from pathlib import Path
import json
import numpy as np
from infer import predict_linearised,predict_response
R=Path(__file__).resolve().parent
fits=json.loads((R/'fits.json').read_text());assert len(fits)==512
u=np.asarray([[0.,0.],[.25,-.5],[-.125,.75]])
for fit in fits:
    a=np.asarray(fit['A']);b=np.asarray(fit['b']);reference=u@a.T+b
    np.testing.assert_allclose(predict_linearised(fit,u),reference,rtol=0,atol=1e-12)
    want=np.tanh(reference) if fit['link_mode']=='tanh' else reference
    np.testing.assert_allclose(predict_response(fit,u),want,rtol=0,atol=1e-12)
for bad in [np.zeros((1,3)),np.full((1,2),np.nan)]:
    try:predict_response(fits[0],bad)
    except ValueError:pass
    else:raise AssertionError('invalid input accepted')
print(json.dumps({'status':'PASS','fits':len(fits),'interventions_per_fit':len(u),'scope':'saved coefficients; synthetic coordinate test, NOT new subject validation'},indent=2))