Example usage for java.util List indexOf

List of usage examples for java.util List indexOf

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Introduction

In this page you can find the example usage for java.util List indexOf.

Prototype

int indexOf(Object o);

Source Link

Document

Returns the index of the first occurrence of the specified element in this list, or -1 if this list does not contain the element.

Usage

From source file:com.cloudera.oryx.rdf.common.pmml.DecisionForestPMML.java

/**
 * @param pmmlFile file to read PMML encoding from
 * @return a {@link DecisionForest} representation of the PMML encoded model
 *///  www  .  jav  a2s  . c o m
public static Pair<DecisionForest, Map<Integer, BiMap<String, Integer>>> read(File pmmlFile)
        throws IOException {

    PMML pmml;
    InputStream in = IOUtils.openMaybeDecompressing(pmmlFile);
    try {
        pmml = IOUtil.unmarshal(in);
    } catch (SAXException e) {
        throw new IOException(e);
    } catch (JAXBException e) {
        throw new IOException(e);
    } finally {
        in.close();
    }

    List<Model> models = pmml.getModels();
    Preconditions.checkNotNull(models);
    Preconditions.checkArgument(!models.isEmpty());
    Preconditions.checkArgument(models.get(0) instanceof MiningModel);
    MiningModel miningModel = (MiningModel) models.get(0);

    Segmentation segmentation = miningModel.getSegmentation();
    Preconditions.checkNotNull(segmentation);

    List<Segment> segments = segmentation.getSegments();
    Preconditions.checkNotNull(segments);
    Preconditions.checkArgument(!segments.isEmpty());

    Map<Integer, BiMap<String, Integer>> columnToCategoryNameToIDMapping = PMMLUtils
            .buildColumnCategoryMapping(pmml.getDataDictionary());
    InboundSettings settings = InboundSettings.create(ConfigUtils.getDefaultConfig());
    DecisionTree[] trees = new DecisionTree[segments.size()];
    double[] weights = new double[trees.length];
    for (int i = 0; i < trees.length; i++) {
        Segment segment = segments.get(i);
        weights[i] = segment.getWeight();
        TreeModel treeModel = (TreeModel) segment.getModel();
        TreeNode root = translateFromPMML(treeModel.getNode(), columnToCategoryNameToIDMapping, settings);
        trees[i] = new DecisionTree(root);
    }

    List<String> columnNames = settings.getColumnNames();
    List<MiningField> fields = miningModel.getMiningSchema().getMiningFields();
    double[] featureImportances = new double[fields.size()];
    for (MiningField field : fields) {
        Double importance = field.getImportance();
        if (importance != null) {
            int featureNumber = columnNames.indexOf(field.getName().getValue());
            featureImportances[featureNumber] = importance;
        }
    }

    return new Pair<DecisionForest, Map<Integer, BiMap<String, Integer>>>(
            new DecisionForest(trees, weights, featureImportances), columnToCategoryNameToIDMapping);
}

From source file:de.tudarmstadt.ukp.dkpro.tc.core.util.ReportUtils.java

/**
 * Creates a confusion matrix by collecting the results from the overall CV run stored in
 * {@code tempM}//w  w  w . ja va  2 s  .  co m
 * 
 * @param tempM
 * @param actualLabelsList
 *            the label powerset transformed list of actual/true labels
 * @param predictedLabelsList
 *            the label powerset transformed list of predicted labels
 * @return
 */
public static double[][] createConfusionMatrix(HashMap<String, Map<String, Integer>> tempM,
        List<String> actualLabelsList, List<String> predictedLabelsList) {
    double[][] matrix = new double[actualLabelsList.size()][predictedLabelsList.size()];

    Iterator<String> actualsIter = tempM.keySet().iterator();
    while (actualsIter.hasNext()) {
        String actual = actualsIter.next();
        Iterator<String> predsIter = tempM.get(actual).keySet().iterator();
        while (predsIter.hasNext()) {
            String pred = predsIter.next();
            int a = actualLabelsList.indexOf(actual);
            int p = predictedLabelsList.indexOf(pred);
            matrix[a][p] = tempM.get(actual).get(pred);
        }
    }
    return matrix;
}

