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ift
NIFTy
Commits
f701e9a9
Commit
f701e9a9
authored
Jan 08, 2019
by
Martin Reinecke
Browse files
start documenting
parent
dd23c622
Changes
4
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Inline
Side-by-side
nifty5/minimization/energy_adapter.py
View file @
f701e9a9
...
...
@@ -20,6 +20,25 @@ from ..minimization.energy import Energy
class
EnergyAdapter
(
Energy
):
"""Helper class which provides the traditional Nifty Energy interface to
Nifty operators with a scalar target domain.
Parameters
-----------
position: Field or MultiField living on the operator's input domain.
The position where the minimization process is started
op: Operator with a scalar target domain
The expression computing the energy from the input data
constants: list of strings (default: [])
The component names of the operator's input domain which are assumed
to be constant during the minimization process.
If the operator's input domain is not a MultiField, this must be empty.
want_metric: bool (default: False)
if True, the class will provide a `metric` property. This should only
be enabled if it is required, because it will most likely consume
additional resources.
"""
def
__init__
(
self
,
position
,
op
,
constants
=
[],
want_metric
=
False
):
super
(
EnergyAdapter
,
self
).
__init__
(
position
)
self
.
_op
=
op
...
...
nifty5/minimization/iteration_controllers.py
View file @
f701e9a9
...
...
@@ -143,7 +143,24 @@ class GradientNormController(IterationController):
class
GradInfNormController
(
IterationController
):
def
__init__
(
self
,
tol
=
None
,
convergence_level
=
1
,
iteration_limit
=
None
,
"""An iteration controller checking (mainly) the L_infinity gradient norm.
Parameters
----------
tol : float
If the L_infinity norm of the energy gradient is below this value, the
convergence counter will be increased in this iteration.
convergence_level : int, default=1
The number which the convergence counter must reach before the
iteration is considered to be converged
iteration_limit : int, optional
The maximum number of iterations that will be carried out.
name : str, optional
if supplied, this string and some diagnostic information will be
printed after every iteration
"""
def
__init__
(
self
,
tol
,
convergence_level
=
1
,
iteration_limit
=
None
,
name
=
None
):
self
.
_tol
=
tol
self
.
_convergence_level
=
convergence_level
...
...
@@ -185,6 +202,25 @@ class GradInfNormController(IterationController):
class
DeltaEnergyController
(
IterationController
):
"""An iteration controller checking (mainly) the energy change from one
iteration to the next.
Parameters
----------
tol_rel_deltaE : float
If the difference between the last and current energies divided by
the current energy is below this value, the convergence counter will
be increased in this iteration.
convergence_level : int, default=1
The number which the convergence counter must reach before the
iteration is considered to be converged
iteration_limit : int, optional
The maximum number of iterations that will be carried out.
name : str, optional
if supplied, this string and some diagnostic information will be
printed after every iteration
"""
def
__init__
(
self
,
tol_rel_deltaE
,
convergence_level
=
1
,
iteration_limit
=
None
,
name
=
None
):
self
.
_tol_rel_deltaE
=
tol_rel_deltaE
...
...
nifty5/operators/simple_linear_operators.py
View file @
f701e9a9
...
...
@@ -25,6 +25,13 @@ from .linear_operator import LinearOperator
class
VdotOperator
(
LinearOperator
):
"""Operator computing the scalar product of its input with a given Field.
Parameters
----------
field : Field/MultiField
The field used to build the scalar product with the operator input
"""
def
__init__
(
self
,
field
):
self
.
_field
=
field
self
.
_domain
=
field
.
domain
...
...
@@ -39,6 +46,7 @@ class VdotOperator(LinearOperator):
class
ConjugationOperator
(
EndomorphicOperator
):
"""Operator computing the complex conjugate of its input."""
def
__init__
(
self
,
domain
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_capability
=
self
.
_all_ops
...
...
@@ -49,6 +57,7 @@ class ConjugationOperator(EndomorphicOperator):
class
Realizer
(
EndomorphicOperator
):
"""Operator returning the real component of its input."""
def
__init__
(
self
,
domain
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_capability
=
self
.
TIMES
|
self
.
ADJOINT_TIMES
...
...
nifty5/probing.py
View file @
f701e9a9
...
...
@@ -19,10 +19,25 @@ from .field import Field
class
StatCalculator
(
object
):
"""Helper class to compute mean and variance of a set of inputs.
Notes
-----
- the memory usage of this object is constant, i.e. it does not increase
with the number of samples added
- FIXME describe the kind of variance used (divided by n-1)
"""
def
__init__
(
self
):
self
.
_count
=
0
def
add
(
self
,
value
):
"""Adds a sample.
Parameters
----------
value: any type that supports multiplication by a scalar and
element-wise addition/subtraction/multiplication.
"""
self
.
_count
+=
1
if
self
.
_count
==
1
:
self
.
_mean
=
1.
*
value
...
...
@@ -35,12 +50,18 @@ class StatCalculator(object):
@
property
def
mean
(
self
):
"""
value type : the mean of all samples added so far.
"""
if
self
.
_count
==
0
:
raise
RuntimeError
return
1.
*
self
.
_mean
@
property
def
var
(
self
):
"""
value type : the variance of all samples added so far.
"""
if
self
.
_count
<
2
:
raise
RuntimeError
return
self
.
_M2
*
(
1.
/
(
self
.
_count
-
1
))
...
...
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