from variational_sampler import VariationalSampler from variational_sampler.gaussian import Gaussian from variational_sampler.toy_dist import ExponentialPowerLaw from variational_sampler.display import display_fit DIM = 1 NPTS = 100 DM = 2 target = ExponentialPowerLaw(beta=1, dim=DIM) vs = VariationalSampler(target, (DM + target.m, target.V), NPTS) f = vs.fit().fit fl = vs.fit('l').fit context = Gaussian(DM + target.m, 2 * target.V) target2 = lambda x: target(x) + context.log(x) vs2 = VariationalSampler(target2, context, NPTS) f2 = vs2.fit().fit / context fl2 = vs2.fit('l').fit / context if DIM == 1: display_fit(vs.x, target, (f, f2, fl, fl2), ('blue', 'green', 'orange', 'red'), ('VS', 'VSc', 'IS', 'ISc')) gopt = Gaussian(target.m, target.V, Z=target.Z) print('Error for VS: %f' % gopt.kl_div(f)) print('Error for VSc: %f' % gopt.kl_div(f2)) print('Error for IS: %f' % gopt.kl_div(fl)) print('Error for ISc: %f' % gopt.kl_div(fl2))