import adaptive_context_experiment as a
for scale in [1.0, 2.0, 3.0]:
    u,y,tr=a.make_data(5000,24,19)
    tr=tr*scale
    # regenerate labels for scaled truth while retaining same inputs
    import numpy as np
    rng=np.random.default_rng(123)
    H=24; yy=np.array([rng.random()<a.sigmoid(np.dot(tr[t],u[H+t-np.arange(1,H+1)])) for t in range(5000)],dtype=np.int8)
    print('scale',scale)
    for lam in [.012,.05,.15,.4,1.0]:
      z=a.run_model(u,yy,tr,'adaptive',24,lam=lam,k=.08)
      print(lam, round(z['cum_logloss'],1), round(z['mean_active_h'],2), round(z['inference_ops_relative'],3))