d3004d19c5cac8c13de9c18935c22843527f4947,pyprob/state.py,,sample,#Any#Any#Any#Any#,79

Before Change

                // The log_prob of samples are zero for regular importance sampling (no learned proposals) as it cancels out
                current_sample = Sample(address, distribution, value, log_prob=0, controlled=True)
            else:
                current_sample = Sample(address, distribution, value, log_prob=distribution.log_prob(value), controlled=True)

            if _trace_state == TraceState.RECORD_USE_INFERENCE_NETWORK:
                previous_sample = None
                if _current_trace.length > 0:

After Change

                global _current_trace_replaced_sample_proposal_distributions
                _current_trace_inference_network.eval()
                if replace:
                    if address not in _current_trace_replaced_sample_proposal_distributions:
                        _current_trace_replaced_sample_proposal_distributions[address] = _current_trace_inference_network.forward_one_time_step(_current_trace_previous_sample, current_sample)
                    proposal_distribution = _current_trace_replaced_sample_proposal_distributions[address]
                else:
                    proposal_distribution = _current_trace_inference_network.forward_one_time_step(_current_trace_previous_sample, current_sample)
                value = proposal_distribution.sample()[0]

Instances