num_warmup, thin_step) # marginal sampler job.recompute_log_pdf = True job.walltime = 60 * 60 # store results in home dir straight away d = os.sep.join(os.path.abspath(__file__).split(os.sep)[:-1]) + os.sep job.aggregator = MCMCJobResultAggregatorStoreHome(d) return job if __name__ == "__main__": logger.setLevel(10) num_repetitions = 10 # plain MCMC parameters, plan is to use every 200th sample thin_step = 1 num_iterations = 5200 num_warmup = 200 compute_local = False if not FileSystem.cmd_exists("sbatch") or compute_local: engine = SerialComputationEngine() else: johns_slurm_hack = "#SBATCH --partition=intel-ivy,wrkstn,compute" folder = os.sep + os.sep.join(["nfs", "data3", "ucabhst", modulename])
import os from kmc.tools.Log import logger import numpy as np from scripts.experiments.trajectories.independent_jobs_classes.random_feats.StudentTrajectoryJob import StudentTrajectoryJob from scripts.experiments.trajectories.tools import process modulename = __file__.split(os.sep)[-1].split('.')[-2] if __name__ == "__main__": logger.setLevel(20) nu_q = 1. sigma_p = 1. Ds = np.sort(2**np.arange(8))[::-1] Ns = np.sort( [50, 100, 200, 500, 1000, 2000, 3000, 4000, 5000, 10000, 20000])[::-1] print(Ns) print(Ds) num_repetitions = 10 num_steps = 100 max_steps = 1000 step_size = .1 scale0 = 0.5 lmbda0 = 0.00008 job_generator = lambda D, N, m: StudentTrajectoryJob(N, D, m, nu_q,