1 files changed, 46 insertions, 0 deletions
diff --git a/artifacts/spectral_frontier_probe/diag6.py b/artifacts/spectral_frontier_probe/diag6.py
new file mode 100644
index 0000000..0158f9d
--- /dev/null
+++ b/artifacts/spectral_frontier_probe/diag6.py
@@ -0,0 +1,46 @@
+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(1)
+
+def icp(XV,XT,O,iters=40):
+    for _ in range(iters):
+        p=hung(XV,XT@O)
+        u,s,vt=np.linalg.svd(XT[p].T@XV); Onew=u@vt
+        if np.allclose(Onew,O,atol=1e-10): O=Onew; break
+        O=Onew
+    return hung(XV,XT@O),O
+
+print("### BLIND: random-restart ICP over O(r), scored by QAP energy")
+for r in (6,8,10,12,16):
+    XV=UV[:,:r]*np.sqrt(np.abs(wV[:r])); XT=UT[:,:r]*np.sqrt(np.abs(wT[:r]))
+    t0=time.time(); best=(1e9,None)
+    R=200
+    for t in range(R):
+        O=ortho_group.rvs(r,random_state=int(rng.integers(1<<30)))
+        p,_=icp(XV,XT,O)
+        e=energy(p)
+        if e<best[0]: best=(e,p)
+    p=best[1]; pd=descend(p)
+    print(f"  r={r:3d} R={R}: best-E {best[0]:.4f} acc {acc(p):.3f} | after descent E {energy(pd):.4f} acc {acc(pd):.3f}  [{time.time()-t0:.0f}s]")