experiment: add dynamic neutral residual projection

1 files changed, 33 insertions, 0 deletions

diff --git a/experiments/conv_local_smoke.py b/experiments/conv_local_smoke.py
index dbae759..cdf1ad0 100644
--- a/experiments/conv_local_smoke.py
+++ b/experiments/conv_local_smoke.py
@@ -1003,6 +1003,26 @@ def kp_mixed_traffic_checks():
     assert stable_fit["min_residual_soma_slope"] < -9e-4
     assert stable_fit["max_applied_stability_margin"] >= 1e-3
 
+    # A deliberately inaccurate slow predictor leaves an affine neutral mode.
+    # The fast controller must remove that mode using only paired neutral
+    # soma/traffic observations, without changing the predictor parameters or
+    # reading the task instruction during its coefficient fit.
+    frozen_before_projection = [value.clone() for value in
+                                net.P_traffic + net.P_traffic_bias]
+    projected = net.mixed_apical_components(
+        instruction, forward["hiddens"], "innovation",
+        neutral_projection=True)
+    projection = projected["neutral_projection"]
+    projected_instruction_error = max(float((left - right).abs().max())
+                                      for left, right in zip(
+        projected["innovation"], instruction))
+    assert projected_instruction_error < 1e-14
+    assert projection["post_projection_traffic_rms_ratio"] < 1e-14
+    assert projection["max_absolute_post_projection_soma_slope"] < 1e-14
+    assert projection["instruction_observations"] == 0
+    assert all(torch.equal(before, after) for before, after in zip(
+        frozen_before_projection, net.P_traffic + net.P_traffic_bias))
+
     for slope, bias in zip(net.P_traffic, net.P_traffic_bias):
         slope.zero_()
         bias.zero_()
@@ -1098,6 +1118,14 @@ def kp_mixed_traffic_checks():
     assert all(not value.requires_grad for value in
                net.W + net.Q + net.P_traffic + net.P_traffic_bias
                + [net.W_out, net.R_out, net.b_out])
+    projected_result = conv_kp_mixed_traffic_step(
+        net, x, y, ConvSDILConfig(
+            eta=1e-4, eta_output=1e-4, eta_P=0.1, momentum=0.0,
+            weight_decay=0.0, learn_A=False, learn_P=True),
+        step=2, rule="innovation", predictor_every=0,
+        neutral_projection=True)
+    assert math.isfinite(projected_result["loss"])
+    assert projected_result["neutral_projection"] is not None
     assert net.mixed_elementwise_ops_per_example("matched") > (
         net.mixed_elementwise_ops_per_example("raw"))
     return {
@@ -1109,6 +1137,11 @@ def kp_mixed_traffic_checks():
             "residual_traffic_rms_ratio"],
         "kp_traffic_closed_form_residual_slope": closed_form[
             "max_absolute_residual_soma_slope"],
+        "kp_traffic_projected_instruction_error": projected_instruction_error,
+        "kp_traffic_projected_residual_ratio": projection[
+            "post_projection_traffic_rms_ratio"],
+        "kp_traffic_projected_residual_slope": projection[
+            "max_absolute_post_projection_soma_slope"],
         "kp_traffic_matched_norm_error": max(norm_errors),
         "kp_traffic_matched_direction_error": max(direction_errors),
         "kp_traffic_reciprocal_correlation_error": max(correlation_errors),