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]