Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
I
IMAGINE
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
3
Issues
3
List
Boards
Labels
Service Desk
Milestones
Merge Requests
1
Merge Requests
1
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Operations
Operations
Incidents
Environments
Analytics
Analytics
CI / CD
Repository
Value Stream
Wiki
Wiki
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
ift
IMAGINE
Commits
c2e9087d
Commit
c2e9087d
authored
Dec 06, 2017
by
Theo Steininger
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Removed volume factors from EnsembleLikelihood
parent
dfb2cb45
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
20 additions
and
44 deletions
+20
-44
imagine/likelihoods/ensemble_likelihood/ensemble_likelihood.py
...ne/likelihoods/ensemble_likelihood/ensemble_likelihood.py
+20
-44
No files found.
imagine/likelihoods/ensemble_likelihood/ensemble_likelihood.py
View file @
c2e9087d
...
...
@@ -5,7 +5,6 @@ import numpy as np
from
nifty
import
DiagonalOperator
,
FieldArray
,
Field
from
imagine.likelihoods.likelihood
import
Likelihood
from
imagine.create_ring_profile
import
create_ring_profile
class
EnsembleLikelihood
(
Likelihood
):
...
...
@@ -14,12 +13,6 @@ class EnsembleLikelihood(Likelihood):
self
.
observable_name
=
observable_name
self
.
measured_data
=
self
.
_strip_data
(
measured_data
)
self
.
data_covariance_operator
=
data_covariance_operator
self
.
data_covariance_includes_profile
=
False
if
profile
is
None
:
profile
=
create_ring_profile
(
self
.
measured_data
.
val
.
get_full_data
())
self
.
profile
=
profile
def
_strip_data
(
self
,
data
):
# if the first element in the domain tuple is a FieldArray we must
...
...
@@ -34,41 +27,30 @@ class EnsembleLikelihood(Likelihood):
field
=
observable
[
self
.
observable_name
]
return
self
.
_process_simple_field
(
field
,
self
.
measured_data
,
self
.
data_covariance_operator
,
self
.
profile
)
self
.
data_covariance_operator
)
def
_process_simple_field
(
self
,
observable
,
measured_data
,
data_covariance_operator
,
profile
):
data_covariance_operator
):
# https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula#Generalization
# B = A^{-1} + U U^dagger
# A = data_covariance
# B^{-1} c = (A_inv -
# A_inv U (I_k + U^dagger A_inv U)^{-1} U^dagger A_inv) c
weight
=
observable
.
domain
[
1
].
weight
(
1
)
k
=
observable
.
shape
[
0
]
n
=
observable
.
shape
[
1
]
obs_val
=
observable
.
val
.
get_full_data
()
obs_mean
=
observable
.
ensemble_mean
().
val
.
get_full_data
()
# divide out profile
obs_val
/=
profile
obs_mean
/=
profile
measured_data
=
measured_data
/
profile
u_val
=
obs_val
-
obs_mean
# compute quantities for OAS estimator
mu
=
np
.
vdot
(
u_val
,
u_val
)
*
weight
/
k
/
n
mu
=
np
.
vdot
(
u_val
,
u_val
)
/
k
/
n
self
.
logger
.
debug
(
"mu: %f"
%
mu
)
alpha
=
(
np
.
einsum
(
u_val
,
[
0
,
1
],
u_val
,
[
2
,
1
])
**
2
).
sum
()
alpha
/=
k
*
2
# correct the volume factor: one factor comes from the internal scalar
# product and one from the trace
alpha
*=
weight
**
2
numerator
=
(
1
-
2.
/
n
)
*
alpha
+
(
mu
*
n
)
**
2
denominator
=
(
k
+
1
-
2.
/
n
)
*
(
alpha
-
((
mu
*
n
)
**
2
)
/
n
)
...
...