From source file:it.eng.spagobi.engines.worksheet.bo.WorkSheetDefinition.java

public static Map<String, List<String>> mergeDomainValuesFilters(List<Attribute> globalFilters,
        List<Attribute> sheetFilters) throws WrongConfigurationForFiltersOnDomainValuesException {
    Iterator<Attribute> globalFiltersIt = globalFilters.iterator();
    Map<String, List<String>> toReturn = new HashMap<String, List<String>>();
    while (globalFiltersIt.hasNext()) {
        Attribute aGlobalFilter = globalFiltersIt.next();
        if (sheetFilters.contains(aGlobalFilter)) { // the filter is defined globally and also on sheets
            // wins the more restrictive filter
            int index = sheetFilters.indexOf(aGlobalFilter);
            Attribute sheetsFilter = sheetFilters.get(index);
            List<String> aGlobalFilterValues = aGlobalFilter.getValuesAsList();
            List<String> sheetsFilterValues = sheetsFilter.getValuesAsList();
            if (aGlobalFilterValues.containsAll(sheetsFilterValues)) {
                // the sheets filters are less or equal to the global filters (this should always happen)
                toReturn.put(aGlobalFilter.getEntityId(), sheetsFilterValues);
            } else {
                logger.error("The global filter on field " + aGlobalFilter.getAlias()
                        + " is overridden by the sheet.");
                throw new WrongConfigurationForFiltersOnDomainValuesException(
                        "The global filter on field " + aGlobalFilter.getAlias()
                                + " is overridden by the sheet. Please expand the global filter's values.");
            }//from   w w  w . java 2  s.co  m
        } else {
            toReturn.put(aGlobalFilter.getEntityId(), aGlobalFilter.getValuesAsList());
        }
    }
    Iterator<Attribute> sheetFiltersIt = sheetFilters.iterator();
    while (sheetFiltersIt.hasNext()) {
        Attribute aSheetsFilter = sheetFiltersIt.next();
        if (toReturn.containsKey(aSheetsFilter.getEntityId())) {
            // conflict already solved
            continue;
        }
        toReturn.put(aSheetsFilter.getEntityId(), aSheetsFilter.getValuesAsList());
    }
    return toReturn;
}

From source file:edu.cmu.tetrad.search.IndTestMultiFisherZ.java

/**
 * Returns the submatrix of m with variables in the order of the x variables.
 *//*from   w w  w  .java  2 s  . com*/
public static TetradMatrix subMatrix(ICovarianceMatrix m, List<Node> x, List<Node> y, List<Node> z) {
    List<Node> variables = m.getVariables();
    TetradMatrix _covMatrix = m.getMatrix();

    // Create index array for the given variables.
    int[] indices = new int[x.size() + y.size() + z.size()];

    for (int i = 0; i < x.size(); i++) {
        indices[i] = variables.indexOf(x.get(i));
    }

    for (int i = 0; i < y.size(); i++) {
        indices[x.size() + i] = variables.indexOf(y.get(i));
    }

    for (int i = 0; i < z.size(); i++) {
        indices[x.size() + y.size() + i] = variables.indexOf(z.get(i));
    }

    // Extract submatrix of correlation matrix using this index array.
    TetradMatrix submatrix = _covMatrix.getSelection(indices, indices);

    return submatrix;
}

From source file:net.minecraftforge.fml.relauncher.libraries.LibraryManager.java

public static List<Artifact> flattenLists(File mcDir) {
    List<Artifact> merged = new ArrayList<>();
    for (ModList list : ModList.getBasicLists(mcDir)) {
        for (Artifact art : list.flatten()) {
            Optional<Artifact> old = merged.stream().filter(art::matchesID).findFirst();
            if (!old.isPresent()) {
                merged.add(art);//from   w w w .  java2 s .com
            } else if (old.get().getVersion().compareTo(art.getVersion()) < 0) {
                merged.add(merged.indexOf(old.get()), art);
                merged.remove(old.get());
            }
        }
    }
    return merged;
}

