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)