D=D,
bananicity=bananicity,
V=V,
num_benchmark_samples=num_benchmark_samples,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
mmd=mmd,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
time_taken=time_taken,
)
if False:
import matplotlib.pyplot as plt
visualize_scatter_2d(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, OracleKernelAdaptiveLangevin):
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(sampler.surrogate, samples, Xs, Ys)
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
plt.title("Drift norms %s" % sampler.get_name())
plt.hist(sampler.forward_drift_norms)
plt.show()
D = mdl.dim
pop_size=50
target_log_pdf = mdl.get_logpdf_closure()
samplers = [
# get_StaticMetropolis_instance(D, target_log_pdf),
# get_AdaptiveMetropolis_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
# get_Kameleon_instance(D, target_log_pdf),
# get_StaticLangevin_instance(D, target_log_pdf, target_grad),
# get_AM_5(D, target_log_pdf),
get_AM_1(D, target_log_pdf),
# get_AM_2(D, target_log_pdf),
# get_AM_05(D, target_log_pdf),
]
for sampler in samplers:
print(sampler.__class__)
start = mdl.rvs(pop_size)
num_iter = 5000
samples, log_target_densities, times = mini_pmc(sampler, start, num_iter, pop_size)
mom_samp = np.array([(samples**i).mean(0) for i in range(1,4)])
visualize_scatter_2d(samples[:,:2])
Log.get_logger().info('===='+str(sampler.step_size)+'====')
plt.show()
sampler_name=sampler.get_name(),
D=D,
bananicity=bananicity,
V=V,
num_benchmark_samples=num_benchmark_samples,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
mmd=mmd,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
time_taken=time_taken,
)
if False:
import matplotlib.pyplot as plt
visualize_scatter_2d(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, OracleKernelAdaptiveLangevin):
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(sampler.surrogate, samples, Xs, Ys)
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
plt.title("Drift norms %s" % sampler.get_name())
plt.hist(sampler.forward_drift_norms)
plt.show()
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
get_StaticMetropolis_instance(D, target_log_pdf),
get_AdaptiveMetropolis_instance(D, target_log_pdf),
get_OracleKameleon_instance(D, target_log_pdf),
get_Kameleon_instance(D, target_log_pdf),
get_StaticLangevin_instance(D, target_log_pdf, target_grad),
get_AdaptiveLangevin_instance(D, target_log_pdf, target_grad),
get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf, target_grad),
get_KernelAdaptiveLangevin_instance(D, target_log_pdf, target_grad),
]
# SMC sampler
for sampler in samplers:
start = np.zeros(D)
num_population = 200
num_samples = num_population
bridge_start = mvnorm(np.zeros(D), np.eye(D) * 10)
samples, log_target_densities, step_sizes, acceptance_rates, evid = mini_smc(num_samples,
num_population,
bridge_start,
target_log_pdf,
sampler)
visualize_scatter_2d(samples, step_sizes, acceptance_rates)
plt.suptitle("%s" % (sampler.__class__.__name__))
plt.show()
D = mdl.dim
pop_size = 50
target_log_pdf = mdl.get_logpdf_closure()
samplers = [
# get_StaticMetropolis_instance(D, target_log_pdf),
# get_AdaptiveMetropolis_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
# get_Kameleon_instance(D, target_log_pdf),
# get_StaticLangevin_instance(D, target_log_pdf, target_grad),
# get_AM_5(D, target_log_pdf),
get_AM_1(D, target_log_pdf),
# get_AM_2(D, target_log_pdf),
# get_AM_05(D, target_log_pdf),
]
for sampler in samplers:
print(sampler.__class__)
start = mdl.rvs(pop_size)
num_iter = 5000
samples, log_target_densities, times = mini_pmc(
sampler, start, num_iter, pop_size)
mom_samp = np.array([(samples**i).mean(0) for i in range(1, 4)])
visualize_scatter_2d(samples[:, :2])
Log.get_logger().info('====' + str(sampler.step_size) + '====')
plt.show()