From source file:com.bluexml.side.m2.repositoryBuilder.Builder.java

private static void applyOptionalJob(File rootPlugins, String option, String filePath)
        throws Exception, IOException {
    if (option.equals("-makeDot")) {
        writeDotFile(rootPlugins, filePath);
        System.out.println("graph dot file generated :" + filePath);
    } else if (option.equals("-patchPom")) {
        List<ModuleConstraint> res = Builder.getModulesConstraint(rootPlugins);
        // add dependency plugin used by SIDE
        ModuleConstraint McForMavenDependeciesPlugins = new ModuleConstraint(
                "org.apache.maven.plugins.maven-dependency-plugin", null, "maven-plugin", "2.0", "2.0");
        res.add(McForMavenDependeciesPlugins);

        File file = new File(filePath);
        if (!file.exists()) {
            System.err.println("File do not exist :" + file);
        }/*from  w  w w  .j av  a  2s. c  o m*/
        List<File> poms = new ArrayList<File>();
        for (ModuleConstraint moduleConstraint : res) {
            File destFile = new File(file.getParent(), res.indexOf(moduleConstraint) + "_.pom");
            FileUtils.copyFile(file, destFile);
            List<ModuleConstraint> res_ = new ArrayList<ModuleConstraint>();
            res_.add(moduleConstraint);
            writePomFile(destFile, res_);
            System.out.println("pom Patched" + destFile);
            poms.add(destFile);
        }
        // write pom.sh script that call go-offline for each patched poms
        Builder.writeShellScript(new File(file.getParent(), "pom.sh"), poms);
    }
}

From source file:com.mh.commons.utils.Reflections.java

/**
 * ?? trim() /*www .ja va2s.  c o m*/
 * ?? trim();  ??
 * @param obj
 * @param escapeList ??trim()
 * @return
 */
public static Object trim(Object obj, List<String> escapeList) {
    if (obj == null)
        return null;
    try {
        Field[] fields = obj.getClass().getDeclaredFields();
        if (fields != null && fields.length > 0) {
            for (Field field : fields) {
                if (field.getModifiers() < 15 && field.getType().toString().equals("class java.lang.String")) {
                    Object val = FieldUtils.readField(field, obj, true);
                    if (val != null) {
                        if (escapeList != null && escapeList.indexOf(field.getName()) != -1)
                            continue;
                        FieldUtils.writeField(field, obj, val.toString().trim(), true);
                    }
                }
            }
        }
    } catch (SecurityException e) {
        e.printStackTrace();
    } catch (IllegalAccessException e) {
        e.printStackTrace();
    }
    return obj;
}

From source file:de.tudarmstadt.ukp.similarity.experiments.coling2012.util.Evaluator.java

@SuppressWarnings("unchecked")
public static void runEvaluationMetric(WekaClassifier wekaClassifier, EvaluationMetric metric, Dataset dataset)
        throws IOException {
    StringBuilder sb = new StringBuilder();

    List<String> gold = ColingUtils.readGoldstandard(dataset);
    List<String> exp = FileUtils.readLines(
            new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/output.csv"));

    if (metric.equals(EvaluationMetric.Accuracy)) {
        double acc = 0.0;

        for (int i = 0; i < gold.size(); i++) {
            if (gold.get(i).equals(exp.get(i)))
                acc++;/*w w  w . j  ava 2  s.  c  om*/
        }

        acc /= gold.size();

        sb.append(acc);
    } else if (metric.equals(EvaluationMetric.AverageF1)) {
        // Get all classes
        Set<String> classesSet = new HashSet<String>();
        for (String cl : gold)
            classesSet.add(cl);

        // Order the classes
        List<String> classes = new ArrayList<String>(classesSet);

