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ift
NIFTy
Commits
27f4f66b
Commit
27f4f66b
authored
May 13, 2020
by
Philipp Arras
Browse files
Proper dtype handling
parent
d83a042e
Pipeline
#74867
passed with stages
in 21 minutes and 8 seconds
Changes
2
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
nifty6/multi_field.py
View file @
27f4f66b
...
...
@@ -103,7 +103,10 @@ class MultiField(Operator):
@
staticmethod
def
from_random
(
random_type
,
domain
,
dtype
=
np
.
float64
,
**
kwargs
):
domain
=
MultiDomain
.
make
(
domain
)
if
dtype
in
[
np
.
float64
,
np
.
complex128
]:
if
isinstance
(
dtype
,
dict
):
dtype
=
{
kk
:
np
.
dtype
(
dt
)
for
kk
,
dt
in
dtype
.
items
()}
else
:
dtype
=
np
.
dtype
(
dtype
)
dtype
=
{
kk
:
dtype
for
kk
in
domain
.
keys
()}
dct
=
{
kk
:
Field
.
from_random
(
random_type
,
domain
[
kk
],
dtype
[
kk
],
**
kwargs
)
for
kk
in
domain
.
keys
()}
...
...
nifty6/operators/energy_operators.py
View file @
27f4f66b
...
...
@@ -34,18 +34,18 @@ def _check_sampling_dtype(domain, dtypes):
if
dtypes
is
None
:
return
if
isinstance
(
domain
,
DomainTuple
):
dtypes
=
{
''
:
dtypes
}
np
.
dtype
(
dtypes
)
return
elif
isinstance
(
domain
,
MultiDomain
):
if
dtypes
in
[
np
.
float64
,
np
.
complex128
]:
if
isinstance
(
dtypes
,
dict
):
for
dt
in
dtypes
.
values
():
np
.
dtype
(
dt
)
if
set
(
domain
.
keys
())
==
set
(
dtypes
.
keys
()):
return
else
:
np
.
dtype
(
dtypes
)
return
dtypes
=
dtypes
.
values
()
if
set
(
domain
.
keys
())
!=
set
(
dtypes
.
keys
()):
raise
ValueError
else
:
raise
TypeError
for
dt
in
dtypes
.
values
():
if
dt
not
in
[
np
.
float64
,
np
.
complex128
]:
raise
ValueError
raise
TypeError
def
_field_to_dtype
(
field
):
...
...
@@ -332,7 +332,8 @@ class StudentTEnergy(EnergyOperator):
E_
\\
theta(f) = -
\\
log
\\
text{StudentT}_
\\
theta(f)
=
\\
frac{
\\
theta + 1}{2}
\\
log(1 +
\\
frac{f^2}{
\\
theta}),
where f is a field defined on `domain`.
where f is a field defined on `domain`. Assumes that the data is `float64`
for sampling.
Parameters
----------
...
...
@@ -342,12 +343,9 @@ class StudentTEnergy(EnergyOperator):
Degree of freedom parameter for the student t distribution
"""
def
__init__
(
self
,
domain
,
theta
,
sampling_dtype
=
np
.
float64
):
def
__init__
(
self
,
domain
,
theta
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_theta
=
theta
self
.
_sampling_dtype
=
sampling_dtype
if
sampling_dtype
==
np
.
complex128
:
raise
NotImplementedError
(
'Complex data not supported yet'
)
def
apply
(
self
,
x
):
self
.
_check_input
(
x
)
...
...
@@ -355,9 +353,7 @@ class StudentTEnergy(EnergyOperator):
if
not
x
.
want_metric
:
return
res
met
=
makeOp
((
self
.
_theta
+
1
)
/
(
self
.
_theta
+
3
),
self
.
domain
)
if
self
.
_sampling_dtype
is
not
None
:
met
=
SamplingDtypeSetter
(
met
,
self
.
_sampling_dtype
)
return
res
.
add_metric
(
met
)
return
res
.
add_metric
(
SamplingDtypeSetter
(
met
,
np
.
float64
))
class
BernoulliEnergy
(
EnergyOperator
):
...
...
@@ -420,7 +416,8 @@ class StandardHamiltonian(EnergyOperator):
ic_samp : IterationController
Tells an internal :class:`SamplingEnabler` which convergence criterion
to use to draw Gaussian samples.
sampling_dtype : FIXME
prior_dtype : numpy.dtype or dict of numpy.dtype, optional
Data type of prior used for sampling.
See also
--------
...
...
@@ -429,9 +426,9 @@ class StandardHamiltonian(EnergyOperator):
`<https://arxiv.org/abs/1812.04403>`_
"""
def
__init__
(
self
,
lh
,
ic_samp
=
None
,
_c_inp
=
None
,
sampling
_dtype
=
np
.
float64
):
def
__init__
(
self
,
lh
,
ic_samp
=
None
,
_c_inp
=
None
,
prior
_dtype
=
np
.
float64
):
self
.
_lh
=
lh
self
.
_prior
=
GaussianEnergy
(
domain
=
lh
.
domain
,
sampling_dtype
=
sampling
_dtype
)
self
.
_prior
=
GaussianEnergy
(
domain
=
lh
.
domain
,
sampling_dtype
=
prior
_dtype
)
if
_c_inp
is
not
None
:
_
,
self
.
_prior
=
self
.
_prior
.
simplify_for_constant_input
(
_c_inp
)
self
.
_ic_samp
=
ic_samp
...
...
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