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ift
NIFTy
Commits
c754769f
Commit
c754769f
authored
Jul 14, 2017
by
Martin Reinecke
Browse files
intermediate stage
parent
67950f71
Changes
3
Hide whitespace changes
Inline
Side-by-side
demos/critical_filtering.py
View file @
c754769f
...
...
@@ -115,10 +115,13 @@ if __name__ == "__main__":
iteration_limit
=
3
,
callback
=
convergence_measure
)
minimizer2
=
VL_BFGS
(
convergence_tolerance
=
0
,
iteration_limit
=
7
,
minimizer2
=
VL_BFGS
(
convergence_tolerance
=
1e-3
,
iteration_limit
=
7
0
,
callback
=
convergence_measure
,
max_history_length
=
3
)
max_history_length
=
10
)
minimizer3
=
SteepestDescent
(
convergence_tolerance
=
1e-3
,
iteration_limit
=
70
,
callback
=
convergence_measure
)
# Setting starting position
flat_power
=
Field
(
p_space
,
val
=
1e-8
)
...
...
@@ -140,7 +143,7 @@ if __name__ == "__main__":
# Initializing the power energy with updated parameters
power_energy
=
CriticalPowerEnergy
(
position
=
t0
,
m
=
m0
,
D
=
D0
,
smoothness_prior
=
10.
,
samples
=
3
)
(
power_energy
,
convergence
)
=
minimizer
1
(
power_energy
)
(
power_energy
,
convergence
)
=
minimizer
2
(
power_energy
)
# Setting new power spectrum
...
...
nifty/minimization/descent_minimizer.py
View file @
c754769f
...
...
@@ -121,18 +121,18 @@ class DescentMinimizer(Loggable, object):
"""
print
"into line search:"
print
" pos: "
,
energy
.
position
.
val
[
0
]
print
" ene: "
,
energy
.
value
#
print "into line search:"
#
print " pos: ",energy.position.val[0]
#
print " ene: ",energy.value
convergence
=
0
f_k_minus_1
=
None
step_length
=
0
iteration_number
=
1
while
True
:
print
"line search next iteration:"
print
" pos: "
,
energy
.
position
.
val
[
0
]
print
" ene: "
,
energy
.
value
#
print "line search next iteration:"
#
print " pos: ",energy.position.val[0]
#
print " ene: ",energy.value
if
self
.
callback
is
not
None
:
try
:
self
.
callback
(
energy
,
iteration_number
)
...
...
@@ -153,7 +153,7 @@ class DescentMinimizer(Loggable, object):
# current position is encoded in energy object
descent_direction
=
self
.
get_descent_direction
(
energy
)
print
"descent direction:"
,
descent_direction
.
val
[
0
]
#
print "descent direction:",descent_direction.val[0]
# compute the step length, which minimizes energy.value along the
# search direction
step_length
,
f_k
,
new_energy
=
\
...
...
@@ -161,17 +161,17 @@ class DescentMinimizer(Loggable, object):
energy
=
energy
,
pk
=
descent_direction
,
f_k_minus_1
=
f_k_minus_1
)
print
"out of wolfe:"
print
" old pos: "
,
energy
.
position
.
val
[
0
]
print
" old ene: "
,
energy
.
value
print
" new pos: "
,
new_energy
.
position
.
val
[
0
]
print
" new ene: "
,
new_energy
.
value
print
" f_k: "
,
f_k
#
print "out of wolfe:"
#
print " old pos: ",energy.position.val[0]
#
print " old ene: ",energy.value
#
print " new pos: ",new_energy.position.val[0]
#
print " new ene: ",new_energy.value
#
print " f_k: ",f_k
f_k_minus_1
=
energy
.
value
print
" step length: "
,
step_length
#
print " step length: ", step_length
tx1
=
energy
.
position
-
new_energy
.
position
print
" step length 2: "
,
(
energy
.
position
-
new_energy
.
position
).
norm
()
print
" step length 3: "
,
new_energy
.
position
.
val
[
0
]
-
energy
.
position
.
val
[
0
]
#
print " step length 2: ", (energy.position-new_energy.position).norm()
#
print " step length 3: ", new_energy.position.val[0]-energy.position.val[0]
# check if new energy value is bigger than old energy value
if
(
new_energy
.
value
-
energy
.
value
)
>
0
:
print
"Line search algorithm returned a new energy that was larger than the old one. Stopping."
...
...
nifty/minimization/line_searching/line_search_strong_wolfe.py
View file @
c754769f
...
...
@@ -61,6 +61,9 @@ class LineSearchStrongWolfe(LineSearch):
"""
# def __init__(self, c1=1e-4, c2=0.9,
# max_step_size=1000000000, max_iterations=100,
# max_zoom_iterations=100):
def
__init__
(
self
,
c1
=
1e-4
,
c2
=
0.9
,
max_step_size
=
50
,
max_iterations
=
10
,
max_zoom_iterations
=
10
):
...
...
@@ -111,6 +114,7 @@ class LineSearchStrongWolfe(LineSearch):
le_0
=
self
.
line_energy
.
at
(
0
)
phi_0
=
le_0
.
value
phiprime_0
=
le_0
.
dd
assert
phiprime_0
<
0
,
"input direction must be a descent direction"
if
phiprime_0
==
0
:
self
.
logger
.
warn
(
"Flat gradient in search direction."
)
...
...
@@ -133,12 +137,12 @@ class LineSearchStrongWolfe(LineSearch):
# start the minimization loop
for
i
in
xrange
(
max_iterations
):
print
"a0a1:"
,
alpha0
,
alpha1
print
"line search outer iteration"
,
i
#
print "a0a1:",alpha0, alpha1
#
print "line search outer iteration", i
le_alpha1
=
self
.
line_energy
.
at
(
alpha1
)
print
"position:"
,
le_alpha1
.
energy
.
position
.
val
[
0
]
#
print "position:", le_alpha1.energy.position.val[0]
phi_alpha1
=
le_alpha1
.
value
print
"energy:"
,
le_alpha1
.
value
#
print "energy:", le_alpha1.value
if
alpha1
==
0
:
self
.
logger
.
warn
(
"Increment size became 0."
)
alpha_star
=
0.
...
...
@@ -175,6 +179,7 @@ class LineSearchStrongWolfe(LineSearch):
# update alphas
alpha0
,
alpha1
=
alpha1
,
min
(
2
*
alpha1
,
max_step_size
)
if
alpha1
==
max_step_size
:
print
"bailout"
alpha_star
=
alpha1
phi_star
=
phi_alpha1
le_star
=
le_alpha1
...
...
@@ -235,10 +240,10 @@ class LineSearchStrongWolfe(LineSearch):
The new Energy object on the new position.
"""
print
"entering zoom"
print
alpha_lo
,
alpha_hi
print
"pos1:"
,
self
.
line_energy
.
at
(
alpha_lo
).
energy
.
position
.
val
[
0
]
print
"pos2:"
,
self
.
line_energy
.
at
(
alpha_hi
).
energy
.
position
.
val
[
0
]
#
print "entering zoom"
#
print alpha_lo, alpha_hi
#
print "pos1:",self.line_energy.at(alpha_lo).energy.position.val[0]
#
print "pos2:",self.line_energy.at(alpha_hi).energy.position.val[0]
max_iterations
=
self
.
max_zoom_iterations
# define the cubic and quadratic interpolant checks
cubic_delta
=
0.2
# cubic
...
...
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