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

Introduction

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

Prototype

RealMatrix multiply(RealMatrix m) throws DimensionMismatchException;

Source Link

Document

Returns the result of postmultiplying this by m .

Usage

From source file:com.joptimizer.algebra.CholeskyFactorizationTest.java

public void testInvert1() throws Exception {
    log.debug("testInvert1");
    double[][] QData = new double[][] { { 1, .12, .13, .14, .15 }, { .12, 2, .23, .24, .25 },
            { .13, .23, 3, 0, 0 }, { .14, .24, 0, 4, 0 }, { .15, .25, 0, 0, 5 } };
    RealMatrix Q = MatrixUtils.createRealMatrix(QData);

    CholeskyFactorization myc = new CholeskyFactorization(DoubleFactory2D.dense.make(QData));
    myc.factorize();/*from   w  ww.  j  ava 2  s.com*/
    RealMatrix L = new Array2DRowRealMatrix(myc.getL().toArray());
    RealMatrix LT = new Array2DRowRealMatrix(myc.getLT().toArray());
    log.debug("L: " + ArrayUtils.toString(L.getData()));
    log.debug("LT: " + ArrayUtils.toString(LT.getData()));
    log.debug("L.LT: " + ArrayUtils.toString(L.multiply(LT).getData()));
    log.debug("LT.L: " + ArrayUtils.toString(LT.multiply(L).getData()));

    // check Q = L.LT
    double norm = L.multiply(LT).subtract(Q).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 1.E-15);

    RealMatrix LInv = new SingularValueDecomposition(L).getSolver().getInverse();
    log.debug("LInv: " + ArrayUtils.toString(LInv.getData()));
    RealMatrix LInvT = LInv.transpose();
    log.debug("LInvT: " + ArrayUtils.toString(LInvT.getData()));
    RealMatrix LTInv = new SingularValueDecomposition(LT).getSolver().getInverse();
    log.debug("LTInv: " + ArrayUtils.toString(LTInv.getData()));
    RealMatrix LTInvT = LTInv.transpose();
    log.debug("LTInvT: " + ArrayUtils.toString(LTInvT.getData()));
    log.debug("LInv.LInvT: " + ArrayUtils.toString(LInv.multiply(LInvT).getData()));
    log.debug("LTInv.LTInvT: " + ArrayUtils.toString(LTInv.multiply(LTInvT).getData()));

    RealMatrix Id = MatrixUtils.createRealIdentityMatrix(Q.getRowDimension());
    //check Q.(LTInv * LInv) = 1
    norm = Q.multiply(LTInv.multiply(LInv)).subtract(Id).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 5.E-15);

    // check Q.QInv = 1
    RealMatrix QInv = MatrixUtils.createRealMatrix(myc.getInverse().toArray());
    norm = Q.multiply(QInv).subtract(Id).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 1.E-15);
}

---

From source file:com.joptimizer.algebra.CholeskyFactorizationTest.java

/**
 * The same as before, but with rescaling.
 *///from www. j  a va2s  .  c  om
public void testInvert2() throws Exception {
    log.debug("testInvert2");
    double[][] QData = new double[][] { { 1, .12, .13, .14, .15 }, { .12, 2, .23, .24, .25 },
            { .13, .23, 3, 0, 0 }, { .14, .24, 0, 4, 0 }, { .15, .25, 0, 0, 5 } };
    RealMatrix Q = MatrixUtils.createRealMatrix(QData);

    CholeskyFactorization myc = new CholeskyFactorization(DoubleFactory2D.dense.make(QData),
            new Matrix1NormRescaler());
    myc.factorize();
    RealMatrix L = new Array2DRowRealMatrix(myc.getL().toArray());
    RealMatrix LT = new Array2DRowRealMatrix(myc.getLT().toArray());
    log.debug("L: " + ArrayUtils.toString(L.getData()));
    log.debug("LT: " + ArrayUtils.toString(LT.getData()));
    log.debug("L.LT: " + ArrayUtils.toString(L.multiply(LT).getData()));
    log.debug("LT.L: " + ArrayUtils.toString(LT.multiply(L).getData()));