@@ -82,38 +64,31 @@ class EnsembleLikelihood(Likelihood):
# rescale U half/half
u_val
*=
np
.
sqrt
(
1
-
rho
)
/
np
.
sqrt
(
k
)
U
=
observable
.
copy_empty
()
U
.
val
=
u_val
# we assume that data_covariance_operator is a DiagonalOperator
if
not
isinstance
(
data_covariance_operator
,
DiagonalOperator
):
raise
TypeError
(
"data_covariance_operator must be a NIFTY "
"DiagonalOperator."
)
A_bare_diagonal
=
data_covariance_operator
.
diagonal
(
bare
=
True
)
if
not
self
.
data_covariance_includes_profile
:
A_bare_diagonal
*=
(
profile
**
2
)
A_bare_diagonal
.
val
+=
rho
*
mu
A
=
DiagonalOperator
(
domain
=
data_covariance_operator
.
domain
,
diagonal
=
A_bare_diagonal
,
bare
=
True
,
copy
=
False
,
default_spaces
=
data_covariance_operator
.
default_spaces
)
A_diagonal_val
=
data_covariance_operator
.
diagonal
(
bare
=
False
).
val
self
.
logger
.
info
((
'rho*mu'
,
rho
*
mu
,
'rho'
,
rho
,
'mu'
,
mu
,
'alhpa'
,
alpha
))
A_diagonal_val
+=
rho
*
mu
a_u
=
A
.
inverse_times
(
U
,
spaces
=
1
)
a_u
_val
=
u_val
/
A_diagonal_val
# build middle-matrix (kxk)
a_u_val
=
a_u
.
val
.
get_full_data
()
middle
=
(
np
.
eye
(
k
)
+
np
.
einsum
(
u_val
.
conjugate
(),
[
0
,
1
],
a_u_val
,
[
2
,
1
])
*
weight
)
a_u_val
,
[
2
,
1
]))
middle
=
np
.
linalg
.
inv
(
middle
)
c
=
measured_data
-
obs_mean
# If the data was incomplete, i.e. contains np.NANs, set those values
# to zero.
c
.
val
.
data
=
np
.
nan_to_num
(
c
.
val
.
data
)
# assuming that A == A^dagger, this can be shortend
# a_c = A.inverse_times(c)
# u_a_c = a_c.dot(U, spaces=1)
...
...
@@ -125,20 +100,21 @@ class EnsembleLikelihood(Likelihood):
# Pure NIFTy is
# u_a_c = c.dot(a_u, spaces=1)
# u_a_c_val = u_a_c.val.get_full_data()
c_
weighted_val
=
c
.
weight
()
.
val
.
get_full_data
()
u_a_c_val
=
np
.
einsum
(
c_
weighted_
val
,
[
1
],
a_u_val
,
[
0
,
1
])
c_
val
=
c
.
val
.
get_full_data
()
u_a_c_val
=
np
.
einsum
(
c_val
,
[
1
],
a_u_val
,
[
0
,
1
])
first_summand
=
A
.
inverse_times
(
c
)
first_summand
_val
=
c_val
/
A_diagonal_val
second_summand_val
=
np
.
einsum
(
middle
,
[
0
,
1
],
u_a_c_val
,
[
1
])
second_summand_val
=
np
.
einsum
(
a_u_val
,
[
0
,
1
],
second_summand_val
,
[
0
])
# second_summand_val *= -1
second_summand
=
first_summand
.
copy_empty
()
second_summand
.
val
=
second_summand_val
#
# second_summand_val *= -1
#
second_summand = first_summand.copy_empty()
#
second_summand.val = second_summand_val
result_1
=
c
.
vdot
(
first_summand
)
result_2
=
-
c
.
vdot
(
second_summand
)
result_1
=
np
.
vdot
(
c_val
,
first_summand_val
)
result_2
=
-
np
.
vdot
(
c_val
,
second_summand_val
)
result
=
-
(
result_1
+
result_2
)
print
(
result_1
,
result_2
,
result
)
self
.
logger
.
info
(
"Calculated (%s): -(%f + %f) = %f"
%
(
self
.
observable_name
,
result_1
,
result_2
,
result
))
# result_array[i] = result
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment