def integrator(self, metric):
system = systems.GaussianEuclideanMetricSystem(
neg_log_dens=lambda q: 0.125 * np.sum(q**4),
metric=metric,
grad_neg_log_dens=lambda q: 0.5 * q**3,
)
return integrators.LeapfrogIntegrator(system, 0.1)
def integrator(self, metric):
system = systems.EuclideanMetricSystem(
neg_log_dens=lambda q: 0.5 * np.sum(q**2),
metric=metric,
grad_neg_log_dens=lambda q: q,
)
return integrators.LeapfrogIntegrator(system, 0.25)
def __init__(self):
super().__init__()
system = systems.GaussianEuclideanMetricSystem(
lambda q: 0, grad_neg_log_dens=lambda q: 0 * q)
self.integrator = integrators.LeapfrogIntegrator(system, 0.5)
self.states = {
size: [
states.ChainState(pos=q, mom=p, dir=1)
for q, p in self.rng.standard_normal((N_STATE, 2, size))
]
for size in SIZES
}
self.h_diff_tol = 1e-10
def __init__(self):
rng = np.random.RandomState(SEED)
integrators_and_state_lists = []
for size in SIZES:
for metric in _generate_metrics(rng, size):
system = systems.GaussianEuclideanMetricSystem(
neg_log_dens=lambda q: 0.125 * np.sum(q**4),
metric=metric,
grad_neg_log_dens=lambda q: 0.5 * q**3)
integrator = integrators.LeapfrogIntegrator(system, 0.1)
state_list = [
ChainState(pos=q, mom=p, dir=1)
for q, p in rng.standard_normal((N_STATE, 2, size))
]
integrators_and_state_lists.append((integrator, state_list))
super().__init__(integrators_and_state_lists, h_diff_tol=1e-2)
def integrator(self, metric):
system = systems.GaussianEuclideanMetricSystem(
neg_log_dens=lambda q: 0,
metric=metric,
grad_neg_log_dens=lambda q: 0 * q)
return integrators.LeapfrogIntegrator(system, 0.5)