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ift
NIFTy
Commits
59a009cb
Commit
59a009cb
authored
Jun 01, 2021
by
Philipp Arras
Browse files
Tweak docs
parent
0d3e909a
Pipeline
#102663
failed with stages
in 12 seconds
Changes
2
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
src/library/variational_models.py
View file @
59a009cb
...
...
@@ -33,14 +33,15 @@ from ..sugar import domain_union, from_random, full, makeField
class
MeanfieldModel
():
'''
Collects the operators required for Gaussian mean-field variational
inference.
"""Collect the operators required for Gaussian mean-field variational
inference.
Parameters
----------
domain: MultiDomain
The domain of the model parameters.
'''
The domain of the model parameters.
"""
def
__init__
(
self
,
domain
):
self
.
domain
=
MultiDomain
.
make
(
domain
)
self
.
Flat
=
Multifield2Vector
(
self
.
domain
)
...
...
@@ -52,17 +53,18 @@ class MeanfieldModel():
self
.
entropy
=
GaussianEntropy
(
self
.
std
.
target
)
@
self
.
std
def
get_initial_pos
(
self
,
initial_mean
=
None
,
initial_sig
=
1
):
'''
Provides an initial position for a given mean parameter vector and an
initial standard deviation.
"""Provide an initial position for a given mean parameter vector and an
initial standard deviation.
Parameters
----------
initial_mean: MultiField
The initial mean of the variational approximation. If not None, a Gaussian sample with mean zero and standard deviation of 0.1 is used.
Default: None
The initial mean of the variational approximation. If not None, a
Gaussian sample with mean zero and standard deviation of 0.1 is
used. Default: None
initial_sig: positive float
The initial standard deviation shared by all parameters. Default: 1
'''
The initial standard deviation shared by all parameters. Default: 1
"""
initial_pos
=
from_random
(
self
.
generator
.
domain
).
to_dict
()
initial_pos
[
'latent'
]
=
full
(
self
.
generator
.
domain
[
'latent'
],
0.
)
...
...
@@ -76,14 +78,15 @@ class MeanfieldModel():
class
FullCovarianceModel
():
'''
Collects the operators required for Gaussian full-covariance variational
inference.
"""Collect the operators required for Gaussian full-covariance variational
inference.
Parameters
----------
domain: MultiDomain
The domain of the model parameters.
'''
The domain of the model parameters.
"""
def
__init__
(
self
,
domain
):
self
.
domain
=
MultiDomain
.
make
(
domain
)
self
.
Flat
=
Multifield2Vector
(
self
.
domain
)
...
...
@@ -108,23 +111,24 @@ class FullCovarianceModel():
Resp
=
Respacer
(
MatMult
.
target
,
mean
.
target
)
self
.
generator
=
self
.
Flat
.
adjoint
@
(
mean
+
Resp
@
MatMult
@
matmul_setup
)
Diag
=
DiagonalSelector
(
cov
.
target
,
self
.
Flat
.
target
)
diag_cov
=
Diag
(
cov
).
absolute
()
self
.
entropy
=
GaussianEntropy
(
diag_cov
.
target
)
@
diag_cov
def
get_initial_pos
(
self
,
initial_mean
=
None
,
initial_sig
=
1
):
'''
Provides an initial position for a given mean parameter vector and a
diagonal covariance with an initial standard deviation.
"""Provide an initial position for a given mean parameter vector and a
diagonal covariance with an initial standard deviation.
Parameters
----------
initial_mean: MultiField
The initial mean of the variational approximation. If not None, a Gaussian sample with mean zero and standard deviation of 0.1 is used.
Default: None
The initial mean of the variational approximation. If not None, a
Gaussian sample with mean zero and standard deviation of 0.1 is
used. Default: None
initial_sig: positive float
The initial standard deviation shared by all parameters. Default: 1
'''
The initial standard deviation shared by all parameters. Default: 1
"""
initial_pos
=
from_random
(
self
.
generator
.
domain
).
to_dict
()
initial_pos
[
'latent'
]
=
full
(
self
.
generator
.
domain
[
'latent'
],
0.
)
diag_tri
=
np
.
diag
(
np
.
full
(
self
.
flat_domain
.
shape
[
0
],
initial_sig
))[
np
.
tril_indices
(
self
.
flat_domain
.
shape
[
0
])]
...