    // check Q = L.LT
    double norm = L.multiply(LT).subtract(Q).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 1.E-15);

    RealMatrix LInv = new SingularValueDecomposition(L).getSolver().getInverse();
    log.debug("LInv: " + ArrayUtils.toString(LInv.getData()));
    RealMatrix LInvT = LInv.transpose();
    log.debug("LInvT: " + ArrayUtils.toString(LInvT.getData()));
    RealMatrix LTInv = new SingularValueDecomposition(LT).getSolver().getInverse();
    log.debug("LTInv: " + ArrayUtils.toString(LTInv.getData()));
    RealMatrix LTInvT = LTInv.transpose();
    log.debug("LTInvT: " + ArrayUtils.toString(LTInvT.getData()));
    log.debug("LInv.LInvT: " + ArrayUtils.toString(LInv.multiply(LInvT).getData()));
    log.debug("LTInv.LTInvT: " + ArrayUtils.toString(LTInv.multiply(LTInvT).getData()));

    RealMatrix Id = MatrixUtils.createRealIdentityMatrix(Q.getRowDimension());
    //check Q.(LTInv * LInv) = 1
    norm = Q.multiply(LTInv.multiply(LInv)).subtract(Id).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 5.E-15);

    // check Q.QInv = 1
    RealMatrix QInv = MatrixUtils.createRealMatrix(myc.getInverse().toArray());
    norm = Q.multiply(QInv).subtract(Id).getNorm();
    log.debug("norm: " + norm);
    assertTrue(norm < 1.E-15);
}

---

From source file:ffnn.FFNNTubesAI.java

@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    RealMatrix input_matrix = new BlockRealMatrix(1, input_layer + 1);

    instance = filterNominalNumeric(instance);

    for (int k = 0; k < input_layer; k++) {
        input_matrix.setEntry(0, k, instance.value(k));
    }/*from  w ww .j a v  a  2 s.c  om*/
    input_matrix.setEntry(0, input_layer, 1.0); // bias

    RealMatrix hidden_matrix = input_matrix.multiply(weight1);
    for (int y = 0; y < hidden_layer; ++y) {
        hidden_matrix.setEntry(0, y, sig(hidden_matrix.getEntry(0, y)));
    }
    RealMatrix output_matrix = hidden_matrix.multiply(weight2).add(bias2);
    for (int y = 0; y < output_layer; ++y) {
        output_matrix.setEntry(0, y, sig(output_matrix.getEntry(0, y)));
    }
    double[][] m = output_matrix.getData();

    return m[0];
}

---

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

@Test
public void eigenValues() {
    RealMatrix R = new Array2DRowRealMatrix(readHarman74Data());

    int nFactors = 2;
    int nVariables = 24;
    double[] x = new double[nVariables];
    for (int i = 0; i < nVariables; i++) {
        x[i] = .5;/*from   w  ww .j  a v a 2 s. c o m*/
    }

    for (int i = 0; i < nVariables; i++) {
        R.setEntry(i, i, 1.0 - x[i]);
    }

    EigenDecomposition eigen = new EigenDecomposition(R);
    RealMatrix eigenVectors = eigen.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);

    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(eigen.getRealEigenvalue(i));
    }
    DiagonalMatrix evMatrix = new DiagonalMatrix(ev);// USE Apache version
    // of Diagonal
    // matrix when
    // upgrade to
    // version 3.2
    RealMatrix LAMBDA = eigenVectors.multiply(evMatrix);
    RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
    RealMatrix RESID = R.subtract(SIGMA);

    double sum = 0.0;
    double squared = 0.0;
    for (int i = 0; i < RESID.getRowDimension(); i++) {
        for (int j = 0; j < RESID.getColumnDimension(); j++) {
            if (i != j) {
                sum += Math.pow(RESID.getEntry(i, j), 2);
            }
        }
    }

    System.out.println(sum);

    // RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
    //
    // System.out.println("SIGMA");
    // for(int i=0;i<SIGMA.getRowDimension();i++){
    // for(int j=0;j<SIGMA.getColumnDimension();j++){
    // System.out.print(SIGMA.getEntry(i, j) + " ");
    // }
    // System.out.println();
    // }
}

