def _hamiltonian_step(self, start, p0, step_size):
n_steps = max(1, int(self.path_length / step_size))
n_steps = min(self.max_steps, n_steps)
energy_change = -np.inf
state = start
last = state
div_info = None
try:
for _ in range(n_steps):
last = state
state = self.integrator.step(step_size, state)
except IntegrationError as e:
div_info = DivergenceInfo("Integration failed.", e, last, None)
else:
if not np.isfinite(state.energy):
div_info = DivergenceInfo(
"Divergence encountered, bad energy.", None, last, state)
energy_change = start.energy - state.energy
if np.isnan(energy_change):
energy_change = -np.inf
if np.abs(energy_change) > self.Emax:
div_info = DivergenceInfo(
"Divergence encountered, large integration error.", None,
last, state)
accept_stat = min(1, np.exp(energy_change))
if div_info is not None or np.random.rand() >= accept_stat:
end = start
accepted = False
else:
end = state
accepted = True
stats = {
"path_length": self.path_length,
"n_steps": n_steps,
"accept": accept_stat,
"energy_error": energy_change,
"energy": state.energy,
"accepted": accepted,
"model_logp": state.model_logp,
}
return HMCStepData(end, accept_stat, div_info, stats)
def _hamiltonian_step(self, start, p0, step_size):
n_steps = max(1, int(self.path_length / step_size))
n_steps = min(self.max_steps, n_steps)
energy_change = -np.inf
state = start
div_info = None
try:
for _ in range(n_steps):
state = self.integrator.step(step_size, state)
except IntegrationError as e:
div_info = DivergenceInfo('Divergence encountered.', e, state)
else:
if not np.isfinite(state.energy):
div_info = DivergenceInfo(
'Divergence encountered, bad energy.', None, state)
energy_change = start.energy - state.energy
if np.isnan(energy_change):
energy_change = -np.inf
if np.abs(energy_change) > self.Emax:
div_info = DivergenceInfo(
'Divergence encountered, large integration error.', None,
state)
accept_stat = min(1, np.exp(energy_change))
if div_info is not None or np.random.rand() >= accept_stat:
end = start
accepted = False
else:
end = state
accepted = True
stats = {
'path_length': self.path_length,
'n_steps': n_steps,
'accept': accept_stat,
'energy_error': energy_change,
'energy': state.energy,
'accepted': accepted,
'model_logp': state.model_logp,
}
return HMCStepData(end, accept_stat, div_info, stats)
def _hamiltonian_step(self, start, p0, step_size):
if self.tune and self.iter_count < 200:
max_treedepth = self.early_max_treedepth
else:
max_treedepth = self.max_treedepth
tree = _Tree(len(p0), self.integrator, start, step_size, self.Emax)
for _ in range(max_treedepth):
direction = logbern(np.log(0.5)) * 2 - 1
divergence_info, turning = tree.extend(direction)
if divergence_info or turning:
break
else:
if not self.tune:
self._reached_max_treedepth += 1
stats = tree.stats()
accept_stat = stats["mean_tree_accept"]
return HMCStepData(tree.proposal, accept_stat, divergence_info, stats)
def _hamiltonian_step(self, start, p0, step_size):
path_length = np.random.rand() * self.path_length
n_steps = max(1, int(path_length / step_size))
energy_change = -np.inf
state = start
div_info = None
try:
for _ in range(n_steps):
state = self.integrator.step(step_size, state)
except IntegrationError as e:
div_info = DivergenceInfo('Divergence encountered.', e, state)
else:
if not np.isfinite(state.energy):
div_info = DivergenceInfo('Divergence encountered, bad energy.', None, state)
energy_change = start.energy - state.energy
if np.abs(energy_change) > self.Emax:
div_info = DivergenceInfo('Divergence encountered, large integration error.',
None, state)
accept_stat = min(1, np.exp(energy_change + state.log_jac))
if div_info is not None or np.random.rand() >= accept_stat:
end = start
accepted = False
else:
end = state
accepted = True
stats = {
'path_length': path_length,
'n_steps': n_steps,
'accept': accept_stat,
'energy_error': energy_change,
'energy': state.energy,
'accepted': accepted,
# 'log_jacobian': state.log_jac, # TODO: backends/ndarray.py to allow this
}
return HMCStepData(end, accept_stat, div_info, stats)