        // Initialize confusion matrix
        // exp\class   A   B
        // A         x1   x2
        // B         x3   x4         
        int[][] matrix = new int[classes.size()][classes.size()];

        // Initialize matrix
        for (int i = 0; i < classes.size(); i++)
            for (int j = 0; j < classes.size(); j++)
                matrix[i][j] = 0;

        // Construct confusion matrix
        for (int i = 0; i < gold.size(); i++) {
            int goldIndex = classes.indexOf(gold.get(i));
            int expIndex = classes.indexOf(exp.get(i));

            matrix[goldIndex][expIndex] += 1;
        }

        // Compute precision and recall per class
        double[] prec = new double[classes.size()];
        double[] rec = new double[classes.size()];

        for (int i = 0; i < classes.size(); i++) {
            double tp = matrix[i][i];
            double fp = 0.0;
            double fn = 0.0;

            // FP
            for (int j = 0; j < classes.size(); j++) {
                if (i == j)
                    continue;

                fp += matrix[j][i];
            }

            // FN
            for (int j = 0; j < classes.size(); j++) {
                if (i == j)
                    continue;

                fn += matrix[i][j];
            }

            // Save
            prec[i] = tp / (tp + fp);
            rec[i] = tp / (tp + fn);
        }

        // Compute average F1 score across all classes
        double f1 = 0.0;

        for (int i = 0; i < classes.size(); i++) {
            double f1PerClass = (2 * prec[i] * rec[i]) / (prec[i] + rec[i]);
            f1 += f1PerClass;
        }

        f1 = f1 / classes.size();

        // Output
        sb.append(f1);
    }

    FileUtils.writeStringToFile(new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString()
            + "/" + metric.toString() + ".txt"), sb.toString());
}

From source file:edu.wpi.checksims.algorithm.similaritymatrix.SimilarityMatrix.java

/**
 * Generate a similarity matrix from a given set of submissions.
 *
 * @param inputSubmissions Submissions to generate from
 * @param results Results to build from. Must contain results for every possible unordered pair of input submissions
 * @return Similarity Matrix built from given results
 * @throws InternalAlgorithmError Thrown on missing results, or results containing a submission not in the input
 *///from   w  w w. j av  a  2  s. c om
public static SimilarityMatrix generateMatrix(Set<Submission> inputSubmissions, Set<AlgorithmResults> results)
        throws InternalAlgorithmError {
    checkNotNull(inputSubmissions);
    checkNotNull(results);
    checkArgument(!inputSubmissions.isEmpty(), "Must provide at least 1 submission to build matrix from");
    checkArgument(!results.isEmpty(), "Must provide at least 1 AlgorithmResults to build matrix from!");

    // Generate the matrix we'll use
    MatrixEntry[][] matrix = new MatrixEntry[inputSubmissions.size()][inputSubmissions.size()];

    // Order the submissions
    List<Submission> orderedSubmissions = Ordering.natural().immutableSortedCopy(inputSubmissions);

    // Generate the matrix

    // Start with the diagonal, filling with 100% similarity
    for (int i = 0; i < orderedSubmissions.size(); i++) {
        Submission s = orderedSubmissions.get(i);

        matrix[i][i] = new MatrixEntry(s, s, s.getNumTokens());
    }

    // Now go through all the results, and build appropriate two MatrixEntry objects for each
    for (AlgorithmResults result : results) {
        int aIndex = orderedSubmissions.indexOf(result.a);
        int bIndex = orderedSubmissions.indexOf(result.b);

        if (aIndex == -1) {
            throw new InternalAlgorithmError(
                    "Processed Algorithm Result with submission not in given input submissions with name \""
                            + result.a.getName() + "\"");
        } else if (bIndex == -1) {
            throw new InternalAlgorithmError(
                    "Processed Algorithm Result with submission not in given input submissions with name \""
                            + result.b.getName() + "\"");
        }

        matrix[aIndex][bIndex] = new MatrixEntry(result.a, result.b, result.identicalTokensA);
        matrix[bIndex][aIndex] = new MatrixEntry(result.b, result.a, result.identicalTokensB);
    }

