population_sizes = [5, 10, 25, 50, 100]
num_repetitions = 30
num_benchmark_samples = 1000
rng_state = np.random.get_state()
np.random.seed(0)
benchmark_sample = sample_banana(num_benchmark_samples, D, bananicity, V)
np.random.set_state(rng_state)
for _ in range(num_repetitions):
for population_size in population_sizes:
num_iter = population_size * num_iter_per_particle
start = np.zeros(D)
target_log_pdf = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=False)
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_AdaptiveIndependentMetropolis_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),
]
for sampler in samplers:
start_time = time.time()
samples, log_target_densities, times = mini_pmc(sampler, start, num_iter, population_size)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
max_moment = 8
D = 10
pop_size = 10
bananicity = 0.03
V = 100
Z = sample_banana(3000, D, bananicity, V)
moments = np.array([(Z**i).mean(0) for i in range(1, max_moment)])
pk = PolynomialKernel(3)
print(moments)
true_correct = np.linalg.inv(np.cov(Z.T))
target_log_pdf = lambda x: log_banana_pdf(
x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
#
]
sampler_is = get_StaticLangevin(
D, target_log_pdf,
target_grad) #get_AdaptiveLangevin(D, target_log_pdf, target_grad)
sampler_mh = get_StaticLangevin(
D, target_log_pdf, target_grad
) #get_AdaptiveLangevin(D, target_log_pdf, target_grad, prec=True, step_size=1.)
start = np.zeros(D)
num_iter = 100