def nn_model_maker(network): s, b, db = hm(network) m = models.hepdata_like(s, b, db) nompars = m.config.suggested_init bonlypars = jax.numpy.asarray([x for x in nompars]) bonlypars = jax.ops.index_update(bonlypars, m.config.poi_index, 0.0) return m, bonlypars
def nn_model_maker(nn_params): s, b, db = nn_params m = models.hepdata_like([s], [b], [db]) nompars = m.config.suggested_init() bonlypars = jax.numpy.asarray([x for x in nompars]) bonlypars = jax.ops.index_update(bonlypars, m.config.poi_index, 0.0) return m, bonlypars
def nll_infspace(pars): truth_pars = [0, 1] pars = transforms.to_bounded_vec(pars, bounds) m = models.hepdata_like([s], [b], [db]) val = m.logpdf(pars, m.expected_data(truth_pars)) return -val[0]
def nn_model_maker(network): s, b, db = hm(network) # print(f's={s}, b={b}, db={db}') # m = pyhf.simplemodels.hepdata_like(s, b, db) # pyhf model m = models.hepdata_like(s, b, db) # neos model nompars = m.config.suggested_init() bonlypars = jax.numpy.asarray([x for x in nompars]) bonlypars = jax.ops.index_update(bonlypars, m.config.poi_index, 0.0) return m, bonlypars
def nll_boundspace(pars): truth_pars = [0, 1] m = models.hepdata_like([s], [b], [db]) val = m.logpdf(pars, m.expected_data(truth_pars)) return -val[0]