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import numpy as np, torch
from scipy.optimize import linear_sum_assignment
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); truth=np.arange(N)
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
acc=lambda p: float((p==truth).mean())
CONST=float((T*T).sum()+(V*V).sum())
en=lambda p:(CONST-2*(T[np.ix_(p,p)]*V).sum())/(N*(N-1))
for r in (12,16,24):
XV=UV[:,:r]*np.sqrt(np.abs(wV[:r])); XT=UT[:,:r]*np.sqrt(np.abs(wT[:r]))
u,s,vt=np.linalg.svd(XT.T@XV); O=u@vt
idx=np.arange(r); band_mass=[]
for b in (1,2,3,4,6,r):
Mk=(np.abs(idx[:,None]-idx[None,:])<=b).astype(float)
frac=float((O**2*Mk).sum()/ (O**2).sum())
Ob=O*Mk; uu,ss,vv=np.linalg.svd(Ob); Ob=uu@vv
p=hung(XV,XT@Ob)
band_mass.append((b,round(frac,3),round(acc(p),3),round(en(p),4)))
print(f"r={r}: (bandwidth, mass of oracle-O inside band, acc after re-orthogonalising, E) -> {band_mass}")
print(f" free params: full {r*(r-1)//2}, band3 {sum(min(3,r-1-i) for i in range(r))}")
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