Example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix.

Prototype

public Array2DRowRealMatrix(final double[] v) 

Source Link

Document

Create a new (column) RealMatrix using v as the data for the unique column of the created matrix.

Usage

From source file:com.itemanalysis.psychometrics.factoranalysis.GPArotationTest.java

@Test
public void testM255Olimin() {
    System.out.println("Oblimin rotation test: m255 data");

    /**/* w ww .j  a  va  2 s. co m*/
     * True result obtained form R using GPArotation package
     */
    double[][] true_Oblimin = { { 0.818982935, -0.086728237, -0.03014828 },
            { 0.836948168, -0.028356336, 0.02072749 }, { 0.650083626, 0.102202784, -0.06494440 },
            { 0.609805424, 0.145551832, 0.03455997 }, { 0.432053641, 0.310356386, -0.13539510 },
            { 0.071824049, 0.723712703, -0.07186957 }, { -0.022431888, 0.822701356, 0.05997316 },
            { 0.113162767, 0.434661061, -0.07100987 }, { 0.129672215, 0.353260863, -0.33144976 },
            { -0.052966793, 0.454048208, -0.28827799 }, { 0.028011398, -0.006402917, -0.89108626 },
            { -0.005289128, 0.004084979, -0.75198003 } };

    RealMatrix L = new Array2DRowRealMatrix(m255MINRESLoadings);
    GPArotation gpa = new GPArotation();
    RotationResults R = gpa.rotate(L, RotationMethod.OBLIMIN, false, 60, 1e-5);
    RealMatrix Lr = R.getFactorLoadings();
    //        System.out.println(R.toString());

    for (int i = 0; i < Lr.getRowDimension(); i++) {
        for (int j = 0; j < Lr.getColumnDimension(); j++) {
            assertEquals("  loading: ", Precision.round(true_Oblimin[i][j], 4),
                    Precision.round(Lr.getEntry(i, j), 5), 1e-4);
        }

    }

}

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From source file:com.clust4j.algo.HierarchicalTests.java

@Test
public void testKernel() {
    final double[][] data = new double[][] { new double[] { 3.65816, 0.29518, 2.123316 },
            new double[] { 0.005, 0.182751, 0.1284 }, new double[] { 4.1234, 0.27395, 1.8900002 } };

    int[] labels;
    final Array2DRowRealMatrix mat = new Array2DRowRealMatrix(data);
    for (Linkage linkage : HierarchicalAgglomerative.Linkage.values()) {
        HierarchicalAgglomerative hac = new HierarchicalAgglomerative(mat,
                new HierarchicalAgglomerativeParameters().setLinkage(linkage).setMetric(new GaussianKernel())
                        .setVerbose(false)).fit();

        labels = hac.getLabels();//  w  ww.j  ava  2 s.c  om
        assertTrue(labels[0] == labels[2]);
    }
}

---

From source file:edu.cudenver.bios.power.test.paper.TestConditionalUnivariateWithConfidenceLimits.java

/**
 * Compare 2 sample t-test results between JavaStatistics,
 * POWERLIB, and simulation//from w w  w. j av a 2  s.co m
 */
public void testUnivariateWithConfidenceLimits() {
    GLMMPowerParameters params = new GLMMPowerParameters();

    // add tests
    params.addTest(Test.UNIREP);

    // add alpha values
    params.addAlpha(0.01);

    // build beta matrix
    double[][] beta = { { 0 }, { 1 } };
    params.setBeta(new FixedRandomMatrix(beta, null, false));
    // add beta scale values from 0 to 0.75
    for (double betaScale = 0; betaScale < 0.76; betaScale += 0.01)
        params.addBetaScale(betaScale);

    // build theta null matrix
    double[][] theta0 = { { 0 } };
    params.setTheta(new Array2DRowRealMatrix(theta0));

    // build sigma matrix
    double[][] sigma = { { 0.068 } };
    params.setSigmaError(new Array2DRowRealMatrix(sigma));
    // add sigma scale values
    params.addSigmaScale(1);

