Simulation code and data for the TMC submission

code/exp4_mismatch.py

@@ -0,0 +1,54 @@
+"""E4: robustness of the decomposition receiver to affinity estimation error.
+
+DR runs with a_hat = a + delta; theory predicts O(delta^2) degradation.
+
+Outputs: data/e4_mismatch.csv
+(figures come from replot_all.py only)
+"""
+import math
+import os
+import numpy as np
+from semantic_mac import (affinity_matrix, matched_filter, demux_dr,
+                          sample_latents_isotropic, metrics)
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+FIG = os.path.join(HERE, "..", "fig")
+DATA = os.path.join(HERE, "..", "data")
+os.makedirs(FIG, exist_ok=True)
+os.makedirs(DATA, exist_ok=True)
+
+U, D, DC = 4, 64, 16
+BATCH = 4000
+BETA = 0.5
+
+
+def main():
+    rng = np.random.default_rng(11)
+    a = math.sqrt(BETA) * np.ones(U)
+    B = affinity_matrix(a)
+    Vc = np.eye(D)[:, :DC]
+    deltas = np.arange(-0.20, 0.201, 0.04)
+    rows = []
+    for snr_db in (5, 10, 15):
+        rho = 10 ** (snr_db / 10)
+        z = sample_latents_isotropic(BATCH, U, D, DC, a, rng)
+        tilde, h = matched_filter(z, B, rho, rng)
+        for d0 in deltas:
+            a_hat = np.clip(a + d0, 0.02, 0.98)
+            B_hat = affinity_matrix(a_hat)
+            cos, _, ser = metrics(
+                demux_dr(tilde, B_hat, h, rho, a_hat, Vc), z)
+            rows.append((snr_db, float(d0), cos, ser))
+            print(f"snr={snr_db} delta={d0:+.2f} cos={cos:.4f} ser={ser:.4f}")
+    with open(os.path.join(DATA, "e4_mismatch.csv"), "w") as f:
+        f.write("snr,delta,cos,ser\n")
+        for r in rows:
+            f.write(",".join(str(x) for x in r) + "\n")
+
+    # figures are produced only by the canonical replot_all.py (uniform
+    # geometry); experiment scripts write CSVs exclusively.
+    print("E4 done. Run replot_all.py to regenerate the figures.")
+
+
+if __name__ == "__main__":
+    main()