def test_tune_not_inplace():
orig_scaling = np.array([0.001, 0.1])
returned_scaling = tune(orig_scaling, acc_rate=0.6)
assert not returned_scaling is orig_scaling
assert np.all(orig_scaling == np.array([0.001, 0.1]))
pass
def astep(self, q0):
if self.stage == 0:
l_new = self.logp_forw(q0)
if not num.isfinite(l_new[self._llk_index]):
raise ValueError('Got NaN in likelihood evaluation! '
'Invalid model definition? '
'Or starting point outside prior bounds!')
q_new = q0
else:
if self.stage_sample == 0:
self.proposal_samples_array = self.proposal_dist(
self.n_steps).astype(tconfig.floatX)
if not self.steps_until_tune and self.tune:
# Tune scaling parameter
logger.debug('Tuning: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
self.scaling = utility.scalar2floatX(
tune(self.scaling,
self.accepted / float(self.tune_interval)))
# Reset counter
self.steps_until_tune = self.tune_interval
self.accepted = 0
logger.debug('Get delta: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
delta = self.proposal_samples_array[self.stage_sample, :] * \
self.scaling
if self.any_discrete:
if self.all_discrete:
delta = num.round(delta, 0)
q0 = q0.astype(int)
q = (q0 + delta).astype(int)
else:
delta[self.discrete] = num.round(delta[self.discrete],
0).astype(int)
q = q0 + delta
q = q[self.discrete].astype(int)
else:
q = q0 + delta
try:
l0 = self.chain_previous_lpoint[self.chain_index]
llk0 = l0[self._llk_index]
except IndexError:
l0 = self.logp_forw(q0)
self.chain_previous_lpoint[self.chain_index] = l0
llk0 = l0[self._llk_index]
if self.check_bound:
logger.debug('Checking bound: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
varlogp = self.check_bnd(q)
if num.isfinite(varlogp):
logger.debug('Calc llk: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
lp = self.logp_forw(q)
logger.debug('Select llk: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
q_new, accepted = metrop_select(
self.beta *
(lp[self._llk_index] - l0[self._llk_index]), q, q0)
if accepted:
logger.debug('Accepted: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
logger.debug('proposed: %f previous: %f' %
(lp[self._llk_index], llk0))
self.accepted += 1
l_new = lp
self.chain_previous_lpoint[self.chain_index] = l_new
else:
logger.debug('Rejected: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
logger.debug(
'proposed: %f previous: %f' %
(lp[self._llk_index], l0[self._llk_index]))
l_new = l0
else:
q_new = q0
l_new = l0
else:
logger.debug('Calc llk: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
lp = self.logp_forw(q)
logger.debug('Select: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
q_new, accepted = metrop_select(
self.beta * (lp[self._llk_index] - llk0), q, q0)
if accepted:
self.accepted += 1
l_new = lp
self.chain_previous_lpoint[self.chain_index] = l_new
else:
l_new = l0
logger.debug('Counters: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
self.steps_until_tune -= 1
self.stage_sample += 1
# reset sample counter
if self.stage_sample == self.n_steps:
self.stage_sample = 0
logger.debug('End step: Chain_%i step_%i' %
(self.chain_index, self.stage_sample))
return q_new, l_new