diff options
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/rain_ep_bias_train.py | 4 | ||||
| -rw-r--r-- | experiments/rain_ep_dillavou_smoke.py | 25 |
2 files changed, 29 insertions, 0 deletions
diff --git a/experiments/rain_ep_bias_train.py b/experiments/rain_ep_bias_train.py index d6f1f85..70ff441 100644 --- a/experiments/rain_ep_bias_train.py +++ b/experiments/rain_ep_bias_train.py @@ -61,6 +61,8 @@ def parse_args() -> argparse.Namespace: help="released physical state-dependence report; omitted means constant B") parser.add_argument("--predictor-rate", type=float, default=0.1) parser.add_argument( + "--predictor-kind", choices=("nlms", "ols"), default="nlms") + parser.add_argument( "--neutral-cadence", type=int, default=1, help="training steps per neutral update; zero freezes after calibration") parser.add_argument("--calibration-batches", type=int, default=0) @@ -247,6 +249,7 @@ def main() -> None: neutral_cadence=args.neutral_cadence, drift_ratio=args.dillavou_drift_ratio, empirical_profile=empirical_profile, + predictor_kind=args.predictor_kind, seed=args.seed + 1729, ) attach_dillavou_to_rain_estimator(estimator, corrector) @@ -370,6 +373,7 @@ def main() -> None: or corrector.empirical_profile is None else corrector.empirical_profile.as_dict()), "predictor_rate": args.predictor_rate, + "predictor_kind": args.predictor_kind, "neutral_cadence": args.neutral_cadence, "layer_calibration_steps": args.layer_calibration_steps, "layer_bias_normalization": args.layer_bias_normalization, diff --git a/experiments/rain_ep_dillavou_smoke.py b/experiments/rain_ep_dillavou_smoke.py index 39bf5fd..3d35299 100644 --- a/experiments/rain_ep_dillavou_smoke.py +++ b/experiments/rain_ep_dillavou_smoke.py @@ -148,6 +148,30 @@ def main() -> None: assert held_innovation_error < 0.05 * held_constant_error, ( held_innovation_error, held_constant_error) + # Two distinct local neutral states identify an exactly affine field when + # the online sufficient-statistics predictor is selected. + profile_ols = DillavouUpdateCorrector( + mode="innovation", bias_ratio=0.2, predictor_rate=1.0, + calibration_steps=2, neutral_cadence=0, + empirical_profile=profile, predictor_kind="ols", seed=67) + for displacement in (-0.25, 0.25): + state = [ + value + displacement * scale + for value, scale in zip(parameters_a, parameter_scale) + ] + profile_ols.apply(clean_a, state) + ols_state = [ + value + 0.7 * scale + for value, scale in zip(parameters_a, parameter_scale) + ] + held_ols = profile_ols.apply(clean_b, ols_state) + held_ols_error = sum( + float((actual - target).square().sum()) + for actual, target in zip(held_ols, clean_b) + ) + assert held_ols_error < 1e-10, held_ols_error + assert profile_ols.debiaser.neutral_observations == 2 + # Integration check: the corruption is attached after Rain's hand-written # local EP estimator and introduces no autograd graph. energy, network, cost, augmented, minimizer, estimator = build_estimator( @@ -183,6 +207,7 @@ def main() -> None: profile.normalized_state_variations), "released_profile_heldout_mse_ratio_affine_over_constant": ( held_innovation_error / held_constant_error), + "released_profile_two_probe_ols_mse": held_ols_error, "autodiff_used_for_learning": False, }) |