1 files changed, 99 insertions, 0 deletions
diff --git a/experiments/bci_smoke.py b/experiments/bci_smoke.py
new file mode 100644
index 0000000..fe3df78
--- /dev/null
+++ b/experiments/bci_smoke.py
@@ -0,0 +1,99 @@
+"""CPU-only mechanics checks for the continuous Harnett-signature BCI."""
+import os
+import sys
+
+import torch
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from sdil.bci import (BCIConfig, BCISDIL, causal_roles, generate_trajectories,
+                      run_day)
+
+
+def check_roles_and_pairing():
+    cfg = BCIConfig(days=2, episodes_per_day=8, steps_per_episode=4)
+    role = causal_roles(cfg)
+    assert torch.allclose(role[:5], torch.full((5,), 0.2))
+    assert torch.allclose(role[5:10], torch.full((5,), -0.2))
+    assert torch.equal(role[10:], torch.zeros(30))
+    first = generate_trajectories(cfg, 11)
+    second = generate_trajectories(cfg, 11)
+    third = generate_trajectories(cfg, 12)
+    assert torch.equal(first.context, second.context)
+    assert torch.equal(first.process_noise, second.process_noise)
+    assert torch.equal(first.perturbations, second.perturbations)
+    assert not torch.equal(first.context, third.context)
+    assert torch.equal(first.context[..., 0], torch.ones_like(first.context[..., 0]))
+    print("BCI causal roles and task-seed pairing: exact")
+
+
+def check_causal_estimator():
+    cfg = BCIConfig(days=2, episodes_per_day=4096, steps_per_episode=2)
+    model = BCISDIL(cfg, model_seed=3)
+    generator = torch.Generator().manual_seed(4)
+    soma = torch.randn(4096, cfg.n_neurons, generator=generator) * 0.2
+    xi = torch.empty_like(soma).bernoulli_(0.5, generator=generator).mul_(2).sub_(1)
+    estimate = model.causal_targets(soma, cfg.target, xi)
+    exact = model.exact_causal_direction(soma, cfg.target)
+    mean_estimate = estimate.mean(0)
+    mean_exact = exact.mean(0)
+    cosine = torch.nn.functional.cosine_similarity(
+        mean_estimate, mean_exact, dim=0).item()
+    assert cosine > 0.99
+    assert estimate[:, 10:].abs().mean() > 0  # simultaneous cross-cell variance
+    assert mean_estimate[10:].abs().max() < 0.03
+    print(f"BCI simultaneous causal estimator mean cosine: {cosine:.6f}")
+
+
+def check_predictor_identification():
+    cfg = BCIConfig(days=2, episodes_per_day=256, steps_per_episode=2,
+                    predictor_eta=0.2)
+    model = BCISDIL(cfg, model_seed=5)
+    generator = torch.Generator().manual_seed(6)
+    active = torch.ones(256, dtype=torch.bool)
+    for _ in range(100):
+        soma = torch.randn(256, cfg.n_neurons, generator=generator).tanh()
+        ordinary = model.coupling * soma
+        model.update_predictor(soma, ordinary, active)
+    assert (model.P - model.coupling).abs().max() < 1e-4
+    assert model.P_bias.abs().max() < 1e-5
+    print("BCI neutral per-cell predictor identifies ordinary coupling: exact")
+
+
+def check_phase_masks_and_velocity_reset():
+    cfg = BCIConfig(days=2, episodes_per_day=8, steps_per_episode=4,
+                    kappa=0.3, feedback="error_velocity")
+    trajectories = generate_trajectories(cfg, 7)
+    initial = BCISDIL(cfg, model_seed=8)
+    intact = initial.clone()
+    online_lesion = initial.clone()
+    plasticity_lesion = initial.clone()
+
+    active = torch.ones(8, dtype=torch.bool)
+    args = (
+        trajectories.context[0, :, 0], trajectories.process_noise[0, :, 0],
+        trajectories.perturbations[0, :, 0], torch.zeros(8, cfg.n_neurons),
+        None, active, 1)
+    out_intact = intact.step(*args, control_gain=1.0, plasticity_gain=1.0,
+                             learn_vectorizer=False)
+    out_online = online_lesion.step(*args, control_gain=0.0, plasticity_gain=1.0,
+                                    learn_vectorizer=False)
+    out_plastic = plasticity_lesion.step(
+        *args, control_gain=1.0, plasticity_gain=0.0, learn_vectorizer=False)
+    assert not torch.equal(out_intact["soma"], out_online["soma"])
+    assert torch.equal(intact.W, online_lesion.W)
+    assert torch.equal(out_intact["soma"], out_plastic["soma"])
+    assert torch.equal(plasticity_lesion.W, initial.W)
+    assert torch.equal(out_intact["feedback"][:, 1], torch.zeros(8))
+
+    day = run_day(initial.clone(), trajectories, 0, collect=True)
+    assert len(day["events"]) == cfg.steps_per_episode
+    assert day["global_step"] == cfg.steps_per_episode
+    print("BCI online/plasticity phase masks and episode velocity reset: exact")
+
+
+if __name__ == "__main__":
+    check_roles_and_pairing()
+    check_causal_estimator()
+    check_predictor_identification()
+    check_phase_masks_and_velocity_reset()
+    print("ALL BCI MECHANICS CHECKS PASSED")