def test_glass_posterior_log_pdf_execute():
if not glass_available:
raise SkipTest("Shogun not available")
D = 9
theta = np.random.randn(D)
target = GlassPosterior()
target.set_up()
target.log_pdf(theta)
def test_glass_posterior_log_pdf_execute():
if not glass_available:
raise SkipTest("Shogun not available")
D = 9
theta = np.random.randn(D)
target = GlassPosterior()
target.set_up()
target.log_pdf(theta)
def test_glass_posterior_setup_execute():
if not glass_available:
raise SkipTest("Shogun not available")
GlassPosterior().set_up()
adaptation_schedule=schedule, acc_star=acc_star)
if __name__ == '__main__':
"""
This example samples from the marginal posterior over hyper-parameters of a
Gaussian Process classification model.
All samplers in the paper are used.
Note this is an illustrative demo and the number of iterations are set very low.
"""
# Glass posterior has 9 dimensions
D = 9
if glass_available:
target = GlassPosterior()
target.set_up()
else:
target = IsotropicZeroMeanGaussian(D=D)
# transition kernel, pick any
samplers = [
get_am_instance(target),
get_mh_instance(target),
get_kam_instance(target),
get_kmc_instance(target)
]
for sampler in samplers:
# MCMC parameters