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ift
NIFTy
Commits
2005e30f
Commit
2005e30f
authored
Sep 26, 2019
by
Martin Reinecke
Browse files
more merges from operator_spectra
parent
d0c8860d
Pipeline
#61053
passed with stages
in 8 minutes and 58 seconds
Changes
4
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
nifty5/minimization/metric_gaussian_kl.py
View file @
2005e30f
...
...
@@ -18,6 +18,8 @@
from
..
import
utilities
from
..linearization
import
Linearization
from
..operators.energy_operators
import
StandardHamiltonian
from
..probing
import
approximation2endo
from
..sugar
import
makeOp
from
.energy
import
Energy
...
...
@@ -56,6 +58,9 @@ class MetricGaussianKL(Energy):
as they are equally legitimate samples. If true, the number of used
samples doubles. Mirroring samples stabilizes the KL estimate as
extreme sample variation is counterbalanced. Default is False.
napprox : int
Number of samples for computing preconditioner for sampling. No
preconditioning is done by default.
_samples : None
Only a parameter for internal uses. Typically not to be set by users.
...
...
@@ -67,12 +72,13 @@ class MetricGaussianKL(Energy):
See also
--------
Metric Gaussian Variational Inference (FIXME in preparation)
`Metric Gaussian Variational Inference`, Jakob Knollmüller,
Torsten A. Enßlin, `<https://arxiv.org/abs/1901.11033>`_
"""
def
__init__
(
self
,
mean
,
hamiltonian
,
n_samples
,
constants
=
[],
point_estimates
=
[],
mirror_samples
=
False
,
_samples
=
None
):
napprox
=
0
,
_samples
=
None
):
super
(
MetricGaussianKL
,
self
).
__init__
(
mean
)
if
not
isinstance
(
hamiltonian
,
StandardHamiltonian
):
...
...
@@ -91,12 +97,15 @@ class MetricGaussianKL(Energy):
if
_samples
is
None
:
met
=
hamiltonian
(
Linearization
.
make_partial_var
(
mean
,
point_estimates
,
True
)).
metric
if
napprox
>
1
:
met
.
_approximation
=
makeOp
(
approximation2endo
(
met
,
napprox
))
_samples
=
tuple
(
met
.
draw_sample
(
from_inverse
=
True
)
for
_
in
range
(
n_samples
))
if
mirror_samples
:
_samples
+=
tuple
(
-
s
for
s
in
_samples
)
self
.
_samples
=
_samples
# FIXME Use simplify for constant input instead
self
.
_lin
=
Linearization
.
make_partial_var
(
mean
,
constants
)
v
,
g
=
None
,
None
for
s
in
self
.
_samples
:
...
...
@@ -110,11 +119,12 @@ class MetricGaussianKL(Energy):
self
.
_val
=
v
/
len
(
self
.
_samples
)
self
.
_grad
=
g
*
(
1.
/
len
(
self
.
_samples
))
self
.
_metric
=
None
self
.
_napprox
=
napprox
def
at
(
self
,
position
):
return
MetricGaussianKL
(
position
,
self
.
_hamiltonian
,
0
,
self
.
_constants
,
self
.
_point_estimates
,
_samples
=
self
.
_samples
)
napprox
=
self
.
_napprox
,
_samples
=
self
.
_samples
)
@
property
def
value
(
self
):
...
...
@@ -129,8 +139,12 @@ class MetricGaussianKL(Energy):
lin
=
self
.
_lin
.
with_want_metric
()
mymap
=
map
(
lambda
v
:
self
.
_hamiltonian
(
lin
+
v
).
metric
,
self
.
_samples
)
self
.
_metric
=
utilities
.
my_sum
(
mymap
)
self
.
_metric
=
self
.
_metric
.
scale
(
1.
/
len
(
self
.
_samples
))
self
.
_unscaled_metric
=
utilities
.
my_sum
(
mymap
)
self
.
_metric
=
self
.
_unscaled_metric
.
scale
(
1.
/
len
(
self
.
_samples
))
def
unscaled_metric
(
self
):
self
.
_get_metric
()
return
self
.
_unscaled_metric
,
1
/
len
(
self
.
_samples
)
def
apply_metric
(
self
,
x
):
self
.
_get_metric
()
...
...
nifty5/operators/simple_linear_operators.py
View file @
2005e30f
...
...
@@ -326,7 +326,7 @@ class NullOperator(LinearOperator):
return
self
.
_nullfield
(
self
.
_tgt
(
mode
))
class
_
PartialExtractor
(
LinearOperator
):
class
PartialExtractor
(
LinearOperator
):
def
__init__
(
self
,
domain
,
target
):
if
not
isinstance
(
domain
,
MultiDomain
):
raise
TypeError
(
"MultiDomain expected"
)
...
...
@@ -335,7 +335,7 @@ class _PartialExtractor(LinearOperator):
self
.
_domain
=
domain
self
.
_target
=
target
for
key
in
self
.
_target
.
keys
():
if
not
(
self
.
_domain
[
key
]
is
not
self
.
_target
[
key
]
)
:
if
self
.
_domain
[
key
]
is
not
self
.
_target
[
key
]:
raise
ValueError
(
"domain mismatch"
)
self
.
_capability
=
self
.
TIMES
|
self
.
ADJOINT_TIMES
...
...
nifty5/probing.py
View file @
2005e30f
...
...
@@ -15,9 +15,10 @@
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
from
.multi_field
import
MultiField
from
.operators.endomorphic_operator
import
EndomorphicOperator
from
.operators.operator
import
Operator
from
.sugar
import
from_random
from
.sugar
import
from_global_data
,
from_random
class
StatCalculator
(
object
):
...
...
@@ -134,3 +135,17 @@ def probe_diagonal(op, nprobes, random_type="pm1"):
x
=
from_random
(
random_type
,
op
.
domain
)
sc
.
add
(
op
(
x
).
conjugate
()
*
x
)
return
sc
.
mean
def
approximation2endo
(
op
,
nsamples
):
print
(
'Calculate preconditioner'
)
sc
=
StatCalculator
()
for
_
in
range
(
nsamples
):
sc
.
add
(
op
.
draw_sample
())
approx
=
sc
.
var
dct
=
approx
.
to_dict
()
for
kk
in
dct
:
foo
=
dct
[
kk
].
to_global_data_rw
()
foo
[
foo
==
0
]
=
1
dct
[
kk
]
=
from_global_data
(
dct
[
kk
].
domain
,
foo
)
return
MultiField
.
from_dict
(
dct
)
test/test_kl.py
0 → 100644
View file @
2005e30f
# 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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