def main():
Log.set_loglevel(logging.DEBUG)
prior = Gaussian(Sigma=eye(2) * 100)
num_estimates = 1000
home = expanduser("~")
folder = os.sep.join([home, "sample_ozone_posterior_rr_sge"])
# cluster admin set project jump for me to exclusively allocate nodes
parameter_prefix = "" # #$ -P jump"
cluster_parameters = BatchClusterParameters(
foldername=folder,
memory=7.8,
loglevel=logging.DEBUG,
parameter_prefix=parameter_prefix,
max_walltime=60 * 60 * 24 - 1)
computation_engine = SGEComputationEngine(cluster_parameters,
check_interval=10)
rr_instance = RussianRoulette(1e-3, block_size=400)
posterior = OzonePosteriorRREngine(rr_instance=rr_instance,
computation_engine=computation_engine,
num_estimates=num_estimates,
prior=prior)
posterior.logdet_method = "shogun_estimate"
proposal_cov = diag([4.000000000000000e-05, 1.072091680000000e+02])
mcmc_sampler = StandardMetropolis(posterior, scale=1.0, cov=proposal_cov)
start = asarray([-11.55, -10.1])
mcmc_params = MCMCParams(start=start, num_iterations=5000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(None, plot_from=1, lag=1))
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
store_chain_output = StoreChainOutput(folder, lag=1)
chain.append_mcmc_output(store_chain_output)
loaded = store_chain_output.load_last_stored_chain()
if loaded is None:
logging.info("Running chain from scratch")
else:
logging.info("Running chain from iteration %d" % loaded.iteration)
chain = loaded
chain.run()
f = open(folder + os.sep + "final_chain", "w")
dump(chain, f)
f.close()
def main():
Log.set_loglevel(logging.DEBUG)
prior = Gaussian(Sigma=eye(2) * 100)
num_estimates = 2
home = expanduser("~")
folder = os.sep.join([home, "sample_ozone_posterior_rr_sge"])
computation_engine = SerialComputationEngine()
rr_instance = RussianRoulette(1e-3, block_size=10)
posterior = OzonePosteriorRREngine(rr_instance=rr_instance,
computation_engine=computation_engine,
num_estimates=num_estimates,
prior=prior)
posterior.logdet_method = "shogun_estimate"
proposal_cov = diag([4.000000000000000e-05, 1.072091680000000e+02])
mcmc_sampler = StandardMetropolis(posterior, scale=1.0, cov=proposal_cov)
start = asarray([-11.35, -13.1])
mcmc_params = MCMCParams(start=start, num_iterations=200)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(None, plot_from=1, lag=1))
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
store_chain_output = StoreChainOutput(folder, lag=50)
chain.append_mcmc_output(store_chain_output)
loaded = store_chain_output.load_last_stored_chain()
if loaded is None:
logging.info("Running chain from scratch")
else:
logging.info("Running chain from iteration %d" % loaded.iteration)
chain = loaded
chain.run()
f = open(folder + os.sep + "final_chain", "w")
dump(chain, f)
f.close()
