# possible to change
D = 2
N = 1000
# target is banana density, fallback to Gaussian if theano is not present
if banana_available:
target = Banana(D=D)
else:
target = IsotropicZeroMeanGaussian(D=D)
samplers = [
StandardMetropolis(target, D),
AdaptiveMetropolis(target, D),
KernelAdaptiveMetropolis(target, D, N=200)
]
for sampler in samplers:
# MCMC parameters, feel free to increase number of iterations
start = np.zeros(D)
num_iter = 1000
# run MCMC
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(sampler, start, num_iter, D)
visualise_trace(samples, log_pdf, accepted, step_sizes)
plt.suptitle("%s, acceptance rate: %.2f" % \
(sampler.__class__.__name__, np.mean(accepted)))
plt.show()
# kmc sampler instance
# note that this version still adapts the step size towards certain acceptance
# set schedule=None to avoid this; other schedules are possible
# the sqrt schedule is very conservative
schedule = standard_sqrt_schedule
acc_star = 0.7
kmc = KMCStatic(
surrogate,
target,
momentum,
num_steps_min,
num_steps_max,
step_size_min,
step_size_max,
adaptation_schedule=schedule,
acc_star=acc_star,
)
# MCMC parameters, feel free to increase number of iterations
start = X[0]
num_iter = 500
# run MCMC
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(kmc, start, num_iter, D)
visualise_trace(samples, log_pdf, accepted, log_pdf_density=surrogate, step_sizes=step_sizes)
plt.suptitle("%s, acceptance rate: %.2f" % (surrogate.__class__.__name__, np.mean(accepted)))
plt.show()
# the sqrt schedule is very conservative
schedule = standard_sqrt_schedule
acc_star = 0.7
kmc = KMCStatic(surrogate,
target,
momentum,
num_steps_min,
num_steps_max,
step_size_min,
step_size_max,
adaptation_schedule=schedule,
acc_star=acc_star)
# MCMC parameters, feel free to increase number of iterations
start = X[0]
num_iter = 500
# run MCMC
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(
kmc, start, num_iter, D)
visualise_trace(samples,
log_pdf,
accepted,
log_pdf_density=surrogate,
step_sizes=step_sizes)
plt.suptitle("%s, acceptance rate: %.2f" % \
(surrogate.__class__.__name__, np.mean(accepted)))
plt.show()
# possible to change
D = 2
N = 1000
# target is banana density, fallback to Gaussian if theano is not present
if banana_available:
target = Banana(D=D)
else:
target = IsotropicZeroMeanGaussian(D=D)
samplers = [
StandardMetropolis(target, D),
AdaptiveMetropolis(target, D),
KernelAdaptiveMetropolis(target, D, N=200)
]
for sampler in samplers:
# MCMC parameters, feel free to increase number of iterations
start = np.zeros(D)
num_iter = 1000
# run MCMC
samples, proposals, accepted, acc_prob, log_pdf, times, step_sizes = mini_mcmc(
sampler, start, num_iter, D)
visualise_trace(samples, log_pdf, accepted, step_sizes)
plt.suptitle("%s, acceptance rate: %.2f" % \
(sampler.__class__.__name__, np.mean(accepted)))
plt.show()