    // build design matrix
    params.setDesignEssence(MatrixUtils.createRealIdentityMatrix(2));
    // add sample size multipliers
    params.addSampleSize(12);

    // build between subject contrast
    double[][] between = { { 1, -1 } };
    params.setBetweenSubjectContrast(new FixedRandomMatrix(between, null, true));

    // parameters for confidence limits
    params.setConfidenceIntervalType(ConfidenceIntervalType.BETA_KNOWN_SIGMA_ESTIMATED);
    params.setSampleSizeForEstimates(24);
    params.setDesignMatrixRankForEstimates(2);

    // run the test
    // 2 sided CI
    params.setAlphaLowerConfidenceLimit(0.025);
    params.setAlphaUpperConfidenceLimit(0.025);
    checker.checkPower(params);
    // 1 sided lower CI
    params.setAlphaLowerConfidenceLimit(0.05);
    params.setAlphaUpperConfidenceLimit(0);
    checker.checkPower(params);
    // 1 sided upper CI
    params.setAlphaLowerConfidenceLimit(0);
    params.setAlphaUpperConfidenceLimit(0.05);
    checker.checkPower(params);
    // output the results
    try {
        ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
        reportBuilder.createValidationReportAsStdout(checker, TITLE, false);
        reportBuilder.createValidationReportAsLaTex(OUTPUT_FILE, TITLE, AUTHOR, STUDY_DESIGN_DESCRIPTION,
                params, checker);
    } catch (Exception e) {
        System.err.println(e.getMessage());
    }

    assertTrue(checker.isSASDeviationBelowTolerance());
    checker.reset();
}

---

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurementTest.java

@Test(expected = IllegalArgumentException.class)
public void testWrongSizeCovarianceMatrix() {
    RealMatrix sigma = new Array2DRowRealMatrix(new double[][] { { 1.5, 0.0 }, { 1.0, 0.5 }, { 1.0, 0.02 } });
    m.setCovarianceMatrix(sigma);//w  ww  . j av a  2  s.  c om
}

---

From source file:com.clust4j.algo.DBSCANTests.java

License:asdf

@Test
public void DBSCANTest3() {
    final double[][] train_array = new double[][] { new double[] { 0.00504, 0.0001, 0.08172 },
            new double[] { 3.65816, 2.9471, 3.12331 }, new double[] { 4.12344, 3.0001, 2.89002 },
            new double[] { 0.00403, 0.0003, 0.08231 } };

    final Array2DRowRealMatrix mat = new Array2DRowRealMatrix(train_array);
    StandardScaler scaler = new StandardScaler().fit(mat);

    assertTrue(Distance.EUCLIDEAN.getDistance(train_array[1], train_array[2]) > 0.5);
    DBSCAN db = new DBSCAN(scaler.transform(mat), new DBSCANParameters(0.75).setMinPts(1).setVerbose(true))
            .fit();//from   w  w  w.  j  a  v a 2s.com
    System.out.println();

    assertTrue(db.getNumberOfIdentifiedClusters() == 2);
    assertTrue(db.getLabels()[1] == db.getLabels()[2]);
    assertTrue(db.getLabels()[0] == db.getLabels()[3]);
    assertFalse(db.hasWarnings());
    assertTrue(db.getMinPts() == 1);
}

---

From source file:edu.cudenver.bios.power.test.paper.TestConditionalTwoSampleTTest.java

/**
 * Compare 2 sample t-test results between JavaStatistics, 
 * POWERLIB, and simulation/*from  w w w . j  av  a 2s . co m*/
 */
public void testTwoSampleTTEst() {
    GLMMPowerParameters params = new GLMMPowerParameters();

    // add tests
    params.addTest(Test.UNIREP);

    // add alpha values
    params.addAlpha(0.05);

    // build beta matrix
    double[][] beta = { { 0 }, { 1 } };
    params.setBeta(new FixedRandomMatrix(beta, null, false));
    // add beta scale values
    for (double betaScale = 0; betaScale <= 2.5; betaScale += 0.05)
        params.addBetaScale(betaScale);

