def build_minimiser(self):
assert self._params.engine in [
"SimpleLBFGS", "LBFGScurvs", "GaussNewton"
]
if self._params.engine == "SimpleLBFGS":
refiner = SimpleLBFGS(
target=self._target,
prediction_parameterisation=self._prediction_parameterisation,
log=self._params.logfile,
)
return refiner
if self._params.engine == "LBFGScurvs":
refiner = LBFGScurvs(
target=self._target,
prediction_parameterisation=self._prediction_parameterisation,
log=self._params.logfile,
)
return refiner
if self._params.engine == "GaussNewton":
refiner = GaussNewtonIterations(
target=self._target,
prediction_parameterisation=self._prediction_parameterisation,
log=self._params.logfile,
)
return refiner
def __init__(
self,
scaler,
target,
prediction_parameterisation,
constraints_manager=None,
log=None,
tracking=None,
max_iterations=20,
):
logger.info(
"Performing a round of scaling with a Gauss-Newton minimizer.\n")
ScalingLstbxBuildUpMixin.__init__(self, scaler, target,
prediction_parameterisation)
GaussNewtonIterations.__init__(
self,
target,
prediction_parameterisation,
constraints_manager,
log=log,
tracking=tracking,
max_iterations=max_iterations,
)