Example usage for org.apache.commons.math3.linear RealMatrix add

Introduction

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

Prototype

RealMatrix add(RealMatrix m) throws MatrixDimensionMismatchException;

Source Link

Document

Returns the sum of this and m .

Usage

From source file:com.itemanalysis.psychometrics.mixture.MvNormalMixtureModel.java

private void setCommonCovariance(RealMatrix[] cov) {
    RealMatrix tempS = new Array2DRowRealMatrix(dimensions, dimensions);
    for (int g = 0; g < groups; g++) {
        tempS = tempS.add(cov[g].scalarMultiply(compDistribution[g].getMixingProportion()));
    }//from   w w  w .  j a v a 2s .  co  m
    for (int g = 0; g < groups; g++) {
        cov[g] = tempS;
    }
}

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From source file:com.cloudera.oryx.app.serving.als.model.ALSServingModel.java

public Solver getYTYSolver() {
    Solver cached = cachedYTYSolver.get();
    if (cached != null) {
        return cached;
    }/*  www .  j av a2 s  . c o  m*/
    RealMatrix YTY = null;
    for (FeatureVectors yPartition : Y) {
        RealMatrix YTYpartial = yPartition.getVTV();
        if (YTYpartial != null) {
            YTY = YTY == null ? YTYpartial : YTY.add(YTYpartial);
        }
    }
    // Possible to compute this twice, but not a big deal
    Solver newYTYSolver = LinearSystemSolver.getSolver(YTY);
    cachedYTYSolver.set(newYTYSolver);
    return newYTYSolver;
}

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From source file:com.analog.lyric.dimple.solvers.sumproduct.customFactors.CustomComplexGaussianPolynomial.java

private Object[] calculateWeightedSums(Complex[] in) {

    double[] means = new double[] { 0, 0 };

    for (int i = 0; i < in.length; i++) {
        means[0] += in[i].getReal();/*from   www . j av a  2s.  c om*/
        means[1] += in[i].getImaginary();
    }
    means[0] /= in.length;
    means[1] /= in.length;

    RealMatrix cm = createRealMatrix(2, 2);
    RealVector mm = wrapRealVector(means);

    for (int i = 0; i < in.length; i++) {
        RealVector m = createRealVector(new double[] { in[i].getReal(), in[i].getImaginary() });
        RealVector m2 = m.subtract(mm);
        cm = cm.add(m2.outerProduct(m2));
    }

    cm = cm.scalarMultiply(1.0 / in.length);

    return new Object[] { new Complex(means[0], means[1]), matrixGetDataRef(cm) };

}

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From source file:hulo.localization.models.obs.GaussianProcess.java

public GaussianProcess fit(double[][] X, double[][] Y) {
    int ns = X.length;
    this.X = X;/*from  w  w  w  .  jav a 2  s .  com*/
    this.Y = Y;

    // Compute Gram Matrix (Symmetric matrix)
    K = computeGramMatrix(X);
    RealMatrix KMat = MatrixUtils.createRealMatrix(K);
    RealMatrix sigma_n2I = MatrixUtils.createRealIdentityMatrix(ns).scalarMultiply(sigmaN * sigmaN);
    RealMatrix Ky = KMat.add(sigma_n2I);
    this.Ky = Ky.getData();
    LUDecomposition LUDecomp = new LUDecomposition(Ky);
    detKy = LUDecomp.getDeterminant();

    RealMatrix invKyMat = LUDecomp.getSolver().getInverse();
    invKy = invKyMat.getData();

    // Precompute dY = Y - mX
    int ny = Y[0].length;
    this.mX = new double[ns][ny];
    this.dY = new double[ns][ny];
    for (int i = 0; i < ns; i++) {
        for (int j = 0; j < ny; j++) {
            mX[i][j] = meanFunc(X[i], j);
            dY[i][j] = Y[i][j] - mX[i][j];
        }
    }

    if (optConstVar == 1) {
        invKyDY = computeInvKyDY(invKy, dY);
    }

    return this;
}

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From source file:com.anhth12.lambda.app.serving.als.model.ALSServingModel.java

public Solver getYTYSolver() {
    RealMatrix YTY = null;

    for (int partition = 0; partition < Y.length; partition++) {
        RealMatrix YTYpartial;// w  w  w .ja  va2 s. c  o m
        try (AutoLock al = new AutoLock(yLocks[partition].readLock())) {
            YTYpartial = VectorMath.transposeTimesSelf(Y[partition].values());
        }

        if (YTYpartial != null) {
            YTY = YTY == null ? YTYpartial : YTY.add(YTYpartial);
        }
    }

    return new LinearSystemSolver().getSolver(YTY);
}

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From source file:edu.oregonstate.eecs.mcplan.ml.GaussianMixtureModel.java

@Override
public void run() {
    init();//from w  w  w.j a v  a  2  s.  c  o  m
    System.out.println("Init");
    for (int i = 0; i < mu().length; ++i) {
        System.out.println("Mu " + i + ": " + mu()[i]);
        System.out.println("Sigma " + i + ": " + Sigma()[i]);
    }

    int iterations = 0;
    while (!converged_ && iterations++ < max_iterations_) {
        // Expectation
        makeMixture();
        for (int i = 0; i < n_; ++i) {
            for (int j = 0; j < k_; ++j) {
                c_[i][j] = posterior(data_[i], j);
            }
            Fn.normalize_inplace(c_[i]);
        }

