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ift
NIFTy
Commits
03c31669
Commit
03c31669
authored
Jan 15, 2019
by
Jakob Knollmueller
Browse files
renaming KL and Hamiltonian
parent
5689ba2d
Changes
9
Hide whitespace changes
Inline
Side-by-side
demos/bernoulli_demo.py
View file @
03c31669
...
...
@@ -74,7 +74,7 @@ if __name__ == '__main__':
ic_sampling
=
ift
.
GradientNormController
(
iteration_limit
=
100
)
# Minimize the Hamiltonian
H
=
ift
.
Hamiltonian
(
likelihood
,
ic_sampling
)
H
=
ift
.
Standard
Hamiltonian
(
likelihood
,
ic_sampling
)
H
=
ift
.
EnergyAdapter
(
position
,
H
,
want_metric
=
True
)
# minimizer = ift.L_BFGS(ic_newton)
H
,
convergence
=
minimizer
(
H
)
...
...
demos/getting_started_2.py
View file @
03c31669
...
...
@@ -99,7 +99,7 @@ if __name__ == '__main__':
minimizer
=
ift
.
NewtonCG
(
ic_newton
)
# Compute MAP solution by minimizing the information Hamiltonian
H
=
ift
.
Hamiltonian
(
likelihood
)
H
=
ift
.
Standard
Hamiltonian
(
likelihood
)
initial_position
=
ift
.
from_random
(
'normal'
,
domain
)
H
=
ift
.
EnergyAdapter
(
initial_position
,
H
,
want_metric
=
True
)
H
,
convergence
=
minimizer
(
H
)
...
...
demos/getting_started_3.py
View file @
03c31669
...
...
@@ -100,10 +100,10 @@ if __name__ == '__main__':
# Set up likelihood and information Hamiltonian
likelihood
=
ift
.
GaussianEnergy
(
mean
=
data
,
covariance
=
N
)(
signal_response
)
H
=
ift
.
Hamiltonian
(
likelihood
,
ic_sampling
)
H
=
ift
.
Standard
Hamiltonian
(
likelihood
,
ic_sampling
)
initial_
positio
n
=
ift
.
MultiField
.
full
(
H
.
domain
,
0.
)
positio
n
=
initial_
positio
n
initial_
mea
n
=
ift
.
MultiField
.
full
(
H
.
domain
,
0.
)
mea
n
=
initial_
mea
n
plot
=
ift
.
Plot
()
plot
.
add
(
signal
(
mock_position
),
title
=
'Ground Truth'
)
...
...
@@ -117,9 +117,9 @@ if __name__ == '__main__':
# Draw new samples to approximate the KL five times
for
i
in
range
(
5
):
# Draw new samples and minimize KL
KL
=
ift
.
KL_Energy
(
positio
n
,
H
,
N_samples
)
KL
=
ift
.
MetricGaussianKL
(
mea
n
,
H
,
N_samples
)
KL
,
convergence
=
minimizer
(
KL
)
positio
n
=
KL
.
position
mea
n
=
KL
.
position
# Plot current reconstruction
plot
=
ift
.
Plot
()
...
...
@@ -128,7 +128,7 @@ if __name__ == '__main__':
plot
.
output
(
ny
=
1
,
ysize
=
6
,
xsize
=
16
,
name
=
"loop-{:02}.png"
.
format
(
i
))
# Draw posterior samples
KL
=
ift
.
KL_Energy
(
positio
n
,
H
,
N_samples
)
KL
=
ift
.
MetricGaussianKL
(
mea
n
,
H
,
N_samples
)
sc
=
ift
.
StatCalculator
()
for
sample
in
KL
.
samples
:
sc
.
add
(
signal
(
sample
+
KL
.
position
))
...
...
demos/polynomial_fit.py
View file @
03c31669
...
...
@@ -103,7 +103,7 @@ N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
IC
=
ift
.
DeltaEnergyController
(
tol_rel_deltaE
=
1e-12
,
iteration_limit
=
200
)
likelihood
=
ift
.
GaussianEnergy
(
d
,
N
)(
R
)
Ham
=
ift
.
Hamiltonian
(
likelihood
,
IC
)
Ham
=
ift
.
Standard
Hamiltonian
(
likelihood
,
IC
)
H
=
ift
.
EnergyAdapter
(
params
,
Ham
,
want_metric
=
True
)
# Minimize
...
...
nifty5/__init__.py
View file @
03c31669
...