---

From source file:edu.cudenver.bios.power.glmm.GLMMTestHotellingLawley.java

/**
 * Compute a Hotelling-Lawley Trace statistic
 *
 * @param H hypothesis sum of squares matrix
 * @param E error sum of squares matrix/*ww w.j  a v  a2s . c o m*/
 * @returns F statistic
 */
private double getHotellingLawleyTrace(RealMatrix H, RealMatrix E) throws IllegalArgumentException {
    if (!H.isSquare() || !E.isSquare() || H.getColumnDimension() != E.getRowDimension())
        throw new IllegalArgumentException(
                "Failed to compute Hotelling-Lawley Trace: hypothesis and error matrices must be square and same dimensions");

    RealMatrix inverseE = new LUDecomposition(E).getSolver().getInverse();
    RealMatrix HinverseE = H.multiply(inverseE);

    return HinverseE.getTrace();
}

---

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

private void computeFactorLoadings(double[] x) {
    uniqueness = x;// www  .  j a  v a  2s .com
    communality = new double[nVariables];

    for (int i = 0; i < nVariables; i++) {
        R.setEntry(i, i, 1.0 - x[i]);
    }

    EigenDecomposition E = new EigenDecomposition(R);
    RealMatrix L = E.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);
    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(E.getRealEigenvalue(i));
    }
    DiagonalMatrix M = new DiagonalMatrix(ev);
    RealMatrix LOAD = L.multiply(M);

    //rotate factor loadings
    if (rotationMethod != RotationMethod.NONE) {
        GPArotation gpa = new GPArotation();
        RotationResults results = gpa.rotate(LOAD, rotationMethod);
        LOAD = results.getFactorLoadings();
    }

    Sum[] colSums = new Sum[nFactors];
    Sum[] colSumsSquares = new Sum[nFactors];

    for (int j = 0; j < nFactors; j++) {
        colSums[j] = new Sum();
        colSumsSquares[j] = new Sum();
    }

    factorLoading = new double[nVariables][nFactors];

    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            factorLoading[i][j] = LOAD.getEntry(i, j);
            colSums[j].increment(factorLoading[i][j]);
            colSumsSquares[j].increment(Math.pow(factorLoading[i][j], 2));
            communality[i] += Math.pow(factorLoading[i][j], 2);
        }
    }

    //check sign of factor
    double sign = 1.0;
    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            if (colSums[j].getResult() < 0) {
                sign = -1.0;
            } else {
                sign = 1.0;
            }
            factorLoading[i][j] = factorLoading[i][j] * sign;
        }
    }

    double totSumOfSquares = 0.0;
    sumsOfSquares = new double[nFactors];
    proportionOfExplainedVariance = new double[nFactors];
    proportionOfVariance = new double[nFactors];
    for (int j = 0; j < nFactors; j++) {
        sumsOfSquares[j] = colSumsSquares[j].getResult();
        totSumOfSquares += sumsOfSquares[j];
    }
    for (int j = 0; j < nFactors; j++) {
        proportionOfExplainedVariance[j] = sumsOfSquares[j] / totSumOfSquares;
        proportionOfVariance[j] = sumsOfSquares[j] / nVariables;
    }

}

---

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

private void computeFactorLoadings(double[] x) {
    uniqueness = x;/*from  ww  w  . j av  a2  s.c  o m*/
    communality = new double[nVariables];

    double[] sqrtPsi = new double[nVariables];
    double[] invSqrtPsi = new double[nVariables];
    for (int i = 0; i < nVariables; i++) {
        sqrtPsi[i] = Math.sqrt(x[i]);
        invSqrtPsi[i] = 1.0 / Math.sqrt(x[i]);
    }
    DiagonalMatrix diagPsi = new DiagonalMatrix(x);
    DiagonalMatrix diagSqtPsi = new DiagonalMatrix(sqrtPsi);
    DiagonalMatrix diagInvSqrtPsi = new DiagonalMatrix(invSqrtPsi);