...
@@ -136,14 +140,15 @@ class FullCovarianceModel():
class
GaussianEntropy
(
EnergyOperator
):
'''
Calculates the entropy of a Gaussian distribution given the diagonal of a
triangular decomposition of the covariance.
"""Calculate the entropy of a Gaussian distribution given the diagonal of a
triangular decomposition of the covariance.
Parameters
----------
domain: Domain
The domain of the diagonal.
'''
The domain of the diagonal.
"""
def
__init__
(
self
,
domain
):
self
.
_domain
=
domain
...
...
@@ -159,16 +164,17 @@ class GaussianEntropy(EnergyOperator):
class
LowerTriangularProjector
(
LinearOperator
):
'''
Projects the DOFs of a triangular matrix into the matrix form.
"""Project the DOFs of a triangular matrix into the matrix form.
Parameters
----------
domain: Domain
A one-dimensional domain containing N(N+1)/2 DOFs of a triangular matrix.
A one-dimensional domain containing N(N+1)/2 DOFs of a triangular
matrix.
target: Domain
A two-dimensional domain with NxN entries.
'''
A two-dimensional domain with NxN entries.
"""
def
__init__
(
self
,
domain
,
target
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_target
=
DomainTuple
.
make
(
target
)
...
...
@@ -187,16 +193,17 @@ class LowerTriangularProjector(LinearOperator):
class
DiagonalSelector
(
LinearOperator
):
'''
Extracts the diagonal of a two-dimensional field.
"""Extract the diagonal of a two-dimensional field.
Parameters
----------
domain: Domain
The two-dimensional domain of the input field
The two-dimensional domain of the input field
target: Domain
A one-dimensional domain in which the diagonal of the input field lives.
'''
The one-dimensional domain on which the diagonal of the input field is
defined.
"""
def
__init__
(
self
,
domain
,
target
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_target
=
DomainTuple
.
make
(
target
)
...
...
@@ -211,16 +218,16 @@ class DiagonalSelector(LinearOperator):
class
Respacer
(
LinearOperator
):
'''
Re-maps a field from one domain to another one with the same amounts of
DOFs. Wrapps the numpy.reshape method.
"""Re-map a field from one domain to another one with the same amounts of
DOFs. Wrapps the numpy.reshape method.
Parameters
----------
domain: Domain
The domain of the input field.
The domain of the input field.
target: Domain
The domain of the output field.
'''
The domain of the output field.
"""
def
__init__
(
self
,
domain
,
target
):
self
.
_domain
=
DomainTuple
.
make
(
domain
)
...
...
src/minimization/stochastic_minimizer.py
View file @
59a009cb
...
...
@@ -19,26 +19,25 @@ from .minimizer import Minimizer
class
ADVIOptimizer
(
Minimizer
):
'''
Provides an implementation of an adaptive step-size sequence optimizer,
following https://arxiv.org/abs/1603.00788.
"""Provide an implementation of an adaptive step-size sequence optimizer,
following https://arxiv.org/abs/1603.00788.
Parameters
----------
steps: int
The number of concecutive steps during one call of the optimizer.
eta: positive float
The scale of the step-size sequence. It might have to be adapted to the application to increase performance. Default: 1.
The scale of the step-size sequence. It might have to be adapted to the
application to increase performance. Default: 1.
alpha: float between 0 and 1
The fraction of how much the current gradient impacts the momentum.
The fraction of how much the current gradient impacts the momentum.
tau: positive float
This quantity prevents division by zero.
epsilon: positive float
A small value guarantees Robbins and Monro conditions.
'''
"""
def
__init__
(
self
,
steps
,
eta
=
1
,
alpha
=
0.1
,
tau
=
1
,
epsilon
=
1e-16
):
self
.
alpha
=
alpha
self
.
eta
=
eta
self
.
tau
=
tau
...
...
@@ -59,15 +58,6 @@ class ADVIOptimizer(Minimizer):
return
new_position
def
__call__
(
self
,
E
):
'''
Performs the optimization.
Parameters
----------
E: EnergyOperator
The target function.
'''
from
..minimization.parametric_gaussian_kl
import
ParametricGaussianKL
if
self
.
s
is
None
:
self
.
s
=
E
.
gradient
**
2
...
...
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