def setUp(self):
super(TestSMCUpdater, self).setUp()
self.precession_model = SimplePrecessionModel()
self.num_precession_model = NumericalSimplePrecessionModel()
self.expparams = TestSMCUpdater.TEST_EXPPARAMS.reshape(-1, 1)
self.outcomes = self.precession_model.simulate_experiment(
TestSMCUpdater.MODELPARAMS,
TestSMCUpdater.TEST_EXPPARAMS,
repeat=1).reshape(-1, 1)
self.updater = SMCUpdater(self.precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR)
self.updater_bayes = SMCUpdaterBCRB(self.precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR,
adaptive=True)
self.num_updater = SMCUpdater(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR)
self.num_updater_bayes = SMCUpdaterBCRB(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR,
adaptive=True)
def instantiate_model(self):
return RandomWalkModel(SimplePrecessionModel(),
step_distribution=NormalDistribution(mean=0.1,
var=0.1))
def instantiate_model(self):
return SimplePrecessionModel()
def instantiate_model(self):
return MLEModel(SimplePrecessionModel(), likelihood_power=2)
def instantiate_model(self):
return PoisonedModel(SimplePrecessionModel(), n_samples=10, hedge=0.01)
def instantiate_model(self):
return PoisonedModel(SimplePrecessionModel(), tol=1e-4)
def instantiate_model(self):
return BinomialModel(SimplePrecessionModel())
def instantiate_model(self):
return ALEApproximateModel(SimplePrecessionModel())