...
@@ -49,7 +49,7 @@ from .operators.simple_linear_operators import (
FieldAdapter
,
ducktape
,
GeometryRemover
,
NullOperator
)
from
.operators.energy_operators
import
(
EnergyOperator
,
GaussianEnergy
,
PoissonianEnergy
,
InverseGammaLikelihood
,
BernoulliEnergy
,
Hamiltonian
,
SampledKullbachLeiblerDivergence
)
BernoulliEnergy
,
Standard
Hamiltonian
,
SampledKullbachLeiblerDivergence
)
from
.probing
import
probe_with_posterior_samples
,
probe_diagonal
,
\
StatCalculator
...
...
@@ -68,7 +68,7 @@ from .minimization.scipy_minimizer import (ScipyMinimizer, L_BFGS_B, ScipyCG)
from
.minimization.energy
import
Energy
from
.minimization.quadratic_energy
import
QuadraticEnergy
from
.minimization.energy_adapter
import
EnergyAdapter
from
.minimization.
kl_energy
import
KL_Energy
from
.minimization.
metric_gaussian_kl
import
MetricGaussianKL
from
.sugar
import
*
from
.plot
import
Plot
...
...
nifty5/library/adjust_variances.py
View file @
03c31669
...
...
@@ -19,7 +19,7 @@ from ..minimization.energy_adapter import EnergyAdapter
from
..multi_domain
import
MultiDomain
from
..multi_field
import
MultiField
from
..operators.distributors
import
PowerDistributor
from
..operators.energy_operators
import
Hamiltonian
,
InverseGammaLikelihood
from
..operators.energy_operators
import
Standard
Hamiltonian
,
InverseGammaLikelihood
from
..operators.scaling_operator
import
ScalingOperator
from
..operators.simple_linear_operators
import
ducktape
...
...
@@ -52,7 +52,7 @@ def make_adjust_variances(a,
Returns
-------
Hamiltonian
Standard
Hamiltonian
A Hamiltonian that can be used for further minimization
"""
...
...
@@ -71,7 +71,7 @@ def make_adjust_variances(a,
if
scaling
is
not
None
:
x
=
ScalingOperator
(
scaling
,
x
.
target
)(
x
)
return
Hamiltonian
(
InverseGammaLikelihood
(
d_eval
)(
x
),
ic_samp
=
ic_samp
)
return
Standard
Hamiltonian
(
InverseGammaLikelihood
(
d_eval
)(
x
),
ic_samp
=
ic_samp
)
def
do_adjust_variances
(
position
,
...
...
nifty5/minimization/
kl_energy
.py
→
nifty5/minimization/
metric_gaussian_kl
.py
View file @
03c31669
...
...
@@ -20,31 +20,68 @@ from ..linearization import Linearization
from
..
import
utilities
class
KL_Energy
(
Energy
):
def
__init__
(
self
,
position
,
h
,
nsamp
,
constants
=
[],
constants_samples
=
None
,
gen_mirrored_samples
=
False
,
class
MetricGaussianKL
(
Energy
):
"""Provides the sampled Kullback-Leibler divergence between a distribution and a metric Gaussian.
The Energy object is an implementation of a scalar function including its
gradient and metric at some position.
Parameters
----------
mean : Field
The current mean of the Gaussian.
hamiltonian : Hamiltonian
The Hamiltonian of the approximated probability distribution.
n_samples : integer
The number of samples used to stochastically estimate the KL.
constants : list
A list of parameter keys that are kept constant during optimization.
point_estimates : list
A list of parameter keys for which no samples are drawn, but that are
optimized for, corresponding to point estimates of these.
mirror_samples : boolean
Whether the negative of the drawn samples are also used,
as they are equaly legitimate samples. If true, the number of used
samples doubles. Mirroring samples stabilizes the KL estimate as
extreme sample variation is counterbalanced. (default : False)
Notes
-----
An instance of the Energy class is defined at a certain location. If one
is interested in the value, gradient or metric of the abstract energy
functional one has to 'jump' to the new position using the `at` method.
This method returns a new energy instance residing at the new position. By
this approach, intermediate results from computing e.g. the gradient can
safely be reused for e.g. the value or the metric.
Memorizing the evaluations of some quantities minimizes the computational
effort for multiple calls.
"""
def
__init__
(
self
,
mean
,
hamiltonian
,
n_sampels
,
constants
=
[],
point_estimates
=
None
,
mirror_samples
=
False
,
_samples
=
None
):
super
(
KL_Energy
,
self
).
