Example usage for org.apache.commons.math3.linear MatrixUtils createRealDiagonalMatrix

Introduction

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

Prototype

public static RealMatrix createRealDiagonalMatrix(final double[] diagonal) 

Source Link

Document

Returns a diagonal matrix with specified elements.

Usage

From source file:edu.washington.gs.skyline.model.quantification.WeightedRegression.java

public static double[] weighted(double[][] x, double[] y, double[] weights, boolean intercept) {
    RealMatrix predictor;//from ww  w. j  av  a  2  s .co m
    if (intercept) {
        int nRows = x.length;
        int nCols = x[0].length + 1;
        predictor = MatrixUtils.createRealMatrix(nRows, nCols);
        for (int iRow = 0; iRow < nRows; iRow++) {
            predictor.setEntry(iRow, 0, 1.0);
            for (int iCol = 1; iCol < nCols; iCol++) {
                predictor.setEntry(iRow, iCol, x[iRow][iCol - 1]);
            }
        }
    } else {
        predictor = MatrixUtils.createRealMatrix(x);
    }
    RealVector responseVector = MatrixUtils.createRealVector(y);
    RealMatrix weightsMatrix = MatrixUtils.createRealDiagonalMatrix(weights);
    RealMatrix predictorTransposed = predictor.transpose();
    RealMatrix predictorTransposedTimesWeights = predictorTransposed
            .multiply(weightsMatrix.multiply(predictor));
    CholeskyDecomposition choleskyDecomposition = new CholeskyDecomposition(predictorTransposedTimesWeights);
    RealVector vectorToSolve = predictorTransposed.operate(weightsMatrix.operate(responseVector));
    RealVector result = choleskyDecomposition.getSolver().solve(vectorToSolve);
    return result.toArray();
}

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From source file:com.github.thorbenlindhauer.test.util.LinearAlgebraUtil.java

public static RealMatrix diagonalMatrix(double value, int size) {
    double[] values = new double[size];
    for (int i = 0; i < size; i++) {
        values[i] = value;/* w  w w. ja v a2  s  .  c om*/
    }

    return MatrixUtils.createRealDiagonalMatrix(values);
}

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From source file:com.caseystella.analytics.outlier.batch.rpca.RidgeRegression.java

public void updateCoefficients(double l2penalty) {
    if (this.X_svd == null) {
        this.X_svd = new SingularValueDecomposition(X);
    }/* w  ww .  ja va  2s .  co m*/
    RealMatrix V = this.X_svd.getV();
    double[] s = this.X_svd.getSingularValues();
    RealMatrix U = this.X_svd.getU();

    for (int i = 0; i < s.length; i++) {
        s[i] = s[i] / (s[i] * s[i] + l2penalty);
    }
    RealMatrix S = MatrixUtils.createRealDiagonalMatrix(s);

    RealMatrix Z = V.multiply(S).multiply(U.transpose());

    this.coefficients = Z.operate(this.Y);

    this.fitted = this.X.operate(this.coefficients);
    double errorVariance = 0;
    for (int i = 0; i < residuals.length; i++) {
        this.residuals[i] = this.Y[i] - this.fitted[i];
        errorVariance += this.residuals[i] * this.residuals[i];
    }
    errorVariance = errorVariance / (X.getRowDimension() - X.getColumnDimension());

    RealMatrix errorVarianceMatrix = MatrixUtils.createRealIdentityMatrix(this.Y.length)
            .scalarMultiply(errorVariance);
    RealMatrix coefficientsCovarianceMatrix = Z.multiply(errorVarianceMatrix).multiply(Z.transpose());
    this.standarderrors = getDiagonal(coefficientsCovarianceMatrix);
}

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From source file:gamlss.utilities.MatrixFunctions.java

/**
 * Build identity matrix./*from   ww  w. j a v  a2s  . c o  m*/
 * @param size - size of the matrix
 * @return identity matrix
 */
public static BlockRealMatrix buildIdentityMatrix(final int size) {
    double[] tempArr = new double[size];
    for (int i = 0; i < size; i++) {
        tempArr[i] = 1;
    }
    return new BlockRealMatrix(MatrixUtils.createRealDiagonalMatrix(tempArr).getData());
}

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From source file:com.joptimizer.util.CholeskyFactorization.java

public double[][] getInverse() {

    //QInv = LTInv * LInv, but for symmetry (QInv=QInvT)
    //QInv = LInvT * LTInvT = LInvT * LInv, so
    //LInvT = LTInv, and we calculate
    //QInv = LInvT * LInv

    double[][] lTData = getLT();
    int dim = lTData.length;

    // LTInv calculation (it will be x)
    double[] diag = new double[dim];
    Arrays.fill(diag, 1.);//from  www  . j a  v  a  2  s .c  o m
    double[][] x = MatrixUtils.createRealDiagonalMatrix(diag).getData();
    for (int j = 0; j < dim; j++) {
        final double[] lTJ = lTData[j];
        final double lTJJ = lTJ[j];
        final double[] xJ = x[j];
        for (int k = 0; k < dim; ++k) {
            xJ[k] /= lTJJ;
        }
        for (int i = j + 1; i < dim; i++) {
            final double[] xI = x[i];
            final double lTJI = lTJ[i];
            for (int k = 0; k < dim; ++k) {
                xI[k] -= xJ[k] * lTJI;
            }
        }
    }

