def do(trials, successes):
if kind == 'hmc':
proposal_kernel_kwargs = self.hmc_kwargs()
else:
proposal_kernel_kwargs = self.nuts_kwargs()
return windowed_sampling._windowed_adaptive_impl(
n_draws=9,
joint_dist=get_joint_distribution(trials),
kind=kind,
n_chains=11,
proposal_kernel_kwargs=proposal_kernel_kwargs,
num_adaptation_steps=525,
dual_averaging_kwargs={'target_accept_prob': 0.76},
trace_fn=None,
return_final_kernel_results=False,
discard_tuning=True,
seed=test_util.test_seed(),
successes=successes)
def do(seed, z):
if kind == 'hmc':
proposal_kernel_kwargs = self.hmc_kwargs()
else:
proposal_kernel_kwargs = self.nuts_kwargs()
return windowed_sampling._windowed_adaptive_impl(
n_draws=10,
joint_dist=joint_dist,
kind=kind,
n_chains=2,
proposal_kernel_kwargs=proposal_kernel_kwargs,
num_adaptation_steps=21,
current_state=None,
dual_averaging_kwargs={'target_accept_prob': 0.76},
trace_fn=None,
return_final_kernel_results=False,
discard_tuning=True,
seed=seed,
chain_axis_names=self.axis_name,
z=z)
