msuzen / bristol Goto Github PK

Update python notebook after gen_module as ergodicity related methods moved to ergodicity module.

add cPSE measure

CI is broken as travis org is ceased to exist.

moving to static type hint:
https://docs.python.org/3/library/typing.html

Introduce MANIFEST.in

include README.md
include LICENSE.txt

Circular module needs unit tests.

Given generated matrix, visualise in colour mode:

• color/bw
• historgram

Store the object. Preferable use matplotlib.

Close and Join the child processes.

Parallel random matrix tools and complexity for deep learning. Drop other clutter from the description.

Along with internal representation

Referbish.

Gaussian orthogonal ensemble (GOE), Gaussian unitary ensemble (GUE) and Gaussian symplectic ensemble (GSE)

Detailed doc directory using sphyx with implementation details.

As defined in
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles and
notebook

Introduce density argument for KL/TM metric

Introduce cPSE in PyTorch monitor during training epochs.

When computing COE, and CSE, provide an option to use already existing CUE.

As defined in
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles and
notebook

Introduce mean, variance in matrix generation.

Some unit tests might be logically wrong

introduce ergodicitiy module, using functions from the notebook.

Eigenvalues can be generated in parallel but not single matrices.

Example data set generation module, for example see code here. Note that this is older code, so Circular module name capitalized.

state dict can be used to produce cPSE.

Use docstring compatible structure, such as the following taken from scipy:

def angle(z, deg=0):
    """
    Return the angle of the complex argument.

    Parameters
    ----------
    z : array_like
        A complex number or sequence of complex numbers.
    deg : bool, optional
        Return angle in degrees if True, radians if False (default).

    Returns
    -------
    angle : ndarray or scalar
        The counterclockwise angle from the positive real axis on
        the complex plane, with dtype as numpy.float64.

    See Also
    --------
    arctan2
    absolute



    Examples
    --------
    >>> np.angle([1.0, 1.0j, 1+1j])               # in radians
    array([ 0.        ,  1.57079633,  0.78539816])
    >>> np.angle(1+1j, deg=True)                  # in degrees
    45.0

    """
    if deg:
        fact = 180/pi
    else:
        fact = 1.0

Introduce random number generation utils for circular ensembles. Possibly merge with #13

https://github.com/msuzen/bristol/blob/master/works/mixedMatrixEnsembles/deviation_semicirclelaw.ipynb

Drop author names within code as contributors covers this.