def test_mmh_2d_joint_proposal():
target = Distributions.MultivariateNormal([0., 0.]).pdf
proposal = Distributions.JointIndependent(marginals=[Distributions.Normal(scale=0.2),
Distributions.Normal(scale=0.2)])
x = ModifiedMetropolisHastings(dimension=2, pdf_target=target, n_chains=1, proposal=proposal, random_state=123,
nsamples=10)
assert [round(float(x.samples[-1][0]), 3), round(float(x.samples[-1][1]), 3)] == [-0.783, -0.195]
def test_unconcatenate_chains_mcmc():
target = Distributions.Normal().pdf
x = ModifiedMetropolisHastings(dimension=1, pdf_target=target, burn_length=10, jump=2, n_chains=2,
save_log_pdf=True, random_state=123)
x.run(nsamples=5)
x.run(nsamples=5)
assert (round(float(x.samples[-1]), 3) == -0.744)
def test_mmh_1d_burn_jump():
target = Distributions.Normal().pdf
x = ModifiedMetropolisHastings(dimension=1, pdf_target=target, burn_length=10,
jump=2, n_chains=1, random_state=123, nsamples=10)
assert round(float(x.samples[-1]), 3) == 0.497
def test_mh_1d_asymmetric_proposal_pdf():
target = Distributions.Normal().pdf
proposal = Distributions.Normal()
x = MetropolisHastings(dimension=1, pdf_target=target, proposal=proposal, proposal_is_symmetric=False,
n_chains=1, random_state=123, nsamples=10)
assert round(float(x.samples[-1]), 3) == -1.291
def test_mh_1d_target_pdf():
target = Distributions.Normal().pdf
x = MetropolisHastings(dimension=1, pdf_target=target, n_chains=1, random_state=123, nsamples=10)
assert round(float(x.samples[-1]), 3) == -1.291
def test_mh_1d_acceptance_rate():
target = Distributions.Normal().pdf
x = MetropolisHastings(dimension=1, pdf_target=target, n_chains=1, random_state=123, nsamples=100)
assert round(float(x.acceptance_rate[0]), 3) == 0.707
def test_stretch_1d_burn_jump():
target = Distributions.Normal().pdf
x = Stretch(pdf_target=target, burn_length=10, jump=2, dimension=1, n_chains=2, random_state=123,
nsamples=10)
assert round(float(x.samples[-1]), 3) == -0.961
def test_dream_1d_adapt_chains():
target = Distributions.Normal().pdf
x = DREAM(pdf_target=target, burn_length=1000, jump=2, save_log_pdf=True, dimension=1,
crossover_adaptation=(1000, 1), n_chains=20, random_state=123, nsamples=2000)
assert (round(float(x.samples[-1]), 3) == -0.446)
def test_dream_1d_burn_jump():
target = Distributions.Normal().pdf
x = DREAM(pdf_target=target, burn_length=10, jump=2, dimension=1, n_chains=10, random_state=123,
nsamples=20)
assert round(float(x.samples[-1]), 3) == 0.0
def test_mmh_2d_list_proposal_log_target():
target = [Distributions.Normal().log_pdf, Distributions.Normal().log_pdf]
proposal = [Distributions.Normal(scale=0.2), Distributions.Normal(scale=0.2)]
x = ModifiedMetropolisHastings(dimension=2, log_pdf_target=target, n_chains=1, proposal=proposal,
random_state=123, nsamples=10)
assert [round(float(x.samples[-1][0]), 3), round(float(x.samples[-1][1]), 3)] == [-0.783, -0.195]
def test_mh_1d_samples_per_chain():
target = Distributions.Normal().pdf
x = MetropolisHastings(dimension=1, pdf_target=target, n_chains=2, random_state=123,
nsamples_per_chain=5)
assert round(float(x.samples[-1]), 3) == 0.474
def test_mmh_2d_list_target_log_pdf():
target = [Distributions.Normal().log_pdf, Distributions.Normal().log_pdf]
x = ModifiedMetropolisHastings(dimension=2, log_pdf_target=target, n_chains=1, random_state=123,
nsamples=10)
assert [round(float(x.samples[-1][0]), 3), round(float(x.samples[-1][1]), 3)] == [-0.810, 0.173]