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, *args, **kwargs):
logger.info(
"Performing a round of scaling with an LBFGS minimizer. \n")
ScalingRefinery.__init__(self, scaler, *args, **kwargs)
SimpleLBFGS.__init__(self, *args, **kwargs)
def __init__(self, model, *args, **kwargs):
self.model = model
self.parameterisation = kwargs["prediction_parameterisation"]
SimpleLBFGS.__init__(self, *args, **kwargs)