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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', weights_only=False)
V = standardise(d['visual_field']); T = standardise(d['text_field']); N = len(V)
truth = np.arange(N)
def expand(anchorL, anchorR):
"""anchorL: vision indices; anchorR: claimed text partner. Returns full accuracy."""
probe = np.setdiff1d(np.arange(N), anchorL)
# probe scenes on the text side are whatever is not claimed
probeR = np.setdiff1d(np.arange(N), anchorR)
L = V[np.ix_(probe, anchorL)]; R = T[np.ix_(probeR, anchorR)]
L = (L - L.mean(1, keepdims=True)) / L.std(1, keepdims=True).clip(1e-9)
R = (R - R.mean(1, keepdims=True)) / R.std(1, keepdims=True).clip(1e-9)
S = L @ R.T / L.shape[1]
_, cols = linear_sum_assignment(-S)
resolved = probeR[cols]
n_ok = int((resolved == probe).sum()) + int((anchorR == anchorL).sum())
return n_ok / N
rng = np.random.default_rng(0)
REP = 20
print("=== (a) PURE random anchors: reproduce the published curve ===")
for K in (12, 25, 40, 50, 64, 128):
a = [expand(*(lambda s: (s, s))(np.sort(rng.choice(N, K, replace=False)))) for _ in range(REP)]
print(f" K={K:4d} pure accuracy {np.mean(a):.3f} +- {np.std(a):.3f}")
print("\n=== (b) IMPURE random anchors: c correct, K-c wrong (wrong = derangement among selected) ===")
for K in (12, 25, 40, 50, 100):
for prec in (1.0, 0.92, 0.88, 0.84, 0.80, 0.72, 0.60):
nb = int(round(K * (1 - prec)))
if nb == 1: nb = 2 # a single wrong pair is impossible inside a bijection
accs = []
for _ in range(REP):
sel = np.sort(rng.choice(N, K, replace=False))
right = sel.copy()
if nb >= 2:
bad = rng.choice(K, nb, replace=False)
sh = right[bad].copy()
while True:
perm = rng.permutation(nb)
if not (perm == np.arange(nb)).any(): break
right[bad] = sh[perm]
accs.append(expand(sel, right))
print(f" K={K:4d} prec={prec:.2f} ({K-nb}/{K} right) accuracy {np.mean(accs):.3f} +- {np.std(accs):.3f}")
print()
print("=== (c) IMPURE with wrong partners drawn from OUTSIDE the anchor set ===")
for K in (25, 50):
for prec in (1.0, 0.88, 0.84, 0.80, 0.72):
nb = int(round(K * (1 - prec))); accs = []
for _ in range(REP):
sel = np.sort(rng.choice(N, K, replace=False))
right = sel.copy()
if nb:
bad = rng.choice(K, nb, replace=False)
outside = np.setdiff1d(np.arange(N), sel)
right[bad] = rng.choice(outside, nb, replace=False)
accs.append(expand(sel, right))
print(f" K={K:4d} prec={prec:.2f} accuracy {np.mean(accs):.3f} +- {np.std(accs):.3f}")
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