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ift
NIFTy
Commits
e25f7013
Commit
e25f7013
authored
Jul 08, 2017
by
Theo Steininger
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Merge branch 'minimization_tests' into 'master'
minimization tests See merge request
!143
parents
b0de760d
8fcb0150
Pipeline
#14514
passed with stages
in 12 minutes and 53 seconds
Changes
6
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1
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6 changed files
with
139 additions
and
13 deletions
+139
-13
nifty/minimization/descent_minimizer.py
nifty/minimization/descent_minimizer.py
+11
-4
nifty/minimization/steepest_descent.py
nifty/minimization/steepest_descent.py
+1
-5
nifty/minimization/vl_bfgs.py
nifty/minimization/vl_bfgs.py
+1
-4
test/test_minimization/quadratic_potential.py
test/test_minimization/quadratic_potential.py
+28
-0
test/test_minimization/test_conjugate_gradient.py
test/test_minimization/test_conjugate_gradient.py
+47
-0
test/test_minimization/test_descent_minimizers.py
test/test_minimization/test_descent_minimizers.py
+51
-0
No files found.
nifty/minimization/descent_minimizer.py
View file @
e25f7013
...
...
@@ -156,13 +156,20 @@ class DescentMinimizer(Loggable, object):
pk
=
descend_direction
,
f_k_minus_1
=
f_k_minus_1
)
f_k_minus_1
=
energy
.
value
energy
=
new_energy
# check if new energy value is bigger than old energy value
if
(
new_energy
.
value
-
energy
.
value
)
>
0
:
self
.
logger
.
info
(
"Line search algorithm returned a new energy "
"that was larger than the old one. Stopping."
)
break
energy
=
new_energy
# check convergence
delta
=
abs
(
gradient
).
max
()
*
(
step_length
/
gradient_norm
)
self
.
logger
.
debug
(
"Iteration : %08u step_length = %3.1E "
"delta = %3.1E"
%
(
iteration_number
,
step_length
,
delta
))
self
.
logger
.
debug
(
"Iteration:%08u step_length=%3.1E "
"delta=%3.1E energy=%3.1E"
%
(
iteration_number
,
step_length
,
delta
,
energy
.
value
))
if
delta
==
0
:
convergence
=
self
.
convergence_level
+
2
self
.
logger
.
info
(
"Found minimum according to line-search. "
...
...
nifty/minimization/steepest_descent.py
View file @
e25f7013
...
...
@@ -40,8 +40,4 @@ class SteepestDescent(DescentMinimizer):
"""
descend_direction
=
energy
.
gradient
norm
=
descend_direction
.
norm
()
if
norm
!=
1
:
return
descend_direction
/
-
norm
else
:
return
descend_direction
*
-
1
return
descend_direction
*
-
1
nifty/minimization/vl_bfgs.py
View file @
e25f7013
...
...
@@ -25,7 +25,7 @@ from .line_searching import LineSearchStrongWolfe
class
VL_BFGS
(
DescentMinimizer
):
def
__init__
(
self
,
line_searcher
=
LineSearchStrongWolfe
(),
callback
=
None
,
convergence_tolerance
=
1E-4
,
convergence_level
=
3
,
iteration_limit
=
None
,
max_history_length
=
10
):
iteration_limit
=
None
,
max_history_length
=
5
):
super
(
VL_BFGS
,
self
).
__init__
(
line_searcher
=
line_searcher
,
...
...
@@ -84,9 +84,6 @@ class VL_BFGS(DescentMinimizer):
for
i
in
xrange
(
1
,
len
(
delta
)):
descend_direction
+=
delta
[
i
]
*
b
[
i
]
norm
=
descend_direction
.
norm
()
if
norm
!=
1
:
descend_direction
/=
norm
return
descend_direction
...
...
test/test_minimization/quadratic_potential.py
0 → 100644
View file @
e25f7013
# -*- coding: utf-8 -*-
from
nifty
import
Energy
class
QuadraticPotential
(
Energy
):
def
__init__
(
self
,
position
,
eigenvalues
):
super
(
QuadraticPotential
,
self
).
__init__
(
position
)
self
.
eigenvalues
=
eigenvalues
def
at
(
self
,
position
):
return
self
.