    // Verification pass: Go through and ensure that the entire array was populated
    for (int x = 0; x < orderedSubmissions.size(); x++) {
        for (int y = 0; y < orderedSubmissions.size(); y++) {
            if (matrix[x][y] == null) {
                throw new InternalAlgorithmError("Missing Algorithm Results for comparison of submissions \""
                        + orderedSubmissions.get(x).getName() + "\" and \""
                        + orderedSubmissions.get(y).getName() + "\"");
            }
        }
    }

    return new SimilarityMatrix(matrix, orderedSubmissions, orderedSubmissions, results);
}

From source file:net.lldp.checksims.algorithm.similaritymatrix.SimilarityMatrix.java

/**
 * Generate a similarity matrix from a given set of submissions.
 *
 * @param inputSubmissions Submissions to generate from
 * @param results Results to build from. Must contain results for every possible unordered pair of input submissions
 * @return Similarity Matrix built from given results
 * @throws InternalAlgorithmError Thrown on missing results, or results containing a submission not in the input
 */// ww  w . j a  v a  2s . com
public static SimilarityMatrix generateMatrix(Set<Submission> inputSubmissions, Set<AlgorithmResults> results)
        throws InternalAlgorithmError {
    checkNotNull(inputSubmissions);
    checkNotNull(results);
    checkArgument(!inputSubmissions.isEmpty(), "Must provide at least 1 submission to build matrix from");
    checkArgument(!results.isEmpty(), "Must provide at least 1 AlgorithmResults to build matrix from!");

    // Generate the matrix we'll use
    AlgorithmResults[][] matrix = new AlgorithmResults[inputSubmissions.size()][inputSubmissions.size()];

    //Ordering sortBy = Ordering.natural();
    Ordering<Submission> sortBy = Ordering.from(new Comparator<Submission>() {
        public int compare(Submission a, Submission b) {
            return ((Double) b.getTotalCopyScore()).compareTo(a.getTotalCopyScore());
        }
    });

    // Order the submissions
    List<Submission> orderedSubmissions = sortBy.immutableSortedCopy(inputSubmissions);

    // Generate the matrix

    // Start with the diagonal, filling with 100% similarity
    for (int i = 0; i < orderedSubmissions.size(); i++) {
        Submission s = orderedSubmissions.get(i);

        matrix[i][i] = new AlgorithmResults(Pair.of(s, s), Real.ONE, Real.ONE);
    }

    // Now go through all the results, and build appropriate two MatrixEntry objects for each
    for (AlgorithmResults result : results) {
        int aIndex = orderedSubmissions.indexOf(result.a);
        int bIndex = orderedSubmissions.indexOf(result.b);

        if (aIndex == -1) {
            if (!result.a.testFlag("invalid")) {
                throw new InternalAlgorithmError(
                        "Processed Algorithm Result with submission not in given input submissions with name \""
                                + result.a.getName() + "\"");
            }
        } else if (bIndex == -1) {
            if (!result.b.testFlag("invalid")) {
                throw new InternalAlgorithmError(
                        "Processed Algorithm Result with submission not in given input submissions with name \""
                                + result.b.getName() + "\"");
            }
        } else {
            matrix[aIndex][bIndex] = result.inverse();
            matrix[bIndex][aIndex] = result;
        }
    }

    // Verification pass: Go through and ensure that the entire array was populated
    for (int x = 0; x < orderedSubmissions.size(); x++) {
        for (int y = 0; y < orderedSubmissions.size(); y++) {
            if (matrix[x][y] == null) {
                throw new InternalAlgorithmError("Missing Algorithm Results for comparison of submissions \""
                        + orderedSubmissions.get(x).getName() + "\" and \""
                        + orderedSubmissions.get(y).getName() + "\"");
            }
        }
    }

    return new SimilarityMatrix(matrix, orderedSubmissions, orderedSubmissions, results);
}