From 0d71c455d6ff7271583822af7ad2dfb6787a6f11 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Fri, 29 Sep 2017 18:30:42 +0200
Subject: [PATCH] no more custom convergence measures
---
demos/paper_demos/cartesian_wiener_filter.py | 2 +-
nifty/energies/line_energy.py | 6 ++----
nifty/energies/quadratic_energy.py | 16 ++++------------
nifty/minimization/conjugate_gradient.py | 5 +----
.../minimization/default_iteration_controller.py | 14 ++++----------
5 files changed, 12 insertions(+), 31 deletions(-)
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index eeab6ab5c..69cc161a7 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -90,7 +90,7 @@ if __name__ == "__main__":
# Wiener filter
j = R_harmonic.adjoint_times(N.inverse_times(data))
- ctrl = ift.DefaultIterationController(verbose=True, tol_custom=1e-3, convergence_level=3)
+ ctrl = ift.DefaultIterationController(verbose=True, iteration_limit=100)
inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
diff --git a/nifty/energies/line_energy.py b/nifty/energies/line_energy.py
index f0e67deef..7215072bc 100644
--- a/nifty/energies/line_energy.py
+++ b/nifty/energies/line_energy.py
@@ -95,10 +95,8 @@ class LineEnergy(object):
"""
- return self.__class__(line_position,
- self.energy,
- self.line_direction,
- offset=self.line_position)
+ return LineEnergy(line_position, self.energy, self.line_direction,
+ offset=self.line_position)
@property
def value(self):
diff --git a/nifty/energies/quadratic_energy.py b/nifty/energies/quadratic_energy.py
index 2f97da569..7194c66be 100644
--- a/nifty/energies/quadratic_energy.py
+++ b/nifty/energies/quadratic_energy.py
@@ -8,23 +8,21 @@ class QuadraticEnergy(Energy):
position-independent.
"""
- def __init__(self, position, A, b, _grad=None, _bnorm=None):
+ def __init__(self, position, A, b, _grad=None):
super(QuadraticEnergy, self).__init__(position=position)
self._A = A
self._b = b
- self._bnorm = _bnorm
if _grad is not None:
self._Ax = _grad + self._b
else:
self._Ax = self._A(self.position)
def at(self, position):
- return self.__class__(position=position, A=self._A, b=self._b,
- _bnorm=self.norm_b)
+ return QuadraticEnergy(position=position, A=self._A, b=self._b)
def at_with_grad(self, position, grad):
- return self.__class__(position=position, A=self._A, b=self._b,
- _grad=grad, _bnorm=self.norm_b)
+ return QuadraticEnergy(position=position, A=self._A, b=self._b,
+ _grad=grad)
@property
@memo
@@ -39,9 +37,3 @@ class QuadraticEnergy(Energy):
@property
def curvature(self):
return self._A
-
- @property
- def norm_b(self):
- if self._bnorm is None:
- self._bnorm = self._b.norm()
- return self._bnorm
diff --git a/nifty/minimization/conjugate_gradient.py b/nifty/minimization/conjugate_gradient.py
index 98ab46ecb..ed458d94f 100644
--- a/nifty/minimization/conjugate_gradient.py
+++ b/nifty/minimization/conjugate_gradient.py
@@ -73,7 +73,6 @@ class ConjugateGradient(Minimizer):
if status != controller.CONTINUE:
return energy, status
- norm_b = energy.norm_b
r = energy.gradient
if preconditioner is not None:
d = preconditioner(r)
@@ -111,9 +110,7 @@ class ConjugateGradient(Minimizer):
if gamma == 0:
return energy, controller.CONVERGED
- status = self._controller.check(energy,
- custom_measure=np.sqrt(gamma) /
- norm_b)
+ status = self._controller.check(energy)
if status != controller.CONTINUE:
return energy, status
diff --git a/nifty/minimization/default_iteration_controller.py b/nifty/minimization/default_iteration_controller.py
index cd596e3e4..fa5353dc6 100644
--- a/nifty/minimization/default_iteration_controller.py
+++ b/nifty/minimization/default_iteration_controller.py
@@ -22,26 +22,25 @@ from .iteration_controller import IterationController
class DefaultIterationController(IterationController):
def __init__(self, tol_abs_gradnorm=None, tol_rel_gradnorm=None,
- tol_custom=None, convergence_level=1, iteration_limit=None,
+ convergence_level=1, iteration_limit=None,
name=None, verbose=None):
super(DefaultIterationController, self).__init__()
self._tol_abs_gradnorm = tol_abs_gradnorm
self._tol_rel_gradnorm = tol_rel_gradnorm
- self._tol_custom = tol_custom
self._convergence_level = convergence_level
self._iteration_limit = iteration_limit
self._name = name
self._verbose = verbose
- def start(self, energy, custom_measure=None):
+ def start(self, energy):
self._itcount = -1
self._ccount = 0
if self._tol_rel_gradnorm is not None:
self._tol_rel_gradnorm_now = self._tol_rel_gradnorm \
* energy.gradient_norm
- return self.check(energy, custom_measure)
+ return self.check(energy)
- def check(self, energy, custom_measure=None):
+ def check(self, energy):
self._itcount += 1
inclvl = False
@@ -51,9 +50,6 @@ class DefaultIterationController(IterationController):
if self._tol_rel_gradnorm is not None:
if energy.gradient_norm <= self._tol_rel_gradnorm_now:
inclvl = True
- if self._tol_custom is not None and custom_measure is not None:
- if custom_measure <= self._tol_custom:
- inclvl = True
if inclvl:
self._ccount += 1
else:
@@ -67,8 +63,6 @@ class DefaultIterationController(IterationController):
msg += " Iteration #" + str(self._itcount)
msg += " energy=" + str(energy.value)
msg += " gradnorm=" + str(energy.gradient_norm)
- if custom_measure is not None:
- msg += " custom=" + str(custom_measure)
msg += " clvl=" + str(self._ccount)
print(msg)
# self.logger.info(msg)
--
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