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ift
NIFTy
Commits
7f56278f
Commit
7f56278f
authored
Aug 18, 2018
by
Martin Reinecke
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implement a NIFTy-aware Newton-CG minimizer
parent
09f7ba45
Changes
8
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8 changed files
with
67 additions
and
33 deletions
+67
-33
demos/bernoulli_demo.py
demos/bernoulli_demo.py
+4
-5
demos/getting_started_2.py
demos/getting_started_2.py
+4
-5
demos/getting_started_3.py
demos/getting_started_3.py
+2
-6
demos/polynomial_fit.py
demos/polynomial_fit.py
+1
-1
nifty5/__init__.py
nifty5/__init__.py
+3
-3
nifty5/minimization/descent_minimizers.py
nifty5/minimization/descent_minimizers.py
+51
-0
nifty5/minimization/scipy_minimizer.py
nifty5/minimization/scipy_minimizer.py
+0
-11
test/test_minimization/test_minimizers.py
test/test_minimization/test_minimizers.py
+2
-2
No files found.
demos/bernoulli_demo.py
View file @
7f56278f
...
...
@@ -66,15 +66,14 @@ if __name__ == '__main__':
# Compute likelihood and Hamiltonian
position
=
ift
.
from_random
(
'normal'
,
harmonic_space
)
likelihood
=
ift
.
BernoulliEnergy
(
p
,
data
)
ic_cg
=
ift
.
GradientNormController
(
iteration_limit
=
50
)
ic_newton
=
ift
.
GradientNormController
(
name
=
'Newton'
,
iteration_limit
=
30
,
tol_abs_gradnorm
=
1e-3
)
minimizer
=
ift
.
RelaxedNewton
(
ic_newton
)
ic_newton
=
ift
.
DeltaEnergyController
(
name
=
'Newton'
,
iteration_limit
=
100
,
tol_rel_deltaE
=
1e-8
)
minimizer
=
ift
.
NewtonCG
(
ic_newton
)
ic_sampling
=
ift
.
GradientNormController
(
iteration_limit
=
100
)
# Minimize the Hamiltonian
H
=
ift
.
Hamiltonian
(
likelihood
,
ic_sampling
)
H
=
ift
.
EnergyAdapter
(
position
,
H
,
ic_cg
)
H
=
ift
.
EnergyAdapter
(
position
,
H
)
# minimizer = ift.L_BFGS(ic_newton)
H
,
convergence
=
minimizer
(
H
)
...
...
demos/getting_started_2.py
View file @
7f56278f
...
...
@@ -87,14 +87,13 @@ if __name__ == '__main__':
# Compute likelihood and Hamiltonian
likelihood
=
ift
.
PoissonianEnergy
(
lamb
,
data
)
ic_cg
=
ift
.
GradientNormController
(
iteration_limit
=
50
)
ic_newton
=
ift
.
GradientNormController
(
name
=
'Newton'
,
iteration_limit
=
50
,
tol_abs_gradnorm
=
1e-3
)
minimizer
=
ift
.
RelaxedNewton
(
ic_newton
)
ic_newton
=
ift
.
DeltaEnergyController
(
name
=
'Newton'
,
iteration_limit
=
100
,
tol_rel_deltaE
=
1e-8
)
minimizer
=
ift
.
NewtonCG
(
ic_newton
)
# Minimize the Hamiltonian
H
=
ift
.
Hamiltonian
(
likelihood
)
H
=
ift
.
EnergyAdapter
(
position
,
H
,
ic_cg
)
H
=
ift
.
EnergyAdapter
(
position
,
H
)
H
,
convergence
=
minimizer
(
H
)
# Plot results
...
...
demos/getting_started_3.py
View file @
7f56278f
...
...
@@ -72,14 +72,10 @@ if __name__ == '__main__':
mean
=
data
,
covariance
=
N
)(
signal_response
)
# set up minimization and inversion schemes
ic_cg
=
ift
.
GradientNormController
(
iteration_limit
=
10
)
ic_sampling
=
ift
.
GradientNormController
(
iteration_limit
=
100
)
ic_newton
=
ift
.
DeltaEnergyController
(
name
=
'Newton'
,
tol_rel_deltaE
=
1e-8
,
iteration_limit
=
100
)
minimizer
=
ift
.
RelaxedNewton
(
ic_newton
)
# minimizer = ift.VL_BFGS(ic_newton)
# minimizer = ift.NewtonCG(xtol=1e-10, maxiter=100, disp=True)
# minimizer = ift.L_BFGS_B(ftol=1e-10, gtol=1e-5, maxiter=100, maxcor=20, disp=True)
minimizer
=
ift
.
NewtonCG
(
ic_newton
)
# build model Hamiltonian
H
=
ift
.
Hamiltonian
(
likelihood
,
ic_sampling
)
...
...
@@ -100,7 +96,7 @@ if __name__ == '__main__':
for
_
in
range
(
N_samples
)]
KL
=
ift
.
SampledKullbachLeiblerDivergence
(
H
,
samples
)
KL
=
ift
.
EnergyAdapter
(
position
,
KL
,
ic_cg
,
constants
=
[
"xi"
]
)
KL
=
ift
.
EnergyAdapter
(
position
,
KL
)
KL
,
convergence
=
minimizer
(
KL
)
position
=
KL
.
position
...
...
demos/polynomial_fit.py
View file @
7f56278f
...
...
@@ -90,7 +90,7 @@ H = ift.Hamiltonian(likelihood, IC)
H
=
ift
.
EnergyAdapter
(
params
,
H
,
IC
)
# Minimize
minimizer
=
ift
.
