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())