def _nuts_next(step_size, inverse_mass_matrix, vv_state, rng):
binary_tree = build_tree(vv_update, kinetic_fn, vv_state,
inverse_mass_matrix, step_size, rng)
accept_prob = binary_tree.sum_accept_probs / binary_tree.num_proposals
num_steps = binary_tree.num_proposals
vv_state = vv_state.update(z=binary_tree.z_proposal,
potential_energy=binary_tree.z_proposal_pe,
z_grad=binary_tree.z_proposal_grad)
return vv_state, num_steps, accept_prob
def _nuts_next(step_size, inverse_mass_matrix, vv_state, rng):
binary_tree = build_tree(vv_update, kinetic_fn, vv_state,
inverse_mass_matrix, step_size, rng,
max_delta_energy=max_delta_energy,
max_tree_depth=max_treedepth)
accept_prob = binary_tree.sum_accept_probs / binary_tree.num_proposals
num_steps = binary_tree.num_proposals
vv_state = IntegratorState(z=binary_tree.z_proposal,
r=vv_state.r,
potential_energy=binary_tree.z_proposal_pe,
z_grad=binary_tree.z_proposal_grad)
return vv_state, num_steps, accept_prob, binary_tree.diverging
def fn(vv_state):
tree = build_tree(vv_update, kinetic_fn, vv_state, inverse_mass_matrix,
step_size, rng)
return tree
