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198bd6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | 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
# If the old final-resampling bug returned, these would all be 1/N.
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
# Equal target mass for both actions. Importance correction should cancel
# the 9:1 proposal imbalance in the weighted posterior, up to MC error.
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
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