Example usage for org.apache.commons.lang ArrayUtils toObject

Introduction

In this page you can find the example usage for org.apache.commons.lang ArrayUtils toObject.

Prototype

public static Boolean[] toObject(boolean[] array)

Source Link

Document

Converts an array of primitive booleans to objects.

Usage

From source file:org.dkpro.tc.ml.report.util.ScatterplotRenderer.java

private double getMax(double[] values) {
    return Collections.max(Arrays.asList(ArrayUtils.toObject(values)));
}

From source file:org.drugis.addis.util.convertors.NetworkMetaAnalysisConverter.java

private static ConsistencyResults convertConsistencyResults(NetworkMetaAnalysis ma) {
    ConsistencyResults results = new ConsistencyResults();
    ConsistencyWrapper model = ma.getConsistencyModel();
    if (model.isApproved()) {
        results.setMcmcSettings(convertMCMCSettings(model));
        convertParameterSummaries(ma, model, results.getSummary());
        results.setRelativeEffectsQuantileSummary(convertRelativeEffectQuantileSummaries(ma, model));
        RelativeEffectsSummary relativeEffectSummary = new RelativeEffectsSummary();
        List<Double> list = relativeEffectSummary.getCovariance();
        double[][] matrix = model.getRelativeEffectsSummary().getCovarianceMatrix();
        for (int row = 0; row < matrix.length; ++row) {
            for (int col = row; col < matrix.length; ++col) {
                list.add(matrix[row][col]);
            }/*  w w  w  .  j  a  v a 2  s  .c o  m*/
        }
        List<Double> meanVector = Arrays
                .asList(ArrayUtils.toObject(model.getRelativeEffectsSummary().getMeanVector()));
        relativeEffectSummary.getMeans().addAll(meanVector);
        results.setRelativeEffectsSummary(relativeEffectSummary);

        RankProbabilitySummary rankProbabilities = model.getRankProbabilities();
        int rankProababilitySize = rankProbabilities.getTreatments().size();
        for (int row = 0; row < rankProababilitySize; ++row) {
            for (int col = 0; col < rankProababilitySize; ++col) {
                results.getRankProbabilitySummary()
                        .add(rankProbabilities.getValue(rankProbabilities.getTreatments().get(col), row + 1));
            }
        }
        return results;
    }
    return null;
}

From source file:org.drugis.addis.util.jaxb.NetworkMetaAnalysisConverter.java

private static ConsistencyResults convertConsistencyResults(NetworkMetaAnalysis ma) {
    ConsistencyResults results = new ConsistencyResults();
    ConsistencyWrapper<TreatmentDefinition> model = ma.getConsistencyModel();
    if (model.isApproved()) {
        results.setMcmcSettings(convertMCMCSettings(model));
        convertParameterSummaries(ma, model, results.getSummary());
        results.setRelativeEffectsQuantileSummary(convertRelativeEffectQuantileSummaries(ma, model));
        RelativeEffectsSummary relativeEffectSummary = new RelativeEffectsSummary();
        List<Double> list = relativeEffectSummary.getCovariance();
        double[][] matrix = model.getRelativeEffectsSummary().getCovarianceMatrix();
        for (int row = 0; row < matrix.length; ++row) {
            for (int col = row; col < matrix.length; ++col) {
                list.add(matrix[row][col]);
            }/*from   w  w w  .j av  a 2 s  .co m*/
        }
        List<Double> meanVector = Arrays
                .asList(ArrayUtils.toObject(model.getRelativeEffectsSummary().getMeanVector()));
        relativeEffectSummary.getMeans().addAll(meanVector);
        results.setRelativeEffectsSummary(relativeEffectSummary);

        RankProbabilitySummary rankProbabilities = model.getRankProbabilities();
        int rankProababilitySize = rankProbabilities.getTreatments().size();
        for (int row = 0; row < rankProababilitySize; ++row) {
            for (int col = 0; col < rankProababilitySize; ++col) {
                results.getRankProbabilitySummary()
                        .add(rankProbabilities.getValue(rankProbabilities.getTreatments().get(col), row + 1));
            }
        }
        return results;
    }
    return null;
}

From source file:org.eclipse.january.dataset.OutlierCorrectnessTest.java

@Before
public void setUp() throws Exception {
    Random.seed(2468);//w w w  . ja v  a  2s  .c  o m
    dataNormal = Random.randn(1.0, 1.0, new int[] { 6 });
    dataNormal.sort(null);
    dataOneToFour = DatasetFactory
            .createFromList(Arrays.asList(ArrayUtils.toObject(new double[] { 1, 2, 3, 4 })));
    System.out.println("Normal data " + dataNormal.toString(true));
    System.out.println("data 1-4 " + dataOneToFour.toString(true));
}

From source file:org.eclipse.triquetrum.scisoft.analysis.rpc.flattening.FlatteningTestAbstract.java

