Example usage for org.apache.commons.math3.linear EigenDecomposition getImagEigenvalues

Introduction

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

Prototype

public double[] getImagEigenvalues() 

Source Link

Document

Gets a copy of the imaginary parts of the eigenvalues of the original matrix.

Usage

From source file:net.sf.dsp4j.octave_3_2_4.Eig.java

public static Complex[] eig(RealMatrix d) {
    EigenDecomposition eig = new EigenDecomposition(d);
    double[] realEigenvalues = eig.getRealEigenvalues();
    double[] imagEigenvalues = eig.getImagEigenvalues();

    final Complex[] result = new Complex[realEigenvalues.length];
    for (int i = 0; i < realEigenvalues.length; i++) {
        result[i] = new Complex(realEigenvalues[i], imagEigenvalues[i]);
    }// ww  w.  j a va2  s .c  o  m
    return result;
}

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From source file:edu.cmu.tetrad.search.TimeSeriesUtils.java

public static boolean allEigenvaluesAreSmallerThanOneInModulus(TetradMatrix mat) {

    double[] realEigenvalues = new double[0];
    double[] imagEigenvalues = new double[0];
    try {/* w w  w  . j av  a  2s .c o  m*/
        EigenDecomposition dec = new EigenDecomposition(mat.getRealMatrix());
        realEigenvalues = dec.getRealEigenvalues();
        imagEigenvalues = dec.getImagEigenvalues();
    } catch (MaxCountExceededException e) {
        e.printStackTrace();
    }

    for (int i = 0; i < realEigenvalues.length; i++) {
        double realEigenvalue = realEigenvalues[i];
        double imagEigenvalue = imagEigenvalues[i];
        System.out.println("Real eigenvalues are : " + realEigenvalue + " and imag part : " + imagEigenvalue);
        double modulus = Math.sqrt(Math.pow(realEigenvalue, 2) + Math.pow(imagEigenvalue, 2));

        if (modulus >= 1.0) {
            return false;
        }
    }
    return true;
}

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From source file:org.eclipse.dataset.LinearAlgebra.java

/**
 * @param a/*from w w  w  . j av  a2 s . c  o m*/
 * @return dataset of eigenvalues (can be double or complex double)
 */
public static Dataset calcEigenvalues(Dataset a) {
    EigenDecomposition evd = new EigenDecomposition(createRealMatrix(a));
    double[] rev = evd.getRealEigenvalues();

    if (evd.hasComplexEigenvalues()) {
        double[] iev = evd.getImagEigenvalues();
        return new ComplexDoubleDataset(rev, iev);
    }
    return new DoubleDataset(rev);
}

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From source file:org.eclipse.dataset.LinearAlgebra.java

/**
 * Calculate eigen-decomposition A = V D V^T
 * @param a/*  w w  w  .j  av a2  s . c  o  m*/
 * @return array of D eigenvalues (can be double or complex double) and V eigenvectors
 */
public static Dataset[] calcEigenDecomposition(Dataset a) {
    EigenDecomposition evd = new EigenDecomposition(createRealMatrix(a));
    Dataset[] results = new Dataset[2];

    double[] rev = evd.getRealEigenvalues();
    if (evd.hasComplexEigenvalues()) {
        double[] iev = evd.getImagEigenvalues();
        results[0] = new ComplexDoubleDataset(rev, iev);
    } else {
        results[0] = new DoubleDataset(rev);
    }
    results[1] = createDataset(evd.getV());
    return results;
}

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From source file:org.eclipse.january.dataset.LinearAlgebra.java

/**
 * @param a// w  w w.  java  2  s  .c  o m
 * @return dataset of eigenvalues (can be double or complex double)
 */
public static Dataset calcEigenvalues(Dataset a) {
    EigenDecomposition evd = new EigenDecomposition(createRealMatrix(a));
    double[] rev = evd.getRealEigenvalues();

    if (evd.hasComplexEigenvalues()) {
        double[] iev = evd.getImagEigenvalues();
        return DatasetFactory.createComplexDataset(ComplexDoubleDataset.class, rev, iev);
    }
    return DatasetFactory.createFromObject(rev);
}