__init__
(
positio
n
)
if
h
.
domain
is
not
positio
n
.
domain
:
super
(
MetricGaussianKL
,
self
).
__init__
(
mea
n
)
if
h
amiltonian
.
domain
is
not
mea
n
.
domain
:
raise
TypeError
self
.
_h
=
h
self
.
_h
amiltonian
=
hamiltonian
self
.
_constants
=
constants
if
constants_sampl
es
is
None
:
constants_sampl
es
=
constants
self
.
_constants_samples
=
constants_sampl
es
if
point_estimat
es
is
None
:
point_estimat
es
=
constants
self
.
_constants_samples
=
point_estimat
es
if
_samples
is
None
:
met
=
h
(
Linearization
.
make_partial_var
(
position
,
constants_sampl
es
,
True
)).
metric
met
=
h
amiltonian
(
Linearization
.
make_partial_var
(
mean
,
point_estimat
es
,
True
)).
metric
_samples
=
tuple
(
met
.
draw_sample
(
from_inverse
=
True
)
for
_
in
range
(
nsamp
))
if
gen_
mirror
ed
_samples
:
for
_
in
range
(
n
_
samp
els
))
if
mirror_samples
:
_samples
+=
tuple
(
-
s
for
s
in
_samples
)
self
.
_samples
=
_samples
self
.
_lin
=
Linearization
.
make_partial_var
(
positio
n
,
constants
)
self
.
_lin
=
Linearization
.
make_partial_var
(
mea
n
,
constants
)
v
,
g
=
None
,
None
for
s
in
self
.
_samples
:
tmp
=
self
.
_h
(
self
.
_lin
+
s
)
tmp
=
self
.
_h
amiltonian
(
self
.
_lin
+
s
)
if
v
is
None
:
v
=
tmp
.
val
.
local_data
[()]
g
=
tmp
.
gradient
...
...
@@ -56,9 +93,9 @@ class KL_Energy(Energy):
self
.
_metric
=
None
def
at
(
self
,
position
):
return
KL_Energy
(
position
,
self
.
_h
,
0
,
self
.
_constants
,
self
.
_constants_samples
,
_samples
=
self
.
_samples
)
return
MetricGaussianKL
(
position
,
self
.
_h
amiltonian
,
0
,
self
.
_constants
,
self
.
_constants_samples
,
_samples
=
self
.
_samples
)
@
property
def
value
(
self
):
...
...
@@ -71,7 +108,7 @@ class KL_Energy(Energy):
def
_get_metric
(
self
):
if
self
.
_metric
is
None
:
lin
=
self
.
_lin
.
with_want_metric
()
mymap
=
map
(
lambda
v
:
self
.
_h
(
lin
+
v
).
metric
,
self
.
_samples
)
mymap
=
map
(
lambda
v
:
self
.
_h
amiltonian
(
lin
+
v
).
metric
,
self
.
_samples
)
self
.
_metric
=
utilities
.
my_sum
(
mymap
)
self
.
_metric
=
self
.
_metric
.
scale
(
1.
/
len
(
self
.
_samples
))
...
...
nifty5/operators/energy_operators.py
View file @
03c31669
...
...
@@ -147,7 +147,7 @@ class BernoulliEnergy(EnergyOperator):
return
v
.
add_metric
(
met
)
class
Hamiltonian
(
EnergyOperator
):
class
Standard
Hamiltonian
(
EnergyOperator
):
def
__init__
(
self
,
lh
,
ic_samp
=
None
):
self
.
_lh
=
lh
self
.
_prior
=
GaussianEnergy
(
domain
=
lh
.
domain
)
...
...
test/test_energy_gradients.py
View file @
03c31669
...
...
@@ -69,7 +69,7 @@ def test_hamiltonian_and_KL(field):
field
=
field
.
exp
()
space
=
field
.
domain
lh
=
ift
.
GaussianEnergy
(
domain
=
space
)
hamiltonian
=
ift
.
Hamiltonian
(
lh
)
hamiltonian
=
ift
.
Standard
Hamiltonian
(
lh
)
ift
.
extra
.
check_value_gradient_consistency
(
hamiltonian
,
field
)
S
=
ift
.
ScalingOperator
(
1.
,
space
)
samps
=
[
S
.
draw_sample
()
for
i
in
range
(
3
)]
...
...
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