def test_rng_repeatability():
mu = np.atleast_1d([0.0])
S = np.atleast_2d(1.0)
# distributions
pG = dd.Gaussian(m=mu, S=S)
check_repeatability_dist(pG)
pMoG = dd.MoG(a=np.array([0.25, 0.75]), ms=[mu, mu], Ss=[S, S])
check_repeatability_dist(pMoG)
# simulators
mG = sims.Gauss()
check_repeatability_sim(mG, np.zeros(mG.dim_param).reshape(-1, 1))
mMoG = sims.GaussMixture()
check_repeatability_sim(mMoG, np.zeros(mMoG.dim_param).reshape(-1, 1))
# generators
g = gen.Default(model=mMoG, prior=pMoG, summary=Identity())
check_repeatability_gen(g)
# inference methods
# we're going to create each one with a different deepcopy of g to make
# sure thre are are no side effects e.g. changes to the proposal
x0 = g.gen(1, verbose=False)[1]
inf_opts = dict(obs=x0,
n_components=2,
n_hiddens=[5, 5],
verbose=False,
pilot_samples=0)
yB_nosvi = inf.Basic(deepcopy(g), svi=False, **inf_opts)
check_repeatability_infer(yB_nosvi)
yB_svi = inf.Basic(deepcopy(g), svi=True, **inf_opts)
check_repeatability_infer(yB_svi)
# skip CDELFI for now since it might crash if we don't use the prior
#yC = inf.CDELFI(deepcopy(g), **inf_opts)
#check_repeatability_infer(yC)
yS = inf.SNPE(deepcopy(g), prior_mixin=0.5, **inf_opts)
check_repeatability_infer(yS)
def test_basic_inference(n_params=2, seed=42):
m = Gauss(dim=n_params, seed=seed)
p = dd.Gaussian(m=np.zeros((n_params, )), S=np.eye(n_params), seed=seed)
s = ds.Identity()
g = dg.Default(model=m, prior=p, summary=s)
# set up inference
res = infer.Basic(g, seed=seed)
# run with N samples
out = res.run(1000)
# check result
posterior = res.predict(np.array([0., 0.]).reshape(1, -1))
assert np.allclose(posterior.xs[0].S,
np.array([[0.1, 0.0], [0.0, 0.1]]),
atol=0.05)
assert np.allclose(posterior.xs[0].m, np.array([0.0, 0.0]), atol=0.05)
def test_basic_inference_inputsamples(n_params=2, seed=42, n_pilot=1000):
model = Gauss(dim=n_params, seed=seed)
prior = dd.Gaussian(m=np.zeros((n_params, )),
S=np.eye(n_params),
seed=seed + 1)
s = ds.Identity(seed=seed + 2)
g = dg.Default(model=model, prior=prior, summary=s, seed=seed + 3)
obs = np.zeros((1, n_params)) # reseed generator etc. (?)
m_true, S_true = simplegaussprod(obs, model.noise_cov, prior.m, prior.S)
params, stats = g.gen(n_pilot)
pilot_samples = (params, stats)
res = infer.Basic(g, obs=obs, seed=seed + 4, pilot_samples=pilot_samples)
res.reset(seed=seed + 4)
out = res.run(n_train=1000)
posterior = res.predict(res.obs.reshape(1, -1))
check_gaussian_posterior(posterior, m_true, S_true)