    // build theta null matrix
    double[][] theta0 = { { 0 } };
    params.setTheta(new Array2DRowRealMatrix(theta0));

    // build sigma matrix
    double[][] sigma = { { 1 } };
    params.setSigmaError(new Array2DRowRealMatrix(sigma));
    // add sigma scale values
    for (double sigmaScale : SIGMA_SCALE_LIST)
        params.addSigmaScale(sigmaScale);

    // build design matrix
    params.setDesignEssence(MatrixUtils.createRealIdentityMatrix(2));
    // add sample size multipliers
    for (int sampleSize : SAMPLE_SIZE_LIST)
        params.addSampleSize(sampleSize);

    // build between subject contrast
    double[][] between = { { 1, -1 } };
    params.setBetweenSubjectContrast(new FixedRandomMatrix(between, null, true));

    try {
        checker.checkPower(params);
        // output the results
        ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
        reportBuilder.createValidationReportAsStdout(checker, TITLE, false);
        reportBuilder.createValidationReportAsLaTex(OUTPUT_FILE, TITLE, AUTHOR, STUDY_DESIGN_DESCRIPTION,
                params, checker);

        assertTrue(checker.isSASDeviationBelowTolerance());
        checker.reset();
    } catch (Exception e) {

    }
}

---

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java

@Override
public void reorderAlternatives(List<Alternative> alts) {
    final int n = alternatives.size();

    double permute[][] = new double[n][n];
    for (int i = 0; i < n; ++i) {
        int j = alternatives.indexOf(alts.get(i));
        permute[i][j] = 1;/*from w w w.  jav  a 2s  . c o  m*/
    }
    Array2DRowRealMatrix m = new Array2DRowRealMatrix(permute);

    alternatives.clear();
    alternatives.addAll(alts);

    final RealVector newMeanVector = m.operate(meanVector);
    final RealMatrix newCovarianceMatrix = covarianceMatrix.multiply(m.transpose()).preMultiply(m);
    fireEvents(setMeanVectorInternal(newMeanVector), setCovarianceMatrixInternal(newCovarianceMatrix));
}

---

From source file:com.joptimizer.solvers.KKTSolver.java

protected boolean checkKKTSolutionAccuracy(RealVector v, RealVector w) {
    // build the full KKT matrix
    double norm;/*  www .  j a v a2 s  .  c  om*/

    DoubleMatrix2D M = F2.make(this.H.getData());
    if (this.A != null) {
        if (h != null) {
            DoubleMatrix2D[][] parts = { { F2.make(this.H.getData()), F2.make(this.AT.getData()) },
                    { F2.make(this.A.getData()), null } };
            M = F2.compose(parts);
            RealMatrix KKT = new Array2DRowRealMatrix(M.toArray());
            RealVector X = v.append(w);
            RealVector Y = g.append(h);
            // check ||KKT.X+Y||<tolerance
            norm = KKT.operate(X).add(Y).getNorm();
        } else {
            //H.v + [A]T.w = -g
            norm = H.operate(v).add(AT.operate(w)).add(g).getNorm();
        }
    } else {
        // check ||H.X+h||<tolerance
        norm = H.operate(v).add(g).getNorm();
    }
    Log.d(MainActivity.JOPTIMIZER_LOGTAG, "KKT solution error: " + norm);
    return norm < toleranceKKT;
}

---

From source file:eagle.security.userprofile.model.UserProfileEigenModelEntity.java

public static UserProfileEigenModel deserializeModel(MLModelAPIEntity entity) throws IOException {
    if (entity == null)
        return null;
    String user = entity.getTags() == null ? null : entity.getTags().get(UserProfileConstants.USER_TAG);
    String site = entity.getTags() == null ? null : entity.getTags().get(UserProfileConstants.SITE_TAG);

