def stats(self):
# Update accept stat if any subtrees were accepted
if self.log_size > 0:
# Remove contribution from initial state which is always a perfect
# accept
log_sum_weight = logdiffexp_numpy(self.log_size, 0.0)
self.mean_tree_accept = np.exp(self.log_weighted_accept_sum -
log_sum_weight)
return {
"depth": self.depth,
"mean_tree_accept": self.mean_tree_accept,
"energy_error": self.proposal.energy - self.start.energy,
"energy": self.proposal.energy,
"tree_size": self.n_proposals,
"max_energy_error": self.max_energy_change,
"model_logp": self.proposal.logp,
}
def test_logdiffexp():
a = np.log([1, 2, 3, 4])
b = np.log([0, 1, 2, 3])
assert np.allclose(logdiffexp_numpy(a, b), 0)
assert np.allclose(logdiffexp(a, b).eval(), 0)