def test_IndependentJoint_uniform_rejection():
# check that proposed samples are correctly rejected when using a
# IndependentJoint prior with some child distributions uniform. We used a
# Gaussian proposal to generate some samples that need to be rejected.
N = 1000
B1 = [-1.0, 1.0]
B2 = [-2.0, 2.0]
u1 = dd.Uniform(B1[0], B1[1])
u2 = dd.Uniform(B2[0], B2[1])
prior = dd.IndependentJoint([u1, u2])
m = [0., 0.]
S = [[
2.,
0.,
], [
0.,
2.,
]]
proposal = dd.Gaussian(m=m, S=S)
model = Gauss(dim=2)
s = ds.Identity()
g = dg.Default(model=model, prior=prior, summary=s)
g.proposal = proposal
params, stats = g.gen(N, verbose=False)
assert (params.min(axis=0) >= np.array([B1[0], B2[0]])).all() and \
(params.min(axis=0) <= np.array([B1[1], B2[1]])).all(), \
"rejection failed"
def test_IndependentJoint_eval():
N = 1000
B1 = [-1.0, 1.0]
B2 = [-2.0, 2.0]
u1 = dd.Uniform(B1[0], B1[1])
u2 = dd.Uniform(B2[0], B2[1])
dist = dd.IndependentJoint([u1, u2])
samples = dist.gen(N)
logprobs = dist.eval(samples, log=True)
lpdfval = -np.log((B2[1] - B2[0]) * (B1[1] - B1[0]))
assert np.isclose(logprobs, lpdfval).all()
assert samples.shape == (N, 2)
assert logprobs.shape == (N, )
def test_IndependentJoint_marginals():
N = 1000
m = np.array([1., 3., 0.])
S = [[8., 2., 1.], [2., 3., 2.], [1., 2., 3.]]
gs = [dd.Gaussian(m=m + i, S=S) for i in [-1, 0, 1]]
dist = dd.IndependentJoint(gs)
samples = dist.gen(N)
log_probs = dist.eval(samples, log=True)
jjs = [np.arange(3 * i, 3 * (i + 1)) for i in range(3)]
log_marginals = [dist.eval(samples[:, jj], ii=jj) for jj in jjs]
assert np.isclose(log_probs, np.vstack(log_marginals).sum(axis=0)).all()
log_submarginal_1 = dist.dists[0].eval(samples[:, [1]], ii=[1])
log_submarginal_4 = dist.dists[1].eval(samples[:, [4]], ii=[1])
log_submarginal_6_7_8 = dist.dists[2].eval(samples[:, [6, 7, 8]])
log_submarginal_1_4_6_7_8 = dist.eval(samples[:, [1, 4, 6, 7, 8]],
ii=[1, 4, 6, 7, 8])
assert np.isclose(
log_submarginal_1_4_6_7_8,
log_submarginal_1 + log_submarginal_4 + log_submarginal_6_7_8).all()