From d7b805473621f65f50d731fa56acff63c99c4e84 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Fri, 27 Jul 2018 20:47:00 +0200
Subject: [PATCH] move EnergyAdapter
---
demos/getting_started_2.py | 25 +------------------------
demos/getting_started_3b.py | 24 +-----------------------
nifty5/__init__.py | 1 +
nifty5/energies/energy_adapter.py | 28 ++++++++++++++++++++++++++++
4 files changed, 31 insertions(+), 47 deletions(-)
create mode 100644 nifty5/energies/energy_adapter.py
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index f2267bcff..298aff157 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -20,29 +20,6 @@ import nifty5 as ift
import numpy as np
-class EnergyAdapter(ift.Energy):
- def __init__(self, position, op):
- super(EnergyAdapter, self).__init__(position)
- self._op = op
- pvar = ift.Linearization.make_var(position)
- self._res = op(pvar)
-
- def at(self, position):
- return EnergyAdapter(position, self._op)
-
- @property
- def value(self):
- return self._res.val.local_data[()]
-
- @property
- def gradient(self):
- return self._res.gradient
-
- @property
- def metric(self):
- return self._res.metric
-
-
def get_2D_exposure():
x_shape, y_shape = position_space.shape
@@ -120,7 +97,7 @@ if __name__ == '__main__':
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood)
- H = EnergyAdapter(position, H)
+ H = ift.EnergyAdapter(position, H)
#ift.extra.check_value_gradient_consistency(H)
H = H.make_invertible(ic_cg)
H, convergence = minimizer(H)
diff --git a/demos/getting_started_3b.py b/demos/getting_started_3b.py
index 2a7ce7d27..6cd4054c5 100644
--- a/demos/getting_started_3b.py
+++ b/demos/getting_started_3b.py
@@ -25,28 +25,6 @@ def get_random_LOS(n_los):
ends = list(np.random.uniform(0, 1, (n_los, 2)).T)
return starts, ends
-class EnergyAdapter(ift.Energy):
- def __init__(self, position, op):
- super(EnergyAdapter, self).__init__(position)
- self._op = op
- pvar = ift.Linearization.make_var(position)
- self._res = op(pvar)
-
- def at(self, position):
- return EnergyAdapter(position, self._op)
-
- @property
- def value(self):
- return self._res.val.local_data[()]
-
- @property
- def gradient(self):
- return self._res.gradient
-
- @property
- def metric(self):
- return self._res.metric
-
if __name__ == '__main__':
# FIXME description of the tutorial
np.random.seed(42)
@@ -114,7 +92,7 @@ if __name__ == '__main__':
for _ in range(N_samples)]
KL = ift.SampledKullbachLeiblerDivergence(H, samples)
- KL = EnergyAdapter(position, KL)
+ KL = ift.EnergyAdapter(position, KL)
KL = KL.make_invertible(ic_cg)
KL, convergence = minimizer(KL)
position = KL.position
diff --git a/nifty5/__init__.py b/nifty5/__init__.py
index 66bc0179b..705f12439 100644
--- a/nifty5/__init__.py
+++ b/nifty5/__init__.py
@@ -103,6 +103,7 @@ from .multi.block_diagonal_operator import BlockDiagonalOperator
from .energies.kl import SampledKullbachLeiblerDivergence
from .energies.hamiltonian import Hamiltonian
+from .energies.energy_adapter import EnergyAdapter
from .operator import Operator
from .linearization import Linearization
diff --git a/nifty5/energies/energy_adapter.py b/nifty5/energies/energy_adapter.py
new file mode 100644
index 000000000..612563716
--- /dev/null
+++ b/nifty5/energies/energy_adapter.py
@@ -0,0 +1,28 @@
+from __future__ import absolute_import, division, print_function
+
+from ..compat import *
+from ..minimization.energy import Energy
+from ..linearization import Linearization
+
+
+class EnergyAdapter(Energy):
+ def __init__(self, position, op):
+ super(EnergyAdapter, self).__init__(position)
+ self._op = op
+ pvar = Linearization.make_var(position)
+ self._res = op(pvar)
+
+ def at(self, position):
+ return EnergyAdapter(position, self._op)
+
+ @property
+ def value(self):
+ return self._res.val.local_data[()]
+
+ @property
+ def gradient(self):
+ return self._res.gradient
+
+ @property
+ def metric(self):
+ return self._res.metric
--
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