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

Introduction

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

Prototype

boolean isSquare();

Source Link

Document

Is this a square matrix?

Usage

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

/**
 * @see http://en.wikipedia.org/wiki/Machine_epsilon#Approximation_using_Java
 *//*  w ww.  j  a v a2  s .c o  m*/
//   public static final float calculateFloatMachineEpsilon() {
//      float eps = 1.0f;
//      do {
//         eps /= 2.0f;
//      } while ((float) (1.0 + (eps / 2.0)) != 1.0);
//      Log.d(MainActivity.JOPTIMIZER_LOGTAG,"Calculated float machine epsilon: " + eps);
//      return eps;
//   }

public static RealMatrix squareMatrixInverse(RealMatrix M) throws SingularMatrixException {
    if (!M.isSquare()) {
        throw new IllegalArgumentException("Not square matrix!");
    }

    // try commons-math cholesky
    try {
        CholeskyDecomposition cd = new CholeskyDecomposition(M);
        return cd.getSolver().getInverse();
    } catch (SingularMatrixException e) {
        throw e;
    } catch (Exception e) {
        Log.d(MainActivity.JOPTIMIZER_LOGTAG, e.getMessage());
    }

    // try joptimizer cholesky
    try {
        CholeskyFactorization cd = new CholeskyFactorization(M.getData());
        double[][] MInv = cd.getInverse();
        return MatrixUtils.createRealMatrix(MInv);
    } catch (Exception e) {
        Log.d(MainActivity.JOPTIMIZER_LOGTAG, e.getMessage());
    }

    // try LU
    try {
        LUDecomposition ld = new LUDecomposition(M);
        return ld.getSolver().getInverse();
    } catch (SingularMatrixException e) {
        throw e;
    } catch (Exception e) {
        Log.d(MainActivity.JOPTIMIZER_LOGTAG, e.getMessage());
    }

    // try QR
    try {
        QRDecomposition qr = new org.apache.commons.math3.linear.QRDecomposition(M);
        return qr.getSolver().getInverse();
    } catch (SingularMatrixException e) {
        throw e;
    } catch (Exception e) {
        Log.d(MainActivity.JOPTIMIZER_LOGTAG, e.getMessage());
    }

    return null;
}

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From source file:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * The method determines if the given matrix is positive definite.
 * The matrix must be square.//from  w  w w.j  av  a 2  s . c  o m
 * @param matrix
 * @return true if the matrix is positive definite.
 */
public static boolean isPositiveDefinite(RealMatrix matrix) {
    if (matrix == null || !matrix.isSquare()) {
        throw new IllegalArgumentException("Matrix must be non-null, " + "square. ");
    }
    double[] eigenValues = new EigenDecomposition(matrix).getRealEigenvalues();

    // if all eigenValues are positive, we return true
    boolean isPositiveDefinite = true;
    for (int i = 0; i < eigenValues.length; i++) {
        if (eigenValues[i] <= -EPSILON) {
            isPositiveDefinite = false;
            break;
        }
    }
    return isPositiveDefinite;
}

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From source file:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * The method determines if the given matrix is positive semidefinite.
 * The matrix must be square.// w ww .j a  v a  2 s.co m
 * @param matrix
 * @return true if the matrix is positive semidefinite.
 */
public static boolean isPositiveSemidefinite(RealMatrix matrix) {
    if (matrix == null || !matrix.isSquare()) {
        throw new IllegalArgumentException("Matrix must be non-null, " + "square. ");
    }
    double[] eigenValues = new EigenDecomposition(matrix).getRealEigenvalues();

    // if all eigenValues are nonnegative, we return true
    boolean isPositiveSemidefinite = true;
    for (int i = 0; i < eigenValues.length; i++) {
        if (eigenValues[i] < -EPSILON) {
            isPositiveSemidefinite = false;
            break;
        }
    }
    return isPositiveSemidefinite;
}

---

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  2  s .  c o  m
 * @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;
}

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From source file:info.debatty.java.datasets.sift.Matrix.java

/**
 * @param A a square matrix.//from   w  w  w .  j  av  a  2s  .  c o m
 * @return the inverse of A or null if A is non-square or singular.
 */
public static double[][] inverse(final double[][] A) {
    RealMatrix M = MatrixUtils.createRealMatrix(A);
    if (!M.isSquare()) {
        return null;
    } else {
        double[][] Ai = null;
        try {
            RealMatrix Mi = MatrixUtils.inverse(M); //new LUDecomposition(M).getSolver().getInverse();
            Ai = Mi.getData();
        } catch (SingularMatrixException e) {
        }
        return Ai;
    }
}

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From source file:imagingbook.lib.math.Matrix.java

/**
 * @param A a square matrix./*from  w  w  w.  ja  v a 2 s  . com*/
 * @return the inverse of A or null if A is non-square or singular.
 */
public static double[][] inverse(final double[][] A) {
    RealMatrix M = MatrixUtils.createRealMatrix(A);
    if (!M.isSquare())
        return null;
    else {
        double[][] Ai = null;
        try {
            RealMatrix Mi = new LUDecomposition(M).getSolver().getInverse();
            Ai = Mi.getData();
        } catch (SingularMatrixException e) {
        }
        return Ai;
    }
}

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From source file:edu.cudenver.bios.matrix.test.TestMatrixOrthonormalization.java

/**
 * Verify that the Q'Q = I for the Q matrix produced by the
 * orthonormalization// w w w.  ja  v  a 2 s  .  c o  m
 */
public void testQQisIdentity() {
    RealMatrix Q = norm.getQ();

    // verify that Q'Q = identity
    RealMatrix shouldBeIdentityMatrix = Q.transpose().multiply(Q);
    // make sure the matrix is sqaure
    if (!shouldBeIdentityMatrix.isSquare()) {
        fail();
    }
    // make sure the diagonal elements are one (within tolerance), and off diagonals
    // are zero (within tolerance)
    for (int r = 0; r < shouldBeIdentityMatrix.getRowDimension(); r++) {
        for (int c = 0; c < shouldBeIdentityMatrix.getColumnDimension(); c++) {
            double shouldBeValue = (r == c) ? 1 : 0;
            if (Precision.compareTo(shouldBeIdentityMatrix.getEntry(r, c), shouldBeValue, TOLERANCE) != 0)
                fail();
        }
    }
    assertTrue(true);
}

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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/*from  ww w  . j  a  v  a 2s  .  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();
}

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From source file:es.csic.iiia.planes.util.InverseWishartDistribution.java

/**
 * Builds a new Inverse Wishart distribution with the given scale and degrees of freedom.
 *
 * @param scaleMatrix scale matrix./*w  ww  .  j a va 2  s. co  m*/
 * @param df degrees of freedom.
 */
public InverseWishartDistribution(RealMatrix scaleMatrix, double df) {
    if (!scaleMatrix.isSquare()) {
        throw new RuntimeException("scaleMatrix must be square.");
    }

    this.scaleMatrix = scaleMatrix;
    this.df = df;
    this.random = new Well19937c();
    initialize();
}

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

/**
 * Compute a Pillai Bartlett Trace statistic
 * /*from www  .j  a va  2 s .c  o m*/
 * @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();
}

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