getSourceOperationsThatMatch(Operation<E> target) {
    // First, compute the source elements that match the op.inputs
    Map<Operation<E>, Table<E, E, T>> matchMap = new HashMap<Operation<E>, Table<E, E, T>>();
    for (E targetInput : target.getSignature().getInputs()) {
        Map<E, T> sourceMatch = getSourceElementsThatMatch(targetInput);
        // Find the providers
        for (Map.Entry<E, T> sourceMatchEntry : sourceMatch.entrySet()) {
            E sourceOutput = sourceMatchEntry.getKey();
            Set<Operation<E>> sourceOps = Sets.newHashSet(getOperationsWithOutput(sourceOutput));
            // Annotate source output->target input match
            for (Operation<E> op : sourceOps) {
                Table<E, E, T> matchTable = matchMap.get(op);
                if (matchTable == null) {
                    matchTable = HashBasedTable.create();
                    matchMap.put(op, matchTable);
                }//www  .  j av  a2s  .c  o  m
                // Add match entry
                matchTable.put(sourceOutput, targetInput, sourceMatchEntry.getValue());
            }
        }
    }
    return matchMap;
}

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From source file:org.mousephenotype.dcc.exportlibrary.traverser.Reporter.java

public Reporter(CommandImpl validationResultsExtractor, HibernateManager hibernateManager) {
    this.validationResultsExtractor = validationResultsExtractor;
    this.hibernateManager = hibernateManager;
    this.submissions = HashBasedTable.create();
}

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