        // Maximization
        for (int j = 0; j < k_; ++j) {
            double Z = 0.0;
            final RealVector mu_j = new ArrayRealVector(d_);
            RealMatrix Sigma_j = new Array2DRowRealMatrix(d_, d_);
            for (int i = 0; i < n_; ++i) {
                final double c_ij = c_[i][j];
                Z += c_ij;
                final RealVector x_i = new ArrayRealVector(data_[i]);
                // mu_j += c_ij * x_i
                mu_j.combineToSelf(1.0, 1.0, x_i.mapMultiply(c_ij));
                final RealVector v = x_i.subtract(mu_[j]);
                // Sigma_j += c_ij * |v><v|
                Sigma_j = Sigma_j.add(v.outerProduct(v).scalarMultiply(c_ij));
            }
            final double Zinv = 1.0 / Z;
            final double pi_j = Z / n_;
            mu_j.mapMultiplyToSelf(Zinv);
            Sigma_j = Sigma_j.scalarMultiply(Zinv);
            //            converged &= hasConverged( j, pi_j, mu_j, Sigma_j );
            pi_[j] = pi_j;
            mu_[j] = mu_j;
            Sigma_[j] = Sigma_j;
        }
        //         debug();

        final double log_likelihood = logLikelihood();
        if (Math.abs(log_likelihood - old_log_likelihood_) < epsilon_) {
            converged_ = true;
        }
        old_log_likelihood_ = log_likelihood;
    }
}

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From source file:com.cloudera.oryx.ml.serving.als.model.ALSServingModel.java

public Solver getYTYSolver() {
    RealMatrix YTY = null;
    for (int partition = 0; partition < Y.length; partition++) {
        RealMatrix YTYpartial;// w ww  .  j  a v a  2 s  .c o m
        Lock lock = yLocks[partition].readLock();
        lock.lock();
        try {
            YTYpartial = VectorMath.transposeTimesSelf(Y[partition].values());
        } finally {
            lock.unlock();
        }
        if (YTYpartial != null) {
            YTY = YTY == null ? YTYpartial : YTY.add(YTYpartial);
        }
    }
    return new LinearSystemSolver().getSolver(YTY);
}

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From source file:edu.ucdenver.bios.power.ConditionalOrthogonalPolynomial2FactorTest.java

/**
 * //w  ww .j av  a  2s.c  o m
 * @param checker
 * @param params
 */
private void checkPower(PowerChecker checker, String title, GLMMPowerParameters params) {
    /* 
     * get orthogonal contrasts for within subject factors
     * Log base 2 spacing Clip (2,4,16) and Region(2,8,32) 
     */
    ArrayList<Factor> factorList = new ArrayList<Factor>();
    factorList.add(factorA);
    factorList.add(factorB);
    OrthogonalPolynomialContrastCollection collection = OrthogonalPolynomials.withinSubjectContrast(factorList);
    params.setWithinSubjectContrast(collection.getInteractionContrast(factorList).getContrastMatrix());

    // theta critical matrix used to back-tranform beta from U
    //    THETA = {.25}#{.5 1 -1 .5}; * =Theta(cr) from 1st sentence *after* 
    //  equation 7, Coffey and Muller (2003); 
    double[][] thetaData = { { 0.5, 1, -1, 0.5 } };
    RealMatrix thetaCr = (new Array2DRowRealMatrix(thetaData)).scalarMultiply(0.25);

    // loop over the various sigma matrices
    //Test[] testList = Test.values();
    GLMMTestFactory.Test[] testList = { GLMMTestFactory.Test.UNIREP, GLMMTestFactory.Test.UNIREP_BOX,
            GLMMTestFactory.Test.UNIREP_GEISSER_GREENHOUSE, GLMMTestFactory.Test.UNIREP_HUYNH_FELDT };
    for (GLMMTestFactory.Test test : testList) {
        params.clearTestList();
        params.addTest(test);

        for (RealMatrix sigStar : sigmaStars) {
            RealMatrix U = params.getWithinSubjectContrast();
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *;
            RealMatrix sigmaTemp = U.multiply(sigStar).multiply(U.transpose());
            int dimension = sigmaTemp.getRowDimension();
            RealMatrix Uother = MatrixUtils.getRealMatrixWithFilledValue(dimension, 1,
                    1 / Math.sqrt(U.getColumnDimension()));
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorA).getContrastMatrix());
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorB).getContrastMatrix());
            double varianceMean = (double) sigStar.getTrace() / (double) sigStar.getColumnDimension();
            RealMatrix sigmaError = sigmaTemp
                    .add(Uother.multiply(Uother.transpose()).scalarMultiply(varianceMean));

            if (verbose)
                printMatrix("Sigma Error", sigmaError);
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *; 
            RealMatrix beta = thetaCr.multiply(U.transpose());
            params.setSigmaError(sigmaError);
            params.setBeta(new FixedRandomMatrix(beta.getData(), null, false));

            checker.checkPower(params);
        }

    }

    // output the results
    ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
    reportBuilder.createValidationReportAsStdout(checker, title, false);

    assertTrue("results outside tolerance: " + TOLERANCE, checker.isSASDeviationBelowTolerance(TOLERANCE));
}