RelaxedNewton
(
IC
)
minimizer
=
ift
.
NewtonCG
(
IC
)
H
,
_
=
minimizer
(
H
)
# Draw posterior samples
...
...
nifty5/__init__.py
View file @
7f56278f
...
...
@@ -60,9 +60,9 @@ from .minimization.minimizer import Minimizer
from
.minimization.conjugate_gradient
import
ConjugateGradient
from
.minimization.nonlinear_cg
import
NonlinearCG
from
.minimization.descent_minimizers
import
(
DescentMinimizer
,
SteepestDescent
,
VL_BFGS
,
L_BFGS
,
RelaxedNewton
)
from
.minimization.scipy_minimizer
import
(
ScipyMinimizer
,
NewtonCG
,
L_BFGS_B
,
ScipyCG
)
DescentMinimizer
,
SteepestDescent
,
VL_BFGS
,
L_BFGS
,
RelaxedNewton
,
NewtonCG
)
from
.minimization.scipy_minimizer
import
(
ScipyMinimizer
,
L_BFGS_B
,
ScipyCG
)
from
.minimization.energy
import
Energy
from
.minimization.quadratic_energy
import
QuadraticEnergy
from
.minimization.line_energy
import
LineEnergy
...
...
nifty5/minimization/descent_minimizers.py
View file @
7f56278f
...
...
@@ -153,6 +153,57 @@ class RelaxedNewton(DescentMinimizer):
return
-
energy
.
metric
.
inverse_times
(
energy
.
gradient
)
class
NewtonCG
(
DescentMinimizer
):
""" Calculates the descent direction according to a Newton-CG scheme.
Algorithm derived from SciPy sources.
"""
def
__init__
(
self
,
controller
,
line_searcher
=
None
):
if
line_searcher
is
None
:
line_searcher
=
LineSearchStrongWolfe
(
preferred_initial_step_size
=
1.
)
super
(
NewtonCG
,
self
).
__init__
(
controller
=
controller
,
line_searcher
=
line_searcher
)
def
get_descent_direction
(
self
,
energy
):
float64eps
=
np
.
finfo
(
np
.
float64
).
eps
grad
=
energy
.
gradient
maggrad
=
abs
(
grad
).
sum
()
eta
=
np
.
min
([
0.5
,
np
.
sqrt
(
maggrad
)])
termcond
=
eta
*
maggrad
xsupi
=
energy
.
position
*
0
ri
=
grad
psupi
=
-
ri
i
=
0
dri0
=
ri
.
vdot
(
ri
)
while
True
:
if
abs
(
ri
).
sum
()
<=
termcond
:
return
xsupi
Ap
=
energy
.
metric
(
psupi
)
# check curvature
curv
=
psupi
.
vdot
(
Ap
)
if
0
<=
curv
<=
3
*
float64eps
:
return
xsupi
elif
curv
<
0
:
if
(
i
>
0
):
return
xsupi
else
:
return
(
dri0
/
curv
)
*
grad
# fall back to steepest descent
alphai
=
dri0
/
curv
xsupi
=
xsupi
+
alphai
*
psupi
ri
=
ri
+
alphai
*
Ap
dri1
=
ri
.
vdot
(
ri
)
psupi
=
(
dri1
/
dri0
)
*
psupi
-
ri
i
+=
1
dri0
=
dri1
# update numpy.dot(ri,ri) for next time.
# curvature keeps increasing, bail out
raise
ValueError
(
"Warning: CG iterations didn't converge. "
"The Hessian is not positive definite."
)
class
L_BFGS
(
DescentMinimizer
):
def
__init__
(
self
,
controller
,
line_searcher
=
LineSearchStrongWolfe
(),
max_history_length
=
5
):
...
...
nifty5/minimization/scipy_minimizer.py
View file @
7f56278f
...
...
@@ -139,17 +139,6 @@ class ScipyMinimizer(Minimizer):
return
hlp
.
_energy
,
IterationController
.
CONVERGED
def
NewtonCG
(
xtol
,
maxiter
,
disp
=
False
):
"""Returns a ScipyMinimizer object carrying out the Newton-CG algorithm.
See Also
--------
ScipyMinimizer
"""
options
=
{
"xtol"
:
xtol
,
"maxiter"
:
maxiter
,
"disp"
:
disp
}
return
ScipyMinimizer
(
"Newton-CG"
,
options
,
True
,
None
)
def
L_BFGS_B
(
ftol
,
gtol
,
maxiter
,
maxcor
=
10
,
disp
=
False
,
bounds
=
None
):
"""Returns a ScipyMinimizer object carrying out the L-BFGS-B algorithm.
...
...
test/test_minimization/test_minimizers.py
View file @
7f56278f
...
...
@@ -34,9 +34,9 @@ minimizers = ['ift.VL_BFGS(IC)',
# 'ift.NonlinearCG(IC, "Hestenes-Stiefel"),
'ift.NonlinearCG(IC, "Fletcher-Reeves")'
,
'ift.NonlinearCG(IC, "5.49")'
,
'ift.NewtonCG(xtol=1e-5, maxiter=1000)'
,
'ift.L_BFGS_B(ftol=1e-10, gtol=1e-5, maxiter=1000)'
,
'ift.L_BFGS(IC)'
]
'ift.L_BFGS(IC)'
,
'ift.NewtonCG(IC)'
]
newton_minimizers
=
[
'ift.RelaxedNewton(IC)'
]
quadratic_only_minimizers
=
[
'ift.ConjugateGradient(IC)'
,
...
...
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