__class__
(
position
,
eigenvalues
=
self
.
eigenvalues
)
@
property
def
value
(
self
):
H
=
0.5
*
self
.
position
.
vdot
(
self
.
eigenvalues
(
self
.
position
))
return
H
.
real
@
property
def
gradient
(
self
):
g
=
self
.
eigenvalues
(
self
.
position
)
return
g
@
property
def
curvature
(
self
):
return
self
.
eigenvalues
test/test_minimization/test_conjugate_gradient.py
0 → 100644
View file @
e25f7013
import
unittest
import
numpy
as
np
from
numpy.testing
import
assert_equal
,
assert_almost_equal
from
nifty
import
Field
,
DiagonalOperator
,
RGSpace
,
HPSpace
from
nifty
import
ConjugateGradient
from
test.common
import
expand
spaces
=
[
RGSpace
([
1024
,
1024
],
distances
=
0.123
),
HPSpace
(
32
)]
class
Test_ConjugateGradient
(
unittest
.
TestCase
):
def
test_interface
(
self
):
iteration_limit
=
100
convergence_level
=
4
convergence_tolerance
=
1E-6
callback
=
lambda
z
:
z
minimizer
=
ConjugateGradient
(
iteration_limit
=
iteration_limit
,
convergence_tolerance
=
convergence_tolerance
,
convergence_level
=
convergence_level
,
callback
=
callback
)
assert_equal
(
minimizer
.
iteration_limit
,
iteration_limit
)
assert_equal
(
minimizer
.
convergence_level
,
convergence_level
)
assert_equal
(
minimizer
.
convergence_tolerance
,
convergence_tolerance
)
assert
(
minimizer
.
callback
is
callback
)
@
expand
([[
space
]
for
space
in
spaces
])
def
test_minimization
(
self
,
space
):
np
.
random
.
seed
(
42
)
starting_point
=
Field
.
from_random
(
'normal'
,
domain
=
space
)
*
10
covariance_diagonal
=
Field
.
from_random
(
'uniform'
,
domain
=
space
)
+
0.5
covariance
=
DiagonalOperator
(
space
,
diagonal
=
covariance_diagonal
)
required_result
=
Field
(
space
,
val
=
1.
)
minimizer
=
ConjugateGradient
()
(
position
,
convergence
)
=
minimizer
(
A
=
covariance
,
x0
=
starting_point
,
b
=
required_result
)
assert_almost_equal
(
position
.
val
.
get_full_data
(),
1.
/
covariance_diagonal
.
val
.
get_full_data
(),
decimal
=
3
)
test/test_minimization/test_descent_minimizers.py
0 → 100644
View file @
e25f7013
import
unittest
import
numpy
as
np
from
numpy.testing
import
assert_equal
,
assert_almost_equal
from
nifty
import
Field
,
DiagonalOperator
,
RGSpace
,
HPSpace
from
nifty
import
SteepestDescent
,
RelaxedNewton
,
VL_BFGS
from
itertools
import
product
from
test.common
import
expand
from
quadratic_potential
import
QuadraticPotential
from
nifty
import
logger
minimizers
=
[
SteepestDescent
,
RelaxedNewton
,
VL_BFGS
]
spaces
=
[
RGSpace
([
1024
,
1024
],
distances
=
0.123
),
HPSpace
(
32
)]
class
Test_DescentMinimizers
(
unittest
.
TestCase
):
@
expand
([[
minimizer
]
for
minimizer
in
minimizers
])
def
test_interface
(
self
,
minimizer
):
iteration_limit
=
100
convergence_level
=
4
convergence_tolerance
=
1E-6
callback
=
lambda
z
:
z
minimizer
=
minimizer
(
iteration_limit
=
iteration_limit
,
convergence_tolerance
=
convergence_tolerance
,
convergence_level
=
convergence_level
,
callback
=
callback
)
assert_equal
(
minimizer
.
iteration_limit
,
iteration_limit
)
assert_equal
(
minimizer
.
convergence_level
,
convergence_level
)
assert_equal
(
minimizer
.
convergence_tolerance
,
convergence_tolerance
)
assert
(
minimizer
.
callback
is
callback
)
@
expand
(
product
(
minimizers
,
spaces
))
def
test_minimization
(
self
,
minimizer_class
,
space
):
np
.
random
.
seed
(
42
)
starting_point
=
Field
.
from_random
(
'normal'
,
domain
=
space
)
*
10
covariance_diagonal
=
Field
.
from_random
(
'uniform'
,
domain
=
space
)
+
0.5
covariance
=
DiagonalOperator
(
space
,
diagonal
=
covariance_diagonal
)
energy
=
QuadraticPotential
(
position
=
starting_point
,
eigenvalues
=
covariance
)
minimizer
=
minimizer_class
(
iteration_limit
=
30
)
(
energy
,
convergence
)
=
minimizer
(
energy
)
assert_almost_equal
(
energy
.
value
,
0
,
decimal
=
5
)
assert_almost_equal
(
energy
.
position
.
val
.
get_full_data
(),
0.
,
decimal
=
5
)
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