`altar.bayesian.AnnealingMethod`

Module Contents

Classes

class altar.bayesian.AnnealingMethod.AnnealingMethod(annealer, **kwds)

Base class for the various annealing implementation strategies

step

iteration = 0

wid = 0

workers

initialize(self, application): Initialize me and my parts given an {application} context

start(self, annealer): Start the annealing process from scratch

abstract restart(self, annealer): Start the annealing process from a checkpoint

top(self, annealer): Notification that we are at the beginning of an update

cool(self, annealer): Push my state forward along the cooling schedule

walk(self, annealer): Explore configuration space by walking the Markov chains

resample(self, annealer, statistics): Analyze the acceptance statistics and take the problem state to the end of the annealing step

archive(self, annealer, scaling, stats): Notify archiver to record

bottom(self, annealer): Notification that we are at the bottom of an update

finish(self, annealer): Notification that the simulation is over