Example usage for org.apache.commons.math3.linear DecompositionSolver isNonSingular

List of usage examples for org.apache.commons.math3.linear DecompositionSolver isNonSingular

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Introduction

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

Prototype

boolean isNonSingular();

Source Link

Document

Check if the decomposed matrix is non-singular.

Usage

From source file:hivemall.utils.math.StatsUtils.java

/**
 * pdf(x, x_hat) = exp(-0.5 * (x-x_hat) * inv() * (x-x_hat)T) / ( 2^0.5d * det()^0.5)
 * /*w w  w  .  j  a va 2s  .  c om*/
 * @return value of probabilistic density function
 * @link https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Density_function
 */
public static double pdf(@Nonnull final RealVector x, @Nonnull final RealVector x_hat,
        @Nonnull final RealMatrix sigma) {
    final int dim = x.getDimension();
    Preconditions.checkArgument(x_hat.getDimension() == dim,
            "|x| != |x_hat|, |x|=" + dim + ", |x_hat|=" + x_hat.getDimension());
    Preconditions.checkArgument(sigma.getRowDimension() == dim,
            "|x| != |sigma|, |x|=" + dim + ", |sigma|=" + sigma.getRowDimension());
    Preconditions.checkArgument(sigma.isSquare(), "Sigma is not square matrix");

    LUDecomposition LU = new LUDecomposition(sigma);
    final double detSigma = LU.getDeterminant();
    double denominator = Math.pow(2.d * Math.PI, 0.5d * dim) * Math.pow(detSigma, 0.5d);
    if (denominator == 0.d) { // avoid divide by zero
        return 0.d;
    }

    final RealMatrix invSigma;
    DecompositionSolver solver = LU.getSolver();
    if (solver.isNonSingular() == false) {
        SingularValueDecomposition svd = new SingularValueDecomposition(sigma);
        invSigma = svd.getSolver().getInverse(); // least square solution
    } else {
        invSigma = solver.getInverse();
    }
    //EigenDecomposition eigen = new EigenDecomposition(sigma);
    //double detSigma = eigen.getDeterminant();
    //RealMatrix invSigma = eigen.getSolver().getInverse();

    RealVector diff = x.subtract(x_hat);
    RealVector premultiplied = invSigma.preMultiply(diff);
    double sum = premultiplied.dotProduct(diff);
    double numerator = Math.exp(-0.5d * sum);

    return numerator / denominator;
}

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * L = A x R//  w ww .  ja v a  2  s.co  m
 *
 * @return a matrix A that minimizes A x R - L
 */
@Nonnull
public static RealMatrix solve(@Nonnull final RealMatrix L, @Nonnull final RealMatrix R, final boolean exact)
        throws SingularMatrixException {
    LUDecomposition LU = new LUDecomposition(R);
    DecompositionSolver solver = LU.getSolver();
    final RealMatrix A;
    if (exact || solver.isNonSingular()) {
        A = LU.getSolver().solve(L);
    } else {
        SingularValueDecomposition SVD = new SingularValueDecomposition(R);
        A = SVD.getSolver().solve(L);
    }
    return A;
}

From source file:hivemall.utils.math.MatrixUtils.java

@Nonnull
public static RealMatrix inverse(@Nonnull final RealMatrix m, final boolean exact)
        throws SingularMatrixException {
    LUDecomposition LU = new LUDecomposition(m);
    DecompositionSolver solver = LU.getSolver();
    final RealMatrix inv;
    if (exact || solver.isNonSingular()) {
        inv = solver.getInverse();/* w w w. jav  a  2s  . com*/
    } else {
        SingularValueDecomposition SVD = new SingularValueDecomposition(m);
        inv = SVD.getSolver().getInverse();
    }
    return inv;
}

From source file:edu.tum.cs.vis.model.util.algorithm.ACCUM.java

/**
 * Calculate curvature for vertices of triangle
 * //from w w  w .j av  a  2  s  . c o m
 * @param curvatures
 *            vertex curvature mapping
 * @param tri
 *            triangle to calculate curvature for
 */
static void calculateCurvatureForTriangle(HashMap<Vertex, Curvature> curvatures, Triangle tri) {
    // Edges
    Vector3f e[] = tri.getEdges();

    // N-T-B coordinate system per face
    Vector3f t = new Vector3f(e[0]);
    t.normalize();
    Vector3f n = new Vector3f();
    n.cross(e[0], e[1]);
    Vector3f b = new Vector3f();
    b.cross(n, t);
    b.normalize();

