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Source code for dials.algorithms.symmetry.cosym.engine
"""LBFGS refinement engine for cosym analysis."""
from __future__ import absolute_import, division, print_function
import logging
logger = logging.getLogger(__name__)
import scitbx.lbfgs
[docs]class lbfgs_with_curvs(object):
"""Minimise a target function using the LBFGS minimiser.
Implementation of an LBFGS minimiser using curvature information, according
to the interface defined by :mod:`scitbx.lbfgs`.
"""
def __init__(self, target, coords, use_curvatures=True, termination_params=None):
"""Initialise an lbfgs_with_curvs object.
Args:
target (dials.algorithms.target.Target): The target function to minimise.
coords (scitbx.array_family.flex.double): The starting coordinates for
minimisation.
use_curvatures (bool): Whether or not to use curvature information in the
minimisation. Defaults to True.
termination_params (scitbx.lbfgs.termination_parameters):
Override the default termination parameters for the minimisation.
"""
self.target = target
self.dim = len(coords)
self.x = coords
if use_curvatures:
self.diag_mode = "always"
else:
self.diag_mode = None
self.minimizer = scitbx.lbfgs.run(
target_evaluator=self, termination_params=termination_params
)
[docs] def compute_functional_gradients_diag(self):
"""Compute the functional, gradients and diagonal.
Returns:
tuple: A tuple of the functional, gradients and diagonal, where the
diagonal is the reciprocal of the curvatures.
"""
f, g, curvs = self.compute_functional_gradients_and_curvatures()
# Curvatures of zero will cause a crash, because their inverse is taken.
assert curvs.all_gt(0.0)
diags = 1.0 / curvs
return f, g, diags
[docs] def compute_functional_gradients_and_curvatures(self):
"""Compute the functional, gradients and curvatures.
Returns:
tuple: A tuple of the functional, gradients and curvatures.
"""
self.f, self.g = self.target.compute_functional_and_gradients(self.x)
self.c = self.curvatures()
return self.f, self.g, self.c
[docs] def compute_functional_and_gradients(self):
"""Compute the functional and gradients.
Returns:
tuple: A tuple of the functional and gradients.
"""
self.f, self.g = self.target.compute_functional_and_gradients(self.x)
return self.f, self.g
[docs] def callback_after_step(self, minimizer):
"""Log progress after each successful step of the minimisation."""
logger.debug("minimization step: f, iter, nfun:")
logger.debug("%s %s %s" % (self.f, minimizer.iter(), minimizer.nfun()))
