Example usage for com.google.common.collect Multiset count

Introduction

In this page you can find the example usage for com.google.common.collect Multiset count.

Prototype

int count(@Nullable Object element);

Source Link

Document

Returns the number of occurrences of an element in this multiset (the count of the element).

Usage

From source file:edu.cmu.lti.oaqa.baseqa.providers.ml.classifiers.WekaProvider.java

@Override
public void train(List<Map<String, Double>> X, List<String> Y, boolean crossValidation)
        throws AnalysisEngineProcessException {
    // create attribute (including label) info
    ArrayList<Attribute> attributes = new ArrayList<>();
    ClassifierProvider.featureNames(X).stream().map(Attribute::new).forEachOrdered(attributes::add);
    Attribute label = new Attribute("__label__", ClassifierProvider.labelNames(Y));
    attributes.add(label);/*  w  ww  .j a  v a2 s  .  co m*/
    String name = Files.getNameWithoutExtension(modelFile.getName());
    datasetSchema = new Instances(name, attributes, X.size());
    datasetSchema.setClass(label);
    // add instances
    Instances trainingInstances = new Instances(datasetSchema, X.size());
    if (balanceWeight) {
        Multiset<String> labelCounts = HashMultiset.create(Y);
        double maxCount = labelCounts.entrySet().stream().mapToInt(Multiset.Entry::getCount).max()
                .orElseThrow(AnalysisEngineProcessException::new);
        for (int i = 0; i < X.size(); i++) {
            String y = Y.get(i);
            double weight = maxCount / labelCounts.count(y);
            trainingInstances.add(newInstance(X.get(i), y, weight, trainingInstances));
        }
    } else {
        for (int i = 0; i < X.size(); i++) {
            trainingInstances.add(newInstance(X.get(i), Y.get(i), 1.0, trainingInstances));
        }
    }
    // training
    try {
        classifier = AbstractClassifier.forName(classifierName, options);
        classifier.buildClassifier(trainingInstances);
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
    // write model and dataset schema
    try {
        SerializationHelper.write(modelFile.getAbsolutePath(), classifier);
        SerializationHelper.write(datasetSchemaFile.getAbsolutePath(), datasetSchema);
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
    // backup training dataset as arff file
    if (datasetExportFile != null) {
        try {
            ArffSaver saver = new ArffSaver();
            saver.setInstances(trainingInstances);
            saver.setFile(datasetExportFile);
            saver.writeBatch();
        } catch (IOException e) {
            throw new AnalysisEngineProcessException(e);
        }
    }
    if (crossValidation) {
        try {
            Evaluation eval = new Evaluation(trainingInstances);
            Random rand = new Random();
            eval.crossValidateModel(classifier, trainingInstances, 10, rand);
            LOG.debug(eval.toSummaryString());
        } catch (Exception e) {
            throw new AnalysisEngineProcessException(e);
        }
    }
}

---

From source file:com.memonews.mahout.sentiment.SentimentModelHelper.java

Vector encodeFeatureVector(final File file, final Multiset<String> overallCounts) throws IOException {
    final Multiset<String> words = ConcurrentHashMultiset.create();

    final BufferedReader reader = Files.newReader(file, Charsets.UTF_8);
    try {//from  w  w w. j  a va 2s  . co  m
        countWords(analyzer, words, reader, overallCounts);
    } finally {
        Closeables.closeQuietly(reader);
    }

    final Vector v = new RandomAccessSparseVector(FEATURES);
    bias.addToVector("", 1, v);
    for (final String word : words.elementSet()) {
        encoder.addToVector(word, Math.log1p(words.count(word)), v);
    }

    return v;
}

---

From source file:org.sonar.plugins.core.issue.IssueCountersDecorator.java

private void saveIssuesForSeverity(DecoratorContext context, RulePriority ruleSeverity,
        Multiset<RulePriority> severitiesBag) {
    Metric metric = SeverityUtils.severityToIssueMetric(ruleSeverity);
    if (context.getMeasure(metric) == null) {
        Collection<Measure> children = context.getChildrenMeasures(MeasuresFilters.metric(metric));
        int sum = MeasureUtils.sum(true, children).intValue() + severitiesBag.count(ruleSeverity);
        context.saveMeasure(metric, (double) sum);
    }//from ww  w. j  ava 2 s  .  c om
}