@Test
public void testPrimitiveArrays() {
    int[] ints = { 1, 5, -7 };
    flattenAndUnflatten(ints);/*from w  w  w  .j  a va 2 s .co m*/
    flattenAndUnflatten(ArrayUtils.toObject(ints), ints);

    double[] doubles = { 1.4, 12.6, 0 };
    flattenAndUnflatten(doubles);
    flattenAndUnflatten(ArrayUtils.toObject(doubles), doubles);

    boolean[] booleans = { true, false, false, true };
    flattenAndUnflatten(booleans);
    flattenAndUnflatten(ArrayUtils.toObject(booleans), booleans);

    double[][] doubles2d = { { 1, 5, -7 }, { 1.4, 12.6, 0 } };
    flattenAndUnflatten(doubles2d, new double[][] { doubles2d[0], doubles2d[1] });
    flattenAndUnflatten(new double[][] { doubles2d[0], doubles2d[1] });
}

From source file:org.eclipse.triquetrum.scisoft.analysis.rpc.flattening.helpers.PrimitiveArrayHelper.java

@Override
public Object flatten(Object obj, IRootFlattener rootFlattener) {
    if (obj instanceof int[]) {
        return ArrayUtils.toObject((int[]) obj);
    } else if (obj instanceof boolean[]) {
        return ArrayUtils.toObject((boolean[]) obj);
    } else if (obj instanceof double[]) {
        return ArrayUtils.toObject((double[]) obj);
    }//from  w w w .ja v a2s  .  c o m
    throw new AssertionError();
}

From source file:org.eclipse.wb.internal.core.model.property.editor.IntegerArrayPropertyEditor.java

@Override
public String getText(Property property) throws Exception {
    Object value = property.getValue();
    if (value instanceof int[]) {
        int[] array = (int[]) value;
        return StringUtils.join(ArrayUtils.toObject(array), ' ');
    }/*w ww  .ja v a 2  s .  c  o m*/
    return null;
}

From source file:org.eclipse.wb.internal.xwt.model.widgets.SashFormInfo.java

/**
 * Sets the "weights" attribute.//  w w  w.  java 2s .c o m
 */
private void setWeights(int[] weights) throws Exception {
    String weightsString = StringUtils.join(ArrayUtils.toObject(weights), ", ");
    getCreationSupport().getElement().setAttribute("weights", weightsString);
}

From source file:org.epochx.ge.model.epox.EvenParity.java

/**
 * Calculates the fitness score for the given program. The fitness of a
 * program for the even-parity problem is calculated by evaluating it
 * using each of the possible sets of input values. There are
 * <code>2^noInputBits</code> possible sets of inputs. The fitness of the
 * program is the quantity of those input sequences that the program
 * returned an incorrect response for. That is, a fitness value of
 * <code>0.0</code> indicates the program responded correctly for every
 * possible set of input values.//from w  w w.j a  v a2s . c o m
 * 
 * @param p {@inheritDoc}
 * @return the calculated fitness for the given program.
 */
@Override
public double getFitness(final CandidateProgram p) {
    final GECandidateProgram program = (GECandidateProgram) p;

    double score = 0;

    // Evaluate all possible inputValues.
    for (final boolean[] vars : inputValues) {
        // Convert to object array.
        final Boolean[] objVars = ArrayUtils.toObject(vars);

        Boolean result = null;
        try {
            result = (Boolean) interpreter.eval(program.getSourceCode(), argNames, objVars);
        } catch (final MalformedProgramException e) {
            // Assign worst possible fitness and stop evaluating.
            score = 0;
            break;
        }

        // Increment score for a correct response.
        if ((result != null) && (result == isEvenNoTrue(vars))) {
            score++;
        } else if (!program.isValid()) {
            score = 0;
            break;
        }
    }

    return inputValues.length - score;
}

From source file:org.epochx.ge.model.epox.Majority.java

/**
 * Calculates the fitness score for the given program. The fitness of a
 * program for the majority problem is calculated by evaluating it
 * using each of the possible sets of input values. There are
 * <code>2^noInputBits</code> possible sets of inputs. The fitness of the
 * program is the quantity of those input sequences that the program
 * returned an incorrect response for. That is, a fitness value of
 * <code>0.0</code> indicates the program responded correctly for every
 * possible set of input values.//from   w  ww.  j av a 2s. c o  m
 * 
 * @param p {@inheritDoc}
 * @return the calculated fitness for the given program.
 */
@Override
public double getFitness(final CandidateProgram p) {
    final GECandidateProgram program = (GECandidateProgram) p;

    double score = 0;

    // Evaluate all possible inputValues.
    for (final boolean[] vars : inputValues) {
        // Convert to object array.
        final Boolean[] objVars = ArrayUtils.toObject(vars);

        Boolean result = null;
        try {
            result = (Boolean) interpreter.eval(program.getSourceCode(), argNames, objVars);
        } catch (final MalformedProgramException e) {
            // Assign worst possible fitness and stop evaluating.
            score = 0;
            break;
        }

        if ((result != null) && (result == majorityTrue(vars))) {
            score++;
        }
    }

    return inputValues.length - score;
}