1 files changed, 49 insertions, 0 deletions
diff --git a/artifacts/spectral_frontier_probe/confirm.py b/artifacts/spectral_frontier_probe/confirm.py
new file mode 100644
index 0000000..d0fc003
--- /dev/null
+++ b/artifacts/spectral_frontier_probe/confirm.py
@@ -0,0 +1,49 @@
+import sys, time, torch, numpy as np
+sys.path.insert(0,'/home/yurenh2/emm')
+from scipy.optimize import linear_sum_assignment
+from scipy.stats import ortho_group
+from worldalign.synth_fast_gate import fast_pair_descent
+dev='cuda:3'
+def standardise(M):
+    M=np.asarray(M,dtype=np.float64); mask=~np.eye(len(M),dtype=bool); v=M[mask]
+    out=(M-v.mean())/v.std(); np.fill_diagonal(out,0.0); return out
+d=torch.load('/home/yurenh2/emm/artifacts/synth_v1/omit_size.pt',map_location='cpu')
+V=standardise(d['visual_field']); T=standardise(d['text_field']); N=len(V)
+Vt=torch.tensor(V,dtype=torch.float32,device=dev); Tt=torch.tensor(T,dtype=torch.float32,device=dev)
+CONST=float((Tt*Tt).sum()+(Vt*Vt).sum())
+def energy(p):
+    P=torch.as_tensor(np.asarray(p),dtype=torch.long,device=dev)
+    return (CONST-2.0*float((Tt[P[:,None],P[None,:]]*Vt).sum()))/(N*(N-1))
+def descend(p,steps=4000):
+    P=torch.as_tensor(np.asarray(p),dtype=torch.long,device=dev)
+    return fast_pair_descent(Tt,Vt,P,steps).cpu().numpy()
+truth=np.arange(N); acc=lambda p: float((p==truth).mean())
+wV,UV=np.linalg.eigh(V); wV=wV[::-1]; UV=UV[:,::-1]
+wT,UT=np.linalg.eigh(T); wT=wT[::-1]; UT=UT[:,::-1]
+def hung(A,B):
+    C=((A**2).sum(1)[:,None]+(B**2).sum(1)[None,:]-2*A@B.T); r,c=linear_sum_assignment(C); return c
+rng=np.random.default_rng(20260801)
+def icp(XV,XT,O,iters=30):
+    for _ in range(iters):
+        p=hung(XV,XT@O)
+        u,s,vt=np.linalg.svd(XT[p].T@XV); On=u@vt
+        if np.allclose(On,O,atol=1e-10): O=On; break
+        O=On
+    return hung(XV,XT@O)
+t0=time.time(); pool=[]
+for r in (4,6,8,10,12,14,16,20,24):
+    XV=UV[:,:r]*np.sqrt(np.abs(wV[:r])); XT=UT[:,:r]*np.sqrt(np.abs(wT[:r]))
+    cand=[]
+    for t in range(120):
+        p=icp(XV,XT,ortho_group.rvs(r,random_state=int(rng.integers(1<<30))))
+        cand.append((energy(p),p))
+    cand.sort(key=lambda z:z[0])
+    best=None
+    for e,p in cand[:5]:
+        pd=descend(p); ed=energy(pd)
+        if best is None or ed<best[0]: best=(ed,pd)
+    pool.append((best[0],best[1],r))
+    print(f"  r={r:3d}: pre-descent best E {cand[0][0]:.4f} (acc {acc(cand[0][1]):.3f}) -> post-descent E {best[0]:.4f} acc {acc(best[1]):.3f}",flush=True)
+pool.sort(key=lambda z:z[0])
+print(f"\nBLIND PICK (lowest E over all r): r={pool[0][2]}  E={pool[0][0]:.4f}  ACC={acc(pool[0][1]):.3f}")
+print(f"E(truth)={energy(truth):.4f}   total wall {time.time()-t0:.0f}s")