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License: GNU General Public License v3.0
Parallel random matrix tools and complexity for deep learning
License: GNU General Public License v3.0
CI is broken as travis org is ceased to exist.
Referbish.
Update python notebook after gen_module
as ergodicity related methods moved to ergodicity
module.
Parallel random matrix tools and complexity for deep learning. Drop other clutter from the description.
Example data set generation module, for example see code here. Note that this is older code, so Circular
module name capitalized.
introduce ergodicitiy module, using functions from the notebook.
Introduce density argument for KL/TM metric
When computing COE, and CSE, provide an option to use already existing CUE.
state dict can be used to produce cPSE.
Detailed doc
directory using sphyx with implementation details.
Introduce MANIFEST.in
include README.md
include LICENSE.txt
Given generated matrix, visualise in colour mode:
color
/bw
historgram
Store the object. Preferable use matplotlib
.
add cPSE measure
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
Close and Join the child processes.
Some unit tests might be logically wrong
As defined in
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles and
notebook
As defined in
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles and
notebook
Gaussian orthogonal ensemble (GOE), Gaussian unitary ensemble (GUE) and Gaussian symplectic ensemble (GSE)
Introduce random number generation utils for circular ensembles. Possibly merge with #13
Introduce cPSE in PyTorch monitor during training epochs.
Along with internal representation
Introduce mean, variance in matrix generation.
Eigenvalues can be generated in parallel but not single matrices.
Drop author names within code as contributors covers this.
Circular module needs unit tests.
moving to static type hint:
https://docs.python.org/3/library/typing.html
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