Reduce amount of SAMS debug output
@@ -582,8 +582,8 @@ class SAMSSampler(MultiStateSampler):
# Retrieve target probabilities
log_pi_k = self.log_target_probabilities
pi_k = np.exp(self.log_target_probabilities)
- logger.debug(' log target probabilities log_pi_k: %s' % str(log_pi_k))
- logger.debug(' target probabilities pi_k: %s' % str(pi_k))
+ #logger.debug(' log target probabilities log_pi_k: %s' % str(log_pi_k))
+ #logger.debug(' target probabilities pi_k: %s' % str(pi_k))
# Update which stage we're in, checking histogram flatness
self._update_stage()
@@ -610,7 +610,7 @@ class SAMSSampler(MultiStateSampler):
if self.weight_update_method == 'optimal':
# Based on Eq. 9 of Ref. [1]
logZ_update = gamma * np.exp(-log_pi_k[state_index])
- logger.debug(' optimal logZ increment: %s' % str(logZ_update))
+ #logger.debug(' optimal logZ increment: %s' % str(logZ_update))
self._logZ[state_index] += logZ_update
elif self.weight_update_method == 'rao-blackwellized':
# Based on Eq. 12 of Ref [1]
@@ -618,11 +618,11 @@ class SAMSSampler(MultiStateSampler):
# TODO: Can we use masked arrays for this purpose?
log_P_k = jump_and_mix_data.replica_log_P_k[replica_index,:]
neighborhood = np.where(self._neighborhoods[replica_index,:])[0] # compact list of states defining neighborhood
- logger.debug(' using neighborhood: %s' % str(neighborhood))
- logger.debug(' using log_P_k : %s' % str(log_P_k[neighborhood]))
- logger.debug(' using log_pi_k: %s' % str(log_pi_k[neighborhood]))
+ #logger.debug(' using neighborhood: %s' % str(neighborhood))
+ #logger.debug(' using log_P_k : %s' % str(log_P_k[neighborhood]))
+ #logger.debug(' using log_pi_k: %s' % str(log_pi_k[neighborhood]))
logZ_update = gamma * np.exp(log_P_k[neighborhood] - log_pi_k[neighborhood])
- logger.debug(' Rao-Blackwellized logZ increment: %s' % str(logZ_update))
+ #logger.debug(' Rao-Blackwellized logZ increment: %s' % str(logZ_update))
self._logZ[neighborhood] += logZ_update
else:
raise Exception('Programming error: Unreachable code')