    RealMatrix Sstar = diagInvSqrtPsi.multiply(R).multiply(diagInvSqrtPsi);
    EigenDecomposition E = new EigenDecomposition(Sstar);
    RealMatrix L = E.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);
    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(Math.max(E.getRealEigenvalue(i) - 1, 0));
    }
    DiagonalMatrix M = new DiagonalMatrix(ev);
    RealMatrix LOAD2 = L.multiply(M);
    RealMatrix LOAD = diagSqtPsi.multiply(LOAD2);

    //rotate factor loadings
    if (rotationMethod != RotationMethod.NONE) {
        GPArotation gpa = new GPArotation();
        RotationResults results = gpa.rotate(LOAD, rotationMethod);
        LOAD = results.getFactorLoadings();
    }

    Sum[] colSums = new Sum[nFactors];
    Sum[] colSumsSquares = new Sum[nFactors];

    for (int j = 0; j < nFactors; j++) {
        colSums[j] = new Sum();
        colSumsSquares[j] = new Sum();
    }

    factorLoading = new double[nVariables][nFactors];

    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            factorLoading[i][j] = LOAD.getEntry(i, j);
            colSums[j].increment(factorLoading[i][j]);
            colSumsSquares[j].increment(Math.pow(factorLoading[i][j], 2));
            communality[i] += Math.pow(factorLoading[i][j], 2);
        }
    }

    //check sign of factor
    double sign = 1.0;
    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            if (colSums[j].getResult() < 0) {
                sign = -1.0;
            } else {
                sign = 1.0;
            }
            factorLoading[i][j] = factorLoading[i][j] * sign;
        }
    }

    double totSumOfSquares = 0.0;
    sumsOfSquares = new double[nFactors];
    proportionOfExplainedVariance = new double[nFactors];
    proportionOfVariance = new double[nFactors];
    for (int j = 0; j < nFactors; j++) {
        sumsOfSquares[j] = colSumsSquares[j].getResult();
        totSumOfSquares += sumsOfSquares[j];
    }
    for (int j = 0; j < nFactors; j++) {
        proportionOfExplainedVariance[j] = sumsOfSquares[j] / totSumOfSquares;
        proportionOfVariance[j] = sumsOfSquares[j] / nVariables;
    }

}

---

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

private void computeFactorLoadings(double[] x) {
    uniqueness = x;//from   w  ww  . j a v  a2 s.c  o m
    communality = new double[nVariables];

    double[] sqrtPsi = new double[nVariables];
    double[] invSqrtPsi = new double[nVariables];
    for (int i = 0; i < nVariables; i++) {
        sqrtPsi[i] = Math.sqrt(x[i]);
        invSqrtPsi[i] = 1.0 / Math.sqrt(x[i]);
    }
    DiagonalMatrix diagPsi = new DiagonalMatrix(x);
    DiagonalMatrix diagSqtPsi = new DiagonalMatrix(sqrtPsi);
    DiagonalMatrix diagInvSqrtPsi = new DiagonalMatrix(invSqrtPsi);

    RealMatrix Sstar = diagInvSqrtPsi.multiply(R2).multiply(diagInvSqrtPsi);
    EigenDecomposition E = new EigenDecomposition(Sstar);
    RealMatrix L = E.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);
    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(Math.max(E.getRealEigenvalue(i) - 1, 0));
    }
    DiagonalMatrix M = new DiagonalMatrix(ev);
    RealMatrix LOAD2 = L.multiply(M);
    RealMatrix LOAD = diagSqtPsi.multiply(LOAD2);

    //rotate factor loadings
    if (rotationMethod != RotationMethod.NONE) {
        GPArotation gpa = new GPArotation();
        RotationResults results = gpa.rotate(LOAD, rotationMethod);
        LOAD = results.getFactorLoadings();
    }

    Sum[] colSums = new Sum[nFactors];
    Sum[] colSumsSquares = new Sum[nFactors];

    for (int j = 0; j < nFactors; j++) {
        colSums[j] = new Sum();
        colSumsSquares[j] = new Sum();
    }

    factorLoading = new double[nVariables][nFactors];

