Merge branch 'AbsDeltaEnergyController' into 'NIFTy_5'

Add AbsDeltaEnergyController to NIFTy

See merge request !341

nifty5/__init__.py

@@ -59,7 +59,7 @@ from .probing import probe_with_posterior_samples, probe_diagonal, \
 from .minimization.line_search import LineSearch
 from .minimization.iteration_controllers import (
     IterationController, GradientNormController, DeltaEnergyController,
-    GradInfNormController)
+    GradInfNormController, AbsDeltaEnergyController)
 from .minimization.minimizer import Minimizer
 from .minimization.conjugate_gradient import ConjugateGradient
 from .minimization.nonlinear_cg import NonlinearCG

nifty5/minimization/iteration_controllers.py

@@ -15,9 +15,10 @@
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
 
+import numpy as np
+
 from ..logger import logger
 from ..utilities import NiftyMeta
-import numpy as np
 
 
 class IterationController(metaclass=NiftyMeta):
@@ -266,3 +267,70 @@ class DeltaEnergyController(IterationController):
             return self.CONVERGED
 
         return self.CONTINUE
+
+
+class AbsDeltaEnergyController(IterationController):
+    """An iteration controller checking (mainly) the energy change from one
+    iteration to the next.
+
+    Parameters
+    ----------
+    deltaE : float
+        If the difference between the last and current energies 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, deltaE, convergence_level=1, iteration_limit=None,
+                 name=None):
+        self._deltaE = deltaE
+        self._convergence_level = convergence_level
+        self._iteration_limit = iteration_limit
+        self._name = name
+
+    def start(self, energy):
+        self._itcount = -1
+        self._ccount = 0
+        self._Eold = 0.
+        return self.check(energy)
+
+    def check(self, energy):
+        self._itcount += 1
+
+        inclvl = False
+        Eval = energy.value
+        diff = abs(self._Eold-Eval)
+        if self._itcount > 0:
+            if diff < self._deltaE:
+                inclvl = True
+        self._Eold = Eval
+        if inclvl:
+            self._ccount += 1
+        else:
+            self._ccount = max(0, self._ccount-1)
+
+        # report
+        if self._name is not None:
+            logger.info(
+                "{}: Iteration #{} energy={:.6E} diff={:.6E} crit={:.1E} clvl={}"
+                .format(self._name, self._itcount, Eval, diff, self._deltaE,
+                        self._ccount))
+
+        # Are we done?
+        if self._iteration_limit is not None:
+            if self._itcount >= self._iteration_limit:
+                logger.warning(
+                    "{} Iteration limit reached. Assuming convergence"
+                    .format("" if self._name is None else self._name+": "))
+                return self.CONVERGED
+        if self._ccount >= self._convergence_level:
+            return self.CONVERGED
+
+        return self.CONTINUE