    // Estimate curvature based on variation of normals
    // along edges
    float m[] = { 0, 0, 0 };
    double w[][] = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
    for (int j = 0; j < 3; j++) {

        float u = e[j].dot(t);
        float v = e[j].dot(b);
        w[0][0] += u * u;
        w[0][1] += u * v;
        // w[1][1] += v*v + u*u;
        // w[1][2] += u*v;
        w[2][2] += v * v;
        Vector3f dn = new Vector3f(tri.getPosition()[(j + 2) % 3].getNormalVector());
        dn.sub(tri.getPosition()[(j + 1) % 3].getNormalVector());
        float dnu = dn.dot(t);
        float dnv = dn.dot(b);
        m[0] += dnu * u;
        m[1] += dnu * v + dnv * u;
        m[2] += dnv * v;
    }
    w[1][1] = w[0][0] + w[2][2];
    w[1][2] = w[0][1];

    RealMatrix coefficients = new Array2DRowRealMatrix(w, false);
    DecompositionSolver solver = new LUDecomposition(coefficients).getSolver();
    if (!solver.isNonSingular()) {
        return;
    }

    RealVector constants = new ArrayRealVector(new double[] { m[0], m[1], m[2] }, false);
    RealVector solution = solver.solve(constants);

    m[0] = (float) solution.getEntry(0);
    m[1] = (float) solution.getEntry(1);
    m[2] = (float) solution.getEntry(2);

    // Push it back out to the vertices
    for (int j = 0; j < 3; j++) {
        Vertex vj = tri.getPosition()[j];

        float c1, c12, c2;
        float ret[] = proj_curv(t, b, m[0], m[1], m[2], curvatures.get(vj).getPrincipleDirectionMax(),
                curvatures.get(vj).getPrincipleDirectionMin());
        c1 = ret[0];
        c12 = ret[1];
        c2 = ret[2];

        Curvature c = curvatures.get(vj);

        double wt;
        if (j == 0)
            wt = tri.getCornerarea().x / vj.getPointarea();
        else if (j == 1)
            wt = tri.getCornerarea().y / vj.getPointarea();
        else
            wt = tri.getCornerarea().z / vj.getPointarea();

        synchronized (c) {
            c.setCurvatureMax((float) (c.getCurvatureMax() + wt * c1));
            c.setCurvatureMinMax((float) (c.getCurvatureMinMax() + wt * c12));
            c.setCurvatureMin((float) (c.getCurvatureMin() + wt * c2));
        }
    }
}

From source file:com.anhth12.lambda.common.math.LinearSystemSolver.java

public boolean isNonSingular(RealMatrix M) {
    QRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();

    return solver.isNonSingular();
}

From source file:com.cloudera.oryx.common.math.CommonsMathLinearSystemSolver.java

@Override
public boolean isNonSingular(RealMatrix M) {
    QRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    return solver.isNonSingular();
}

From source file:com.cloudera.oryx.common.math.LinearSystemSolver.java

public boolean isNonSingular(RealMatrix M) {
    QRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    return solver.isNonSingular();
}

From source file:com.anhth12.lambda.common.math.LinearSystemSolver.java

public Solver getSolver(RealMatrix M) {
    if (M == null) {
        return null;
    }//  www.  ja  va 2  s.  c o  m

    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);

    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
        return new Solver(solver);
    }
    int apparentRank = decomposition.getRank(0.01);

    log.warn(
            "{} x {} matrix is near-singular (threshold {}). Add more data or decrease the "
                    + "value of als.hyperparams.features, to <= about {}",
            M.getRowDimension(), M.getColumnDimension(), SINGULARITY_THRESHOLD, apparentRank);
    throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}

From source file:com.cloudera.oryx.common.math.CommonsMathLinearSystemSolver.java

@Override
public Solver getSolver(RealMatrix M) {
    if (M == null) {
        return null;
    }//from   www  .  j  a v  a  2  s. c o  m
    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
        return new CommonsMathSolver(solver);
    }
    // Otherwise try to report apparent rank
    int apparentRank = decomposition.getRank(0.01); // Better value?
    log.warn(
            "{} x {} matrix is near-singular (threshold {}). Add more data or decrease the value of model.features, "
                    + "to <= about {}",
            M.getRowDimension(), M.getColumnDimension(), SINGULARITY_THRESHOLD, apparentRank);
    throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}

From source file:com.cloudera.oryx.common.math.LinearSystemSolver.java

public Solver getSolver(RealMatrix M) {
    if (M == null) {
        return null;
    }//from w w w .  j  a  va 2  s .co  m
    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
        return new Solver(solver);
    }
    // Otherwise try to report apparent rank
    int apparentRank = decomposition.getRank(0.01); // Better value?
    log.warn(
            "{} x {} matrix is near-singular (threshold {}). Add more data or decrease the "
                    + "value of als.hyperparams.features, to <= about {}",
            M.getRowDimension(), M.getColumnDimension(), SINGULARITY_THRESHOLD, apparentRank);
    throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}