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flake8 style changes

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This commit is contained in:
Fabian Joswig 2021-10-11 12:22:58 +01:00
parent 43f85efff5
commit 57c6a07801
9 changed files with 87 additions and 128 deletions
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28
pyerrors/linalg.py
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@ -6,15 +6,17 @@ import autograd.numpy as anp # Thinly-wrapped numpy
from .pyerrors import derived_observable
### This code block is directly taken from the current master branch of autograd and remains
# This code block is directly taken from the current master branch of autograd and remains
# only until the new version is released on PyPi
from functools import partial
from autograd.extend import defvjp
_dot = partial(anp.einsum, '...ij,...jk->...ik')
# batched diag
_diag = lambda a: anp.eye(a.shape[-1])*a
_diag = lambda a: anp.eye(a.shape[-1]) * a
# batched diagonal, similar to matrix_diag in tensorflow
def _matrix_diag(a):
reps = anp.array(a.shape)
reps[:-1] = 1
@ -24,14 +26,17 @@ def _matrix_diag(a):
# https://arxiv.org/pdf/1701.00392.pdf Eq(4.77)
# Note the formula from Sec3.1 in https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf is incomplete
def grad_eig(ans, x):
"""Gradient of a general square (complex valued) matrix"""
e, u = ans # eigenvalues as 1d array, eigenvectors in columns
e, u = ans # eigenvalues as 1d array, eigenvectors in columns
n = e.shape[-1]
def vjp(g):
ge, gu = g
ge = _matrix_diag(ge)
f = 1/(e[..., anp.newaxis, :] - e[..., :, anp.newaxis] + 1.e-20)
f = 1 / (e[..., anp.newaxis, :] - e[..., :, anp.newaxis] + 1.e-20)
f -= _diag(f)
ut = anp.swapaxes(u, -1, -2)
r1 = f * _dot(ut, gu)
@ -43,8 +48,10 @@ def grad_eig(ans, x):
# but the derivative should be real in real input case when imaginary delta is forbidden
return r
return vjp
defvjp(anp.linalg.eig, grad_eig)
### End of the code block from autograd.master
# End of the code block from autograd.master
def scalar_mat_op(op, obs, **kwargs):
@ -92,8 +99,8 @@ def eig(obs, **kwargs):
"""Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig."""
if kwargs.get('num_grad') is True:
return _num_diff_eig(obs, **kwargs)
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
w = derived_observable(lambda x, **kwargs: anp.real(anp.linalg.eig(x)[0]), obs)
return w
@ -214,7 +221,6 @@ def _num_diff_eigh(obs, **kwargs):
for i in range(dim):
res_vec.append(derived_observable(_mat, raveled_obs, n=0, i=i, **kwargs))
res_mat = []
for i in range(dim):
row = []
@ -244,7 +250,7 @@ def _num_diff_eig(obs, **kwargs):
res = np.linalg.eig(np.array(mat))[n]
if n == 0:
# Discard imaginary part of eigenvalue here
# Discard imaginary part of eigenvalue here
return np.real(res[kwargs.get('i')])
else:
return res[kwargs.get('i')][kwargs.get('j')]
@ -260,8 +266,8 @@ def _num_diff_eig(obs, **kwargs):
res_vec = []
for i in range(dim):
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
res_vec.append(derived_observable(_mat, raveled_obs, n=0, i=i, **kwargs))
return np.array(res_vec) @ np.identity(dim)