| import math |
|
|
|
|
| def _imports(): |
| try: |
| from bayesian_langchain.smc import ParticleFilter, Proposal, SMCConfig |
| except ImportError: |
| from bayesian_langgraph.smc import ParticleFilter, Proposal, SMCConfig |
| return ParticleFilter, Proposal, SMCConfig |
|
|
|
|
| def test_final_step_preserves_nonuniform_posterior_weights(): |
| ParticleFilter, Proposal, SMCConfig = _imports() |
| pf = ParticleFilter(SMCConfig(n_particles=64, n_steps=1, ess_resample_fraction=0.99, seed=7)) |
|
|
| def propose(state, t): |
| return [Proposal("good", 0.5), Proposal("bad", 0.5)] |
|
|
| def transition(state, action, t): |
| return action |
|
|
| def log_target(prev, action, nxt, t): |
| return 0.0 if action == "good" else math.log(1e-5) |
|
|
| particles = pf.run(None, propose, transition, log_target) |
| probs = [p.posterior for p in particles] |
| assert abs(sum(probs) - 1.0) < 1e-12 |
| |
| assert max(probs) - min(probs) > 1e-3 |
|
|
|
|
| def test_importance_weight_contains_inverse_proposal_probability(): |
| ParticleFilter, Proposal, SMCConfig = _imports() |
| pf = ParticleFilter(SMCConfig(n_particles=256, n_steps=1, seed=11)) |
|
|
| def propose(state, t): |
| return [Proposal("a", 0.9), Proposal("b", 0.1)] |
|
|
| def transition(state, action, t): |
| return action |
|
|
| |
| |
| def log_target(prev, action, nxt, t): |
| return math.log(0.5) |
|
|
| particles = pf.run(None, propose, transition, log_target) |
| mass = {"a": 0.0, "b": 0.0} |
| for p in particles: |
| mass[p.state] += p.posterior |
| assert abs(mass["a"] - 0.5) < 0.15 |
| assert abs(mass["b"] - 0.5) < 0.15 |
|
|