    // transposition (L is upper-triangular)
    double[][] LInvTData = new double[dim][dim];
    for (int i = 0; i < dim; i++) {
        double[] LInvTDatai = LInvTData[i];
        for (int j = i; j < dim; j++) {
            LInvTDatai[j] = x[j][i];
        }
    }

    // QInv
    // NB: LInvT is upper-triangular, so LInvT[i][j]=0 if i>j
    final double[][] QInvData = new double[dim][dim];
    for (int row = 0; row < dim; row++) {
        final double[] LInvTDataRow = LInvTData[row];
        final double[] QInvDataRow = QInvData[row];
        for (int col = row; col < dim; col++) {// symmetry of QInv
            final double[] LInvTDataCol = LInvTData[col];
            double sum = 0;
            for (int i = col; i < dim; i++) {// upper triangular
                sum += LInvTDataRow[i] * LInvTDataCol[i];
            }
            QInvDataRow[col] = sum;
            QInvData[col][row] = sum;// symmetry of QInv
        }
    }

    return QInvData;
}

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

@Test
public void testRange() {
    delta.setMeanVector(new ArrayRealVector(new double[] { 1.0, 2.0, 3.0 }));
    delta.setCovarianceMatrix(MatrixUtils.createRealDiagonalMatrix(new double[] { 14.0, 10.2, 3.6 }));
    baseline.setMean(-2.0);/*  www.  j  a  v a2 s. co  m*/
    baseline.setStDev(2.5);

    assertEquals(delta.getRange().getStart() + baseline.getRange().getStart(), m.getRange().getStart(), 1e-7);
    assertEquals(delta.getRange().getEnd() + baseline.getRange().getEnd(), m.getRange().getEnd(), 1e-7);
}

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From source file:com.caseystella.analytics.outlier.batch.rpca.RPCA.java

private double computeL(double mu) {
    double LPenalty = lpenalty * mu;
    SingularValueDecomposition svd = new SingularValueDecomposition(X.subtract(S));
    double[] penalizedD = softThreshold(svd.getSingularValues(), LPenalty);
    RealMatrix D_matrix = MatrixUtils.createRealDiagonalMatrix(penalizedD);
    L = svd.getU().multiply(D_matrix).multiply(svd.getVT());
    return sum(penalizedD) * LPenalty;
}

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

@Test
public void testDeepCopy() {
    List<Alternative> newAlts = new ArrayList<Alternative>();
    for (Alternative a : alternatives) {
        newAlts.add(a.deepCopy());/*from  w ww . ja  va  2  s . com*/
    }
    m.setMeanVector(new ArrayRealVector(new double[] { 666, 8, 665 }));
    m.setCovarianceMatrix(MatrixUtils.createRealDiagonalMatrix(new double[] { 4, 3, 2 }));

    MultivariateGaussianCriterionMeasurement clone = m.deepCopy(newAlts);
    assertNotSame(m, clone);
    assertEquals(newAlts, clone.getAlternatives());
    assertSame(newAlts.get(0), clone.getAlternatives().get(0));
    assertEquals(m.getMeanVector(), clone.getMeanVector());
    assertNotSame(m.getMeanVector(), clone.getMeanVector());
    assertEquals(m.getCovarianceMatrix(), clone.getCovarianceMatrix());
    assertNotSame(m.getCovarianceMatrix(), clone.getCovarianceMatrix());
}

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From source file:gamlss.smoothing.PB.java

/**
 * Constructs the penalty matrix./*from  w w w. j  a va2 s .co  m*/
 * @param xM - base matrix
 * @return penalty matrix
 */
private BlockRealMatrix formD(final BlockRealMatrix xM) {
    tempArr = new double[xM.getColumnDimension()];
    for (int i = 0; i < tempArr.length; i++) {
        tempArr[i] = 1.0;
    }
    if (Controls.ORDER == 0) {
        return new BlockRealMatrix(MatrixUtils.createRealDiagonalMatrix(tempArr).getData());
    } else {
        return MatrixFunctions.diff(
                new BlockRealMatrix(MatrixUtils.createRealDiagonalMatrix(tempArr).getData()), Controls.ORDER);
    }
}

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From source file:org.pmad.gmm.MyEDC.java

/**
 * Gets the block diagonal matrix D of the decomposition.
 * D is a block diagonal matrix.//  ww  w .ja  v  a2 s  .c o  m
 * Real eigenvalues are on the diagonal while complex values are on
 * 2x2 blocks { {real +imaginary}, {-imaginary, real} }.
 *
 * @return the D matrix.
 *
 * @see #getRealEigenvalues()
 * @see #getImagEigenvalues()
 */
public RealMatrix getD() {

    if (cachedD == null) {
        // cache the matrix for subsequent calls
        cachedD = MatrixUtils.createRealDiagonalMatrix(realEigenvalues);

        for (int i = 0; i < imagEigenvalues.length; i++) {
            if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) > 0) {
                cachedD.setEntry(i, i + 1, imagEigenvalues[i]);
            } else if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0) {
                cachedD.setEntry(i, i - 1, imagEigenvalues[i]);
            }
        }
    }
    return cachedD;
}

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