---

From source file:org.dllearner.utilities.examples.AutomaticNegativeExampleFinderSPARQL2.java

private SortedSet<OWLIndividual> negativeExamplesBySiblingClasses(Multiset<OWLClass> positiveExamplesTypes,
        int cnt, int totalCnt) {
    logger.info("Applying sibling classes strategy...");
    SortedSet<OWLIndividual> negExamples = new TreeSet<>();

    // for each type of the positive examples
    for (OWLClass nc : positiveExamplesTypes.elementSet()) {
        int frequency = positiveExamplesTypes.count(nc);

        // get sibling classes
        Set<OWLClass> siblingClasses = sr.getSiblingClasses(nc);
        siblingClasses = filterByNamespace(siblingClasses);
        logger.info("Sibling classes: " + siblingClasses);

        int limit = (int) Math
                .ceil(((double) frequency / positiveExamplesTypes.size()) / siblingClasses.size() * cnt);

        // get instances for each sibling class
        for (OWLClass siblingClass : siblingClasses) {
            SortedSet<OWLIndividual> individuals = sr.getIndividualsExcluding(siblingClass, nc, totalCnt);
            individuals.removeAll(negExamples);
            SetManipulation.stableShrink(individuals, limit);
            negExamples.addAll(individuals);
        }//  www  . j ava2  s  .  c o  m
    }
    negExamples = SetManipulation.stableShrink(negExamples, cnt);
    logger.info("Negative examples(" + negExamples.size() + "): " + negExamples);
    return negExamples;
}

---

From source file:org.dllearner.algorithms.pattern.OWLAxiomPatternFinder.java

private synchronized void addOntologyPatterns(URI physicalURI, OWLOntology ontology,
        Multiset<OWLAxiom> patterns) {
    int ontologyId = addOntology(physicalURI, ontology);
    for (OWLAxiom pattern : patterns.elementSet()) {
        try {/*from   w w w  .  j  a  v  a  2 s  .  c  o m*/
            int patternId = addPattern(pattern);
            int occurrences = patterns.count(pattern);
            insertOntologyPatternPs.setInt(1, ontologyId);
            insertOntologyPatternPs.setInt(2, patternId);
            insertOntologyPatternPs.setInt(3, occurrences);
            insertOntologyPatternPs.execute();
        } catch (SQLException e) {
            System.err.println("Adding pattern\n" + pattern + "\nfailed." + e.getMessage());
        }
    }
}

---

From source file:sklearn.cluster.KMeans.java

@Override
public ClusteringModel encodeModel(Schema schema) {
    int[] shape = getClusterCentersShape();

    int numberOfClusters = shape[0];
    int numberOfFeatures = shape[1];

    List<? extends Number> clusterCenters = getClusterCenters();
    List<Integer> labels = getLabels();

    Multiset<Integer> labelCounts = HashMultiset.create();

    if (labels != null) {
        labelCounts.addAll(labels);//from   w  ww  .java2  s .  com
    }

    List<Cluster> clusters = new ArrayList<>();

    for (int i = 0; i < numberOfClusters; i++) {
        Array array = PMMLUtil
                .createRealArray(MatrixUtil.getRow(clusterCenters, numberOfClusters, numberOfFeatures, i));

        Cluster cluster = new Cluster().setId(String.valueOf(i))
                .setSize((labelCounts.size() > 0 ? labelCounts.count(i) : null)).setArray(array);

        clusters.add(cluster);
    }

    List<Feature> features = schema.getFeatures();

    List<ClusteringField> clusteringFields = ClusteringModelUtil.createClusteringFields(features);

    ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE)
            .setCompareFunction(CompareFunction.ABS_DIFF).setMeasure(new SquaredEuclidean());

    Output output = ClusteringModelUtil.createOutput(FieldName.create("Cluster"), clusters);

    ClusteringModel clusteringModel = new ClusteringModel(MiningFunction.CLUSTERING,
            ClusteringModel.ModelClass.CENTER_BASED, numberOfClusters, ModelUtil.createMiningSchema(schema),
            comparisonMeasure, clusteringFields, clusters).setOutput(output);

    return clusteringModel;
}

---

From source file:bots.mctsbot.ai.bots.bot.gametree.rollout.BucketRollOut.java

private double[] calcDeadCardWeights(Multiset<Integer> ranks, Multiset<Integer> deadRanks) {
    Iterator<Integer> iter = ranks.iterator();
    double[] deadCardWeights = new double[nbBuckets];
    for (int bucket = 0; bucket < nbBuckets; bucket++) {
        double nbDead = 0;
        for (int j = 0; j < nbSamplesPerBucket; j++) {
            int rank = iter.next();
            double count = ranks.count(rank);
            double deadCount = deadRanks.count(rank);
            nbDead += deadCount / count;
        }//from   w  w w  . j a va2  s  .  co  m
        deadCardWeights[bucket] = ((nbSamplesPerBucket - nbDead) / nbSamplesPerBucket);
    }
    return deadCardWeights;
}