    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            factorLoading[i][j] = LOAD.getEntry(i, j);
            colSums[j].increment(factorLoading[i][j]);
            colSumsSquares[j].increment(Math.pow(factorLoading[i][j], 2));
            communality[i] += Math.pow(factorLoading[i][j], 2);
        }
    }

    //check sign of factor
    double sign = 1.0;
    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            if (colSums[j].getResult() < 0) {
                sign = -1.0;
            } else {
                sign = 1.0;
            }
            factorLoading[i][j] = factorLoading[i][j] * sign;
        }
    }

    double totSumOfSquares = 0.0;
    sumsOfSquares = new double[nFactors];
    proportionOfExplainedVariance = new double[nFactors];
    proportionOfVariance = new double[nFactors];
    for (int j = 0; j < nFactors; j++) {
        sumsOfSquares[j] = colSumsSquares[j].getResult();
        totSumOfSquares += sumsOfSquares[j];
    }
    for (int j = 0; j < nFactors; j++) {
        proportionOfExplainedVariance[j] = sumsOfSquares[j] / totSumOfSquares;
        proportionOfVariance[j] = sumsOfSquares[j] / nVariables;
    }

}

---

From source file:edu.cudenver.bios.power.glmm.GLMMTestPillaiBartlett.java

/**
 * Compute a Pillai Bartlett Trace statistic
 * /*from ww w  . j  a  va2 s . c  om*/
 * @param H hypothesis sum of squares matrix
 * @param E error sum of squares matrix
 * @returns F statistic
 */
private double getPillaiBartlettTrace(RealMatrix H, RealMatrix E) {
    if (!H.isSquare() || !E.isSquare() || H.getColumnDimension() != E.getRowDimension())
        throw new IllegalArgumentException(
                "Failed to compute Pillai-Bartlett Trace: hypothesis and error matrices must be square and same dimensions");

    double a = C.getRowDimension();
    double b = U.getColumnDimension();
    double s = (a < b) ? a : b;
    double p = beta.getColumnDimension();

    RealMatrix adjustedH = H;
    if ((s == 1 && p > 1) || fMethod == FApproximation.PILLAI_ONE_MOMENT_OMEGA_MULT
            || fMethod == FApproximation.MULLER_TWO_MOMENT_OMEGA_MULT) {
        adjustedH = H.scalarMultiply(((double) (totalN - rank) / (double) totalN));
    }

    RealMatrix T = adjustedH.add(E);
    RealMatrix inverseT = new LUDecomposition(T).getSolver().getInverse();

    RealMatrix HinverseT = adjustedH.multiply(inverseT);

    return HinverseT.getTrace();
}

---

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

@Override
public double looPredLogLikelihood() {

    double[][] Y = getY();
    double[][] dY = getdY();
    double[][] mask = getMask();

    int ns = Y.length;
    int ny = Y[0].length;

    RealMatrix Ky = MatrixUtils.createRealMatrix(this.Ky);
    RealMatrix invK = new LUDecomposition(Ky).getSolver().getInverse();

    RealMatrix dYmat = MatrixUtils.createRealMatrix(dY);

    double[] LOOPredLL = new double[ny];
    for (int j = 0; j < ny; j++) {
        RealMatrix dy = MatrixUtils.createColumnRealMatrix(dYmat.getColumn(j));
        RealMatrix invKdy = invK.multiply(dy);
        double sum = 0;
        for (int i = 0; i < ns; i++) {
            double mu_i = dYmat.getEntry(i, j) - invKdy.getEntry(i, 0) / invK.getEntry(i, i);
            double sigma_i2 = 1 / invK.getEntry(i, i);
            double logLL = StatUtils.logProbaNormal(dYmat.getEntry(i, j), mu_i, Math.sqrt(sigma_i2));
            sum += logLL * mask[i][j];/*from  ww  w.j  a v a  2s . c  o  m*/
        }
        LOOPredLL[j] = sum;
    }

    double sumLOOPredLL = 0;
    for (int j = 0; j < ny; j++) {
        sumLOOPredLL += LOOPredLL[j];
    }

    return sumLOOPredLL;
}

---

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