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ift
IMAGINE
Commits
caf867fb
Commit
caf867fb
authored
Mar 22, 2017
by
Theo Steininger
Browse files
Modified EnsembleLikelihood in order to compare to the ensemble mean.
parent
b891254c
Changes
1
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Inline
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imagine/likelihoods/ensemble_likelihood/ensemble_likelihood.py
View file @
caf867fb
...
...
@@ -55,42 +55,43 @@ class EnsembleLikelihood(Likelihood):
np
.
einsum
(
u_val
.
conjugate
(),
[
0
,
1
],
a_u_val
,
[
2
,
1
]))
middle
=
np
.
linalg
.
inv
(
middle
)
result_array
=
np
.
zeros
(
k
)
for
i
in
xrange
(
k
):
c
=
measured_data
-
obs_val
[
i
]
# assuming that A == A^dagger, this can be shortend
# a_c = A.inverse_times(c)
# u_a_c = a_c.dot(U, spaces=1)
# u_a_c = u_a_c.conjugate()
# and: double conjugate shouldn't make a difference
# u_a_c = c.conjugate().dot(a_u, spaces=1).conjugate()
# 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
])
first_summand
=
A
.
inverse_times
(
c
)
self
.
logger
.
debug
(
"Calculated first summand."
)
second_summand_val
=
np
.
einsum
(
middle
,
[
0
,
1
],
u_a_c_val
,
[
1
])
self
.
logger
.
debug
(
"Intermediate step."
)
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
result_1
=
-
c
.
dot
(
first_summand
)
result_2
=
-
c
.
dot
(
second_summand
)
result
=
result_1
+
result_2
self
.
logger
.
debug
(
"Calculated %i of %i: %f + %f = %f"
%
(
i
,
k
,
result_1
,
result_2
,
result
))
result_array
[
i
]
=
result
total_result
=
result_array
.
mean
()
# result_array = np.zeros(k)
# for i in xrange(k):
# c = measured_data - obs_val[i]
c
=
measured_data
-
obs_mean
# assuming that A == A^dagger, this can be shortend
# a_c = A.inverse_times(c)
# u_a_c = a_c.dot(U, spaces=1)
# u_a_c = u_a_c.conjugate()
# and: double conjugate shouldn't make a difference
# u_a_c = c.conjugate().dot(a_u, spaces=1).conjugate()
# 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
])
first_summand
=
A
.
inverse_times
(
c
)
self
.
logger
.
debug
(
"Calculated first summand."
)
second_summand_val
=
np
.
einsum
(
middle
,
[
0
,
1
],
u_a_c_val
,
[
1
])
self
.
logger
.
debug
(
"Intermediate step."
)
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
result_1
=
-
c
.
dot
(
first_summand
)
result_2
=
-
c
.
dot
(
second_summand
)
result
=
result_1
+
result_2
self
.
logger
.
debug
(
"Calculated %i of %i: %f + %f = %f"
%
(
i
,
k
,
result_1
,
result_2
,
result
))
# result_array[i] = result
# total_result = result_array.mean()
total_result
=
result
normalization
=
measured_data
.
dot
(
measured_data
)
normalized_total_result
=
total_result
/
normalization
self
.
logger
.
info
(
"Applied normalization for total result: "
...
...
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