---

From source file:org.dllearner.utilities.examples.AutomaticNegativeExampleFinderSPARQL2.java

private SortedSet<OWLIndividual> negativeExamplesBySuperClasses(Multiset<OWLClass> positiveExamplesTypes,
        Set<OWLIndividual> negativeExamples, int cnt, int totalCnt) {
    logger.info("Applying super class strategy...");
    SortedSet<OWLIndividual> negExamples = new TreeSet<>();
    //for each type of the positive examples
    for (OWLClass nc : positiveExamplesTypes.elementSet()) {
        int frequency = positiveExamplesTypes.count(nc);
        //get super classes
        Set<OWLClassExpression> superClasses = sr.getSuperClasses(nc);
        superClasses.remove(df.getOWLThing());
        //               superClasses.remove(Thing.instance);
        superClasses.remove(df.getOWLClass(OWLRDFVocabulary.RDFS_RESOURCE.getIRI()));
        superClasses = filterByNamespace(superClasses);
        logger.info("Super classes: " + superClasses);

        int limit = (int) Math
                .ceil(((double) frequency / positiveExamplesTypes.size()) / superClasses.size() * cnt);
        //get instances for each super class
        for (OWLClassExpression superClass : superClasses) {
            SortedSet<OWLIndividual> individuals = sr.getIndividualsExcluding(superClass, nc, totalCnt);
            individuals.removeAll(negativeExamples);
            individuals.removeAll(negExamples);
            SetManipulation.stableShrink(individuals, limit);
            negExamples.addAll(individuals);
        }/*from ww  w.  j a v a2  s. c om*/
    }
    negExamples = SetManipulation.stableShrink(negExamples, cnt);
    logger.info("Negative examples(" + negExamples.size() + "): " + negExamples);
    return negExamples;
}

---

From source file:org.sonar.plugins.core.sensors.ViolationsDecorator.java

private void computeViolationsPerSeverities(DecoratorContext context) {
    Multiset<RulePriority> severitiesBag = HashMultiset.create();
    for (Violation violation : context.getViolations()) {
        severitiesBag.add(violation.getSeverity());
    }//  ww w  .  j  ava  2  s  .  c om

    for (RulePriority severity : RulePriority.values()) {
        Metric metric = SeverityUtils.severityToViolationMetric(severity);
        if (context.getMeasure(metric) == null) {
            Collection<Measure> children = context.getChildrenMeasures(MeasuresFilters.metric(metric));
            int sum = MeasureUtils.sum(true, children).intValue() + severitiesBag.count(severity);
            context.saveMeasure(metric, (double) sum);
        }
    }
}

---

From source file:org.softinica.maven.jmeter.report.analyser.ThroughputAnalyzer.java

@Override
protected JFreeChart createChart(PageDefinition definition, Input input) {
    Map<String, Multiset<Long>> allSeries = new HashMap<String, Multiset<Long>>();
    for (Sample sample : input.getSamples()) {
        Multiset<Long> rps = allSeries.get(sample.getLabel());
        if (rps == null) {
            rps = TreeMultiset.create();
            allSeries.put(sample.getLabel(), rps);
        }/*from w w  w .ja v a  2 s .c  om*/
        rps.add(((sample.getTimestamp() + (long) sample.getValue()) / 1000) * 1000);
    }
    TimeSeriesCollection dataset = new TimeSeriesCollection();
    for (String label : allSeries.keySet()) {
        Multiset<Long> rps = allSeries.get(label);
        TimeSeries series = new TimeSeries(label);
        for (Long key : rps) {
            series.addOrUpdate(new Second(new Date(key)), rps.count(key));
        }
        dataset.addSeries(series);
    }

    return ChartFactory.createTimeSeriesChart(definition.getTitle(), "Time", "Requests/second", dataset);
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• »