Examples of org.apache.commons.math3.linear.EigenDecomposition

org.apache.commons.math3.linear.EigenDecomposition
nist.gov/javanumerics/jama/">JAMA library, with the following changes:
- a {@link #getVT() getVt} method has been added,
- two {@link #getRealEigenvalue(int) getRealEigenvalue} and {@link #getImagEigenvalue(int) getImagEigenvalue} methods to pick up a single eigenvalue have been added,
- a {@link #getEigenvector(int) getEigenvector} method to pick up a singleeigenvector has been added,
- a {@link #getDeterminant() getDeterminant} method has been added.
- a {@link #getSolver() getSolver} method has been added.
As of 3.1, this class supports general real matrices (both symmetric and non-symmetric):

If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is diagonal and the eigenvector matrix V is orthogonal, i.e. A = V.multiply(D.multiply(V.transpose())) and V.multiply(V.transpose()) equals the identity matrix.

If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2 blocks:
```
 [lambda, mu    ] [   -mu, lambda] 
```
The columns of V represent the eigenvectors in the sense that A*V = V*D, i.e. A.multiply(V) equals V.multiply(D). The matrix V may be badly conditioned, or even singular, so the validity of the equation A = V*D*inverse(V) depends upon the condition of V.

This implementation is based on the paper by A. Drubrulle, R.S. Martin and J.H. Wilkinson "The Implicit QL Algorithm" in Wilksinson and Reinsch (1971) Handbook for automatic computation, vol. 2, Linear algebra, Springer-Verlag, New-York
@see MathWorld @see Wikipedia @since 2.0 (changed to concrete class in 3.0)

        if (ccov1 + ccovmu + negccov > 0 &&
            (iterations % 1. / (ccov1 + ccovmu + negccov) / dimension / 10.) < 1) {
            // to achieve O(N^2)
            C = triu(C, 0).add(triu(C, 1).transpose());
            // enforce symmetry to prevent complex numbers
            final EigenDecomposition eig = new EigenDecomposition(C);
            B = eig.getV(); // eigen decomposition, B==normalized eigenvectors
            D = eig.getD();
            diagD = diag(D);
            if (min(diagD) <= 0) {
                for (int i = 0; i < dimension; i++) {
                    if (diagD.getEntry(i, 0) < 0) {
                        diagD.setEntry(i, 0, 0);

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            for (int i = 0; i < dim; i++) {
                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
        }
    }

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            for (int i = 0; i < dim; i++) {
               sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
        }
    }

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        this.means = MathArrays.copyOf(means);


        covarianceMatrix = new Array2DRowRealMatrix(covariances);


        // Covariance matrix eigen decomposition.
        final EigenDecomposition covMatDec = new EigenDecomposition(covarianceMatrix);


        // Compute and store the inverse.
        covarianceMatrixInverse = covMatDec.getSolver().getInverse();
        // Compute and store the determinant.
        covarianceMatrixDeterminant = covMatDec.getDeterminant();


        // Eigenvalues of the covariance matrix.
        final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();


        for (int i = 0; i < covMatEigenvalues.length; i++) {
            if (covMatEigenvalues[i] < 0) {
                throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
            }
        }


        // Matrix where each column is an eigenvector of the covariance matrix.
        final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }


        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

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            for (int i = 0; i < dim; i++) {
                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
        }
    }

View Full Code Here

        if (ccov1 + ccovmu + negccov > 0 &&
            (iterations % 1. / (ccov1 + ccovmu + negccov) / dimension / 10.) < 1) {
            // to achieve O(N^2)
            C = triu(C, 0).add(triu(C, 1).transpose());
            // enforce symmetry to prevent complex numbers
            final EigenDecomposition eig = new EigenDecomposition(C);
            B = eig.getV(); // eigen decomposition, B==normalized eigenvectors
            D = eig.getD();
            diagD = diag(D);
            if (min(diagD) <= 0) {
                for (int i = 0; i < dimension; i++) {
                    if (diagD.getEntry(i, 0) < 0) {
                        diagD.setEntry(i, 0, 0);

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        if (ccov1 + ccovmu + negccov > 0 &&
            (iterations % 1. / (ccov1 + ccovmu + negccov) / dimension / 10.) < 1) {
            // to achieve O(N^2)
            C = triu(C, 0).add(triu(C, 1).transpose());
            // enforce symmetry to prevent complex numbers
            final EigenDecomposition eig = new EigenDecomposition(C);
            B = eig.getV(); // eigen decomposition, B==normalized eigenvectors
            D = eig.getD();
            diagD = diag(D);
            if (min(diagD) <= 0) {
                for (int i = 0; i < dimension; i++) {
                    if (diagD.getEntry(i, 0) < 0) {
                        diagD.setEntry(i, 0, 0);

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        this.means = MathArrays.copyOf(means);


        covarianceMatrix = new Array2DRowRealMatrix(covariances);


        // Covariance matrix eigen decomposition.
        final EigenDecomposition covMatDec = new EigenDecomposition(covarianceMatrix);


        // Compute and store the inverse.
        covarianceMatrixInverse = covMatDec.getSolver().getInverse();
        // Compute and store the determinant.
        covarianceMatrixDeterminant = covMatDec.getDeterminant();


        // Eigenvalues of the covariance matrix.
        final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();


        for (int i = 0; i < covMatEigenvalues.length; i++) {
            if (covMatEigenvalues[i] < 0) {
                throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
            }
        }


        // Matrix where each column is an eigenvector of the covariance matrix.
        final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }


        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();

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  private final double[] eigenvalues;
  private final double[][] uHat;


  public EigenSolverWrapper(double[][] bbt) {
    int dim = bbt.length;
    EigenDecomposition evd2 = new EigenDecomposition(new Array2DRowRealMatrix(bbt));
    eigenvalues = evd2.getRealEigenvalues();
    RealMatrix uHatrm = evd2.getV();
    uHat = new double[dim][];
    for (int i = 0; i < dim; i++) {
      uHat[i] = uHatrm.getRow(i);
    }
  }

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            for (int i = 0; i < dim; i++) {
               sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
        }
    }

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