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From source file:edu.cudenver.bios.power.test.paper.TestConditionalOrthogonalPolynomial2Factor.java

/**
 * /*from ww w.  j a  v  a2  s  .  com*/
 * @param checker
 * @param outputFilename
 * @param params
 */
private void checkPower(PowerChecker checker, String title, String description, String outputFilename,
        GLMMPowerParameters params) {
    /* 
     * get orthogonal contrasts for within subject factors
     * Log base 2 spacing Clip (2,4,16) and Region(2,8,32) 
     */
    ArrayList<Factor> factorList = new ArrayList<Factor>();
    factorList.add(factorA);
    factorList.add(factorB);
    OrthogonalPolynomialContrastCollection collection = OrthogonalPolynomials.withinSubjectContrast(factorList);
    params.setWithinSubjectContrast(collection.getInteractionContrast(factorList).getContrastMatrix());

    // theta critical matrix used to back-tranform beta from U
    //    THETA = {.25}#{.5 1 -1 .5}; * =Theta(cr) from 1st sentence *after* 
    //  equation 7, Coffey and Muller (2003); 
    double[][] thetaData = { { 0.5, 1, -1, 0.5 } };
    RealMatrix thetaCr = (new Array2DRowRealMatrix(thetaData)).scalarMultiply(0.25);

    // loop over the various sigma matrices
    //Test[] testList = Test.values();
    Test[] testList = { Test.UNIREP, Test.UNIREP_BOX, Test.UNIREP_GEISSER_GREENHOUSE, Test.UNIREP_HUYNH_FELDT };
    for (Test test : testList) {
        params.clearTestList();
        params.addTest(test);

        for (RealMatrix sigStar : sigmaStars) {
            RealMatrix U = params.getWithinSubjectContrast();
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *;
            RealMatrix sigmaTemp = U.multiply(sigStar).multiply(U.transpose());
            int dimension = sigmaTemp.getRowDimension();
            RealMatrix Uother = MatrixUtils.getRealMatrixWithFilledValue(dimension, 1,
                    1 / Math.sqrt(U.getColumnDimension()));
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorA).getContrastMatrix());
            Uother = MatrixUtils.getHorizontalAppend(Uother,
                    collection.getMainEffectContrast(factorB).getContrastMatrix());
            double varianceMean = (double) sigStar.getTrace() / (double) sigStar.getColumnDimension();
            RealMatrix sigmaError = sigmaTemp
                    .add(Uother.multiply(Uother.transpose()).scalarMultiply(varianceMean));

            if (verbose)
                printMatrix("Sigma Error", sigmaError);
            // 1st paragraph in section 2.4, Coffey and Muller 2003 *; 
            RealMatrix beta = thetaCr.multiply(U.transpose());
            params.setSigmaError(sigmaError);
            params.setBeta(new FixedRandomMatrix(beta.getData(), null, false));

            checker.checkPower(params);
        }

    }

    // output the results
    try {
        ValidationReportBuilder reportBuilder = new ValidationReportBuilder();
        reportBuilder.createValidationReportAsStdout(checker, title, false);
        reportBuilder.createValidationReportAsLaTex(outputFilename, title, AUTHOR, description, params,
                checker);
    } catch (Exception e) {
        System.err.println(e.getMessage());
    }

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

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From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

public double[][] hessian(double[] X) {
    RealVector x = new ArrayRealVector(X);

    RealMatrix ret = new Array2DRowRealMatrix(dim, dim);
    for (int i = 0; i < socpConstraintParametersList.size(); i++) {
        SOCPConstraintParameters param = socpConstraintParametersList.get(i);
        double t = this.buildT(param, x);
        RealVector u = this.buildU(param, x);
        double t2uu = t * t - u.dotProduct(u);
        RealVector t2u = u.mapMultiply(-2 * t);
        RealMatrix Jacob = this.buildJ(param, x);
        int k = u.getDimension();
        RealMatrix H = new Array2DRowRealMatrix(k + 1, k + 1);
        RealMatrix ID = MatrixUtils.createRealIdentityMatrix(k);
        H.setSubMatrix(ID.scalarMultiply(t2uu).add(u.outerProduct(u).scalarMultiply(2)).getData(), 0, 0);
        H.setSubMatrix(new double[][] { t2u.toArray() }, k, 0);
        for (int j = 0; j < k; j++) {
            H.setEntry(j, k, t2u.getEntry(j));
        }//  www.  ja va2 s.co  m
        H.setEntry(k, k, t * t + u.dotProduct(u));
        RealMatrix ret_i = Jacob.multiply(H).multiply(Jacob.transpose()).scalarMultiply(2. / Math.pow(t2uu, 2));
        ret = ret.add(ret_i);
    }

    return ret.getData();
}

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