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linalg

Basic linear algebra method.

BSD 3-Clause License Copyright © 2019-2020, Daniel Nagel All rights reserved.

left_eigenvectors(matrix, nvals=None)

Estimate left eigenvectors.

Estimates the left eigenvectors and corresponding eigenvalues of a quadratic matrix.

Parameters:

  • matrix (ndarray) –

    Quadratic 2d matrix eigenvectors and eigenvalues or determined of.

  • nvals (int, default: None ) –

    Number of returned eigenvalues and -vectors. Using ensures probability of real valued matrices.

Returns:

  • eigenvalues ( ndarray ) –

    N eigenvalues sorted by their value (descending).

  • eigenvectors ( ndarray ) –

    N eigenvectors sorted by descending eigenvalues.

Source code in src/msmhelper/msm/utils/linalg.py 
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@decorit.alias('eigl')
def left_eigenvectors(matrix, nvals=None):
    """Estimate left eigenvectors.

    Estimates the left eigenvectors and corresponding eigenvalues of a
    quadratic matrix.

    Parameters
    ----------
    matrix : ndarray
        Quadratic 2d matrix eigenvectors and eigenvalues or determined of.
    nvals : int, optional
        Number of returned eigenvalues and -vectors. Using ensures probability
        of real valued matrices.

    Returns
    -------
    eigenvalues : ndarray
        N eigenvalues sorted by their value (descending).
    eigenvectors : ndarray
        N eigenvectors sorted by descending eigenvalues.

    """
    matrix = np.asarray(matrix)
    return _eigenvectors(matrix.transpose(), nvals)

right_eigenvectors(matrix, nvals=None)

Estimate right eigenvectors.

Estimates the right eigenvectors and corresponding eigenvalues of a quadratic matrix.

Parameters:

  • matrix (ndarray) –

    Quadratic 2d matrix eigenvectors and eigenvalues or determined of.

  • nvals (int, default: None ) –

    Number of returned eigenvalues and -vectors. Using ensures probability of real valued matrices.

Returns:

  • eigenvalues ( ndarray ) –

    N eigenvalues sorted by their value (descending).

  • eigenvectors ( ndarray ) –

    N eigenvectors sorted by descending eigenvalues.

Source code in src/msmhelper/msm/utils/linalg.py 
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@decorit.alias('eig')
def right_eigenvectors(matrix, nvals=None):
    """Estimate right eigenvectors.

    Estimates the right eigenvectors and corresponding eigenvalues of a
    quadratic matrix.

    Parameters
    ----------
    matrix : ndarray
        Quadratic 2d matrix eigenvectors and eigenvalues or determined of.
    nvals : int, optional
        Number of returned eigenvalues and -vectors. Using ensures probability
        of real valued matrices.

    Returns
    -------
    eigenvalues : ndarray
        N eigenvalues sorted by their value (descending).
    eigenvectors : ndarray
        N eigenvectors sorted by descending eigenvalues.

    """
    matrix = np.asarray(matrix)
    return _eigenvectors(matrix, nvals)

left_eigenvalues(matrix, nvals=None)

Estimate left eigenvalues.

Estimates the left eigenvalues of a quadratic matrix.

Parameters:

  • matrix (ndarray) –

    Quadratic 2d matrix eigenvalues or determined of.

  • nvals (int, default: None ) –

    Number of returned eigenvalues and -vectors. Using ensures probability of real valued matrices.

Returns:

  • eigenvalues ( ndarray ) –

    N eigenvalues sorted by their value (descending).

Source code in src/msmhelper/msm/utils/linalg.py 
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@decorit.alias('eiglvals')
def left_eigenvalues(matrix, nvals=None):
    """Estimate left eigenvalues.

    Estimates the left eigenvalues of a quadratic matrix.

    Parameters
    ----------
    matrix : ndarray
        Quadratic 2d matrix eigenvalues or determined of.
    nvals : int, optional
        Number of returned eigenvalues and -vectors. Using ensures probability
        of real valued matrices.

    Returns
    -------
    eigenvalues : ndarray
        N eigenvalues sorted by their value (descending).

    """
    matrix = np.asarray(matrix)
    return _eigenvalues(np.transpose(matrix), nvals)

right_eigenvalues(matrix, nvals=None)

Estimate right eigenvalues.

Estimates the right eigenvalues of a quadratic matrix.

Parameters:

  • matrix (ndarray) –

    Quadratic 2d matrix eigenvalues or determined of.

  • nvals (int, default: None ) –

    Number of returned eigenvalues and -vectors. Using ensures probability of real valued matrices.

Returns:

  • eigenvalues ( ndarray ) –

    N eigenvalues sorted by their value (descending).

Source code in src/msmhelper/msm/utils/linalg.py 
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@decorit.alias('eigvals')
def right_eigenvalues(matrix, nvals=None):
    """Estimate right eigenvalues.

    Estimates the right eigenvalues of a quadratic matrix.

    Parameters
    ----------
    matrix : ndarray
        Quadratic 2d matrix eigenvalues or determined of.
    nvals : int, optional
        Number of returned eigenvalues and -vectors. Using ensures probability
        of real valued matrices.

    Returns
    -------
    eigenvalues : ndarray
        N eigenvalues sorted by their value (descending).

    """
    matrix = np.asarray(matrix)
    return _eigenvalues(matrix, nvals)