start_time = time.time()
samples, log_target_densities, times = mini_pmc(sampler, start, num_iter, population_size)
time_taken = time.time() - start_time
mmd = mmd_to_benchmark_sample(samples, benchmark_sample, degree=3)
rmse_mean = np.mean((true_mean - np.mean(samples, 0)) ** 2)
rmse_cov = np.mean((true_cov - np.cov(samples.T)) ** 2)
logger.info("Storing results under %s" % result_fname)
store_results(result_fname,
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)
start_time = time.time()
samples, log_target_densities, times = mini_pmc(sampler, start, num_iter, population_size)
time_taken = time.time() - start_time
rmse_mean = np.mean((true_mean - np.mean(samples, 0)) ** 2)
rmse_cov = np.mean((true_cov - np.cov(samples.T)) ** 2)
mmd = mmd_to_benchmark_sample(samples, benchmark_samples, degree=3)
logger.info("Storing results under %s" % result_fname)
store_results(result_fname,
sampler_name=sampler.get_name(),
D=D,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
num_initial_oracle=num_initial_oracle,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
mmd=mmd,
time_taken=time_taken,
**sampler.get_parameters()
)
if False:
import matplotlib.pyplot as plt
visualise_pairwise_marginals(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
time_taken = time.time() - start_time
mmd = mmd_to_benchmark_sample(samples,
benchmark_sample,
degree=3)
rmse_mean = np.mean((true_mean - np.mean(samples, 0))**2)
rmse_cov = np.mean((true_cov - np.cov(samples.T))**2)
logger.info("Storing results under %s" % result_fname)
store_results(
result_fname,
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)
sampler, start, num_iter, population_size)
time_taken = time.time() - start_time
rmse_mean = np.mean((true_mean - np.mean(samples, 0))**2)
rmse_cov = np.mean((true_cov - np.cov(samples.T))**2)
mmd = mmd_to_benchmark_sample(samples,
benchmark_samples,
degree=3)
logger.info("Storing results under %s" % result_fname)
store_results(result_fname,
sampler_name=sampler.get_name(),
D=D,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
num_initial_oracle=num_initial_oracle,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
mmd=mmd,
time_taken=time_taken,
**sampler.get_parameters())
if False:
import matplotlib.pyplot as plt
visualise_pairwise_marginals(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
plt.title("Drift norms %s" % sampler.get_name())
