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ift
NIFTy
Commits
646f0dbf
Commit
646f0dbf
authored
Aug 25, 2019
by
Philipp Arras
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Add Kl test
parent
065ec7ac
Pipeline
#54304
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in 8 minutes and 20 seconds
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test/test_kl.py
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test/test_kl.py
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646f0dbf
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import
numpy
as
np
import
nifty5
as
ift
from
numpy.testing
import
assert_
,
assert_allclose
import
pytest
pmp
=
pytest
.
mark
.
parametrize
@
pmp
(
'constants'
,
([],
[
'a'
],
[
'b'
],
[
'a'
,
'b'
]))
@
pmp
(
'point_estimates'
,
([],
[
'a'
],
[
'b'
],
[
'a'
,
'b'
]))
@
pmp
(
'mirror_samples'
,
(
True
,
False
))
def
test_kl
(
constants
,
point_estimates
,
mirror_samples
):
np
.
random
.
seed
(
42
)
dom
=
ift
.
RGSpace
((
12
,),
(
2.12
))
op0
=
ift
.
HarmonicSmoothingOperator
(
dom
,
3
)
op
=
ift
.
ducktape
(
dom
,
None
,
'a'
)
*
(
op0
.
ducktape
(
'b'
))
lh
=
ift
.
GaussianEnergy
(
domain
=
op
.
target
)
@
op
ic
=
ift
.
GradientNormController
(
iteration_limit
=
5
)
h
=
ift
.
StandardHamiltonian
(
lh
,
ic_samp
=
ic
)
mean0
=
ift
.
from_random
(
'normal'
,
h
.
domain
)
nsamps
=
2
kl
=
ift
.
MetricGaussianKL
(
mean0
,
h
,
nsamps
,
constants
=
constants
,
point_estimates
=
point_estimates
,
mirror_samples
=
mirror_samples
,
napprox
=
0
)
klpure
=
ift
.
MetricGaussianKL
(
mean0
,
h
,
nsamps
,
mirror_samples
=
mirror_samples
,
napprox
=
0
,
_samples
=
kl
.
samples
)
# Test value
assert_allclose
(
kl
.
value
,
klpure
.
value
)
# Test gradient
for
kk
in
h
.
domain
.
keys
():
res0
=
klpure
.
gradient
.
to_global_data
()[
kk
]
if
kk
in
constants
:
res0
=
0
*
res0
res1
=
kl
.
gradient
.
to_global_data
()[
kk
]
assert_allclose
(
res0
,
res1
)
# Test number of samples
expected_nsamps
=
2
*
nsamps
if
mirror_samples
else
nsamps
assert_
(
len
(
kl
.
samples
)
==
expected_nsamps
)
# Test point_estimates (after drawing samples)
for
kk
in
point_estimates
:
for
ss
in
kl
.
samples
:
ss
=
ss
.
to_global_data
()[
kk
]
assert_allclose
(
ss
,
0
*
ss
)
# Test constants (after some minimization)
cg
=
ift
.
GradientNormController
(
iteration_limit
=
5
)
minimizer
=
ift
.
NewtonCG
(
cg
)
kl
,
_
=
minimizer
(
kl
)
diff
=
(
mean0
-
kl
.
position
).
to_global_data
()
for
kk
in
constants
:
assert_allclose
(
diff
[
kk
],
0
*
diff
[
kk
])
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