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From source file:org.eclipse.january.dataset.LinearAlgebra.java

/**
 * Calculate eigen-decomposition A = V D V^T
 * @param a//from   w  ww . j  av a  2s.  c om
 * @return array of D eigenvalues (can be double or complex double) and V eigenvectors
 */
public static Dataset[] calcEigenDecomposition(Dataset a) {
    EigenDecomposition evd = new EigenDecomposition(createRealMatrix(a));
    Dataset[] results = new Dataset[2];

    double[] rev = evd.getRealEigenvalues();
    if (evd.hasComplexEigenvalues()) {
        double[] iev = evd.getImagEigenvalues();
        results[0] = DatasetFactory.createComplexDataset(ComplexDoubleDataset.class, rev, iev);
    } else {
        results[0] = DatasetFactory.createFromObject(rev);
    }
    results[1] = createDataset(evd.getV());
    return results;
}

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From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the eigen decomposition of a real matrix.
 * The eigen decomposition of matrix A is a set of two matrices: V and D such that 
 * A = V  D  VT. A, V and D are all m  m matrices.
 * @param a Given matrix./*from   ww  w  .  ja  v  a 2s.c o m*/
 * @return Result W/V arrays.
 */
public static Array[] eigen(Array a) {
    int m = a.getShape()[0];
    Array Wa;
    Array Va = Array.factory(DataType.DOUBLE, new int[] { m, m });
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    EigenDecomposition decomposition = new EigenDecomposition(matrix);
    double[] rev = decomposition.getRealEigenvalues();
    double[] iev = decomposition.getImagEigenvalues();
    if (decomposition.hasComplexEigenvalues()) {
        Wa = Array.factory(DataType.OBJECT, new int[] { m });
        for (int i = 0; i < m; i++) {
            Wa.setObject(i, new Complex(rev[i], iev[i]));
            RealVector v = decomposition.getEigenvector(i);
            for (int j = 0; j < v.getDimension(); j++) {
                Va.setDouble(i * m + j, v.getEntry(j));
            }
        }
    } else {
        Wa = Array.factory(DataType.DOUBLE, new int[] { m });
        for (int i = 0; i < m; i++) {
            Wa.setDouble(i, rev[i]);
            RealVector v = decomposition.getEigenvector(i);
            for (int j = 0; j < v.getDimension(); j++) {
                Va.setDouble(i * m + j, v.getEntry(j));
            }
        }
    }

    return new Array[] { Wa, Va };
}

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From source file:org.rhwlab.dispim.nucleus.NucleusData.java

static public boolean intersect(NucleusData nuc1, NucleusData nuc2) {
    RealMatrix qs1 = nuc1.quadraticSurfaceMatrix();
    RealMatrix qs2 = nuc2.quadraticSurfaceMatrix();

    LUDecomposition lu = new LUDecomposition(qs1);
    RealMatrix qs1Inv = lu.getSolver().getInverse();

    EigenDecomposition ed = new EigenDecomposition(qs1Inv.multiply(qs2));
    double[] realValues = ed.getRealEigenvalues();
    double[] imagValues = ed.getImagEigenvalues();

    // are there two distinct negative real eigenvalues

    for (int i = 0; i < realValues.length - 1; ++i) {
        for (int j = i; j < realValues.length; ++j) {
            if (realValues[i] < 0.0 && realValues[j] < 0.0 && imagValues[i] == 0.0 && imagValues[j] == 0.0
                    && realValues[i] != realValues[j]) {
                return false;
            }//from  ww  w  .  j  av  a 2s . c  o  m
        }
    }
    int sahdfuihs = 0;

    return true;
}

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