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ift
IMAGINE
Commits
c2e9087d
Commit
c2e9087d
authored
Dec 06, 2017
by
Theo Steininger
Browse files
Removed volume factors from EnsembleLikelihood
parent
dfb2cb45
Changes
1
Hide whitespace changes
Inline
Side-by-side
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
...
...
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