    UserProfileEigenModelEntity content = TaggedLogAPIEntity.buildObjectMapper().readValue(entity.getContent(),
            UserProfileEigenModelEntity.class);
    RealVector[] principalComponents = null;
    if (content.getPrincipalComponents() != null) {
        principalComponents = new RealVector[content.getPrincipalComponents().size()];
        int i = 0;
        for (double[] array : content.getPrincipalComponents()) {
            principalComponents[i] = new ArrayRealVector(array);
            i++;/*  w  ww. j  a v  a 2 s  . c  o  m*/
        }
    }
    UserCommandStatistics[] statistics = null;
    if (content.getStatistics() != null) {
        statistics = new UserCommandStatistics[content.getStatistics().size()];
        int i = 0;
        for (Map<String, Object> map : content.getStatistics()) {
            statistics[i++] = new UserCommandStatistics(map);
        }
    }

    return new UserProfileEigenModel(entity.getVersion(), site, user,
            content.getUMatrix() == null ? null : new Array2DRowRealMatrix(content.getUMatrix()),
            content.getDiagonalMatrix() == null ? null : new Array2DRowRealMatrix(content.getDiagonalMatrix()),
            content.getDimension(),
            content.getMinVector() == null ? null : new ArrayRealVector(content.getMinVector()),
            content.getMaxVector() == null ? null : new ArrayRealVector(content.getMaxVector()),
            principalComponents,
            content.getMaximumL2Norm() == null ? null : new ArrayRealVector(content.getMaximumL2Norm()),
            content.getMinimumL2Norm() == null ? null : new ArrayRealVector(content.getMinimumL2Norm()),
            statistics);
}

---

From source file:edu.cudenver.bios.power.test.paper.TestConditionalMultivariate.java

/**
 * Builds matrices for a multivariate GLM with fixed predictors
 *//*w w w . j  a v a2 s.  co m*/
private GLMMPowerParameters buildValidMultivariateFixedInputs() {
    GLMMPowerParameters params = new GLMMPowerParameters();

    // add tests
    for (Test test : Test.values()) {
        params.addTest(test);
    }

    // add alpha values
    for (double alpha : ALPHA_LIST)
        params.addAlpha(alpha);

    int Q = 4;
    // create design matrix
    params.setDesignEssence(MatrixUtils.createRealIdentityMatrix(Q));
    // add sample size multipliers
    for (int sampleSize : SAMPLE_SIZE_LIST)
        params.addSampleSize(sampleSize);

    // build sigma matrix
    double rho = 0.4;
    double[][] sigma = { { 1, rho, rho }, { rho, 1, rho }, { rho, rho, 1 } }; // compound symmetry
    // double [][] sigma = {{1,0.2,0.3},{0.2,1,0.2},{0.3,0.2,1}}; // toeplitz
    params.setSigmaError(new Array2DRowRealMatrix(sigma));
    // add sigma scale values
    for (double sigmaScale : SIGMA_SCALE_LIST)
        params.addSigmaScale(sigmaScale);

    // build beta matrix
    double[][] beta = { { 1, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
    params.setBeta(new FixedRandomMatrix(beta, null, false));
    // add beta scale values
    for (double betaScale : BETA_SCALE_LIST)
        params.addBetaScale(betaScale);

    // build theta null matrix
    double[][] theta0 = { { 0, 0 }, { 0, 0 }, { 0, 0 } };
    params.setTheta(new Array2DRowRealMatrix(theta0));

    // build between subject contrast
    double[][] between = { { 1, -1, 0, 0 }, { 1, 0, -1, 0 }, { 1, 0, 0, -1 } };
    params.setBetweenSubjectContrast(new FixedRandomMatrix(between, null, true));

    // build within subject contrast
    double[][] within = { { 1, 1 }, { -1, 0 }, { 0, -1 } };
    params.setWithinSubjectContrast(new Array2DRowRealMatrix(within));

    //RealMatrix U = params.getWithinSubjectContrast();
    //RealMatrix upu = U.multiply(U.transpose());

    return params;
}

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