distribution = Banana(dimension=8, bananicity=0.03, V=100)
sigma = GaussianKernel.get_sigma_median_heuristic(
distribution.sample(1000).samples)
sigma = 10
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
burnin = 20000
num_iterations = 40000
mcmc_sampler = KameleonWindowLearnScale(distribution,
kernel,
stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
cov_est[0, 0] = distribution.V
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = StandardMetropolis(distribution)
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
mcmc_chain = MCMCChain(mcmc_sampler, mcmc_params)
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiment = SingleChainExperiment(mcmc_chain, experiment_dir)
experiment.run()
cov_est=cov_est))
mcmc_samplers.append(
AdaptiveMetropolis(distribution,
mean_est=mean_est,
cov_est=cov_est))
#
# num_eigen = distribution.dimension
# mcmc_samplers.append(AdaptiveMetropolisPCA(distribution, num_eigen=num_eigen, mean_est=mean_est, cov_est=cov_est))
#
mcmc_samplers.append(StandardMetropolis(distribution))
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
mcmc_chains = [
MCMCChain(mcmc_sampler, mcmc_params)
for mcmc_sampler in mcmc_samplers
]
for mcmc_chain in mcmc_chains:
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiments = [
SingleChainExperiment(mcmc_chain, experiment_dir)
for mcmc_chain in mcmc_chains
]
for experiment in experiments:
ClusterTools.submit_experiment(experiment)
KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
if __name__ == '__main__':
experiment_dir = str(os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
distribution = Flower(amplitude=6, frequency=6, variance=1, radius=10, dimension=8)
sigma = 5
kernel = GaussianKernel(sigma=sigma)
burnin = 60000
num_iterations = 120000
#mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
mcmc_sampler = StandardMetropolis(distribution)
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start, num_iterations=num_iterations, burnin=burnin)
mcmc_chain = MCMCChain(mcmc_sampler, mcmc_params)
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiment = SingleChainExperiment(mcmc_chain, experiment_dir)
experiment.run()
print "using sigma", sigma
kernel = GaussianKernel(sigma=sigma)
for i in range(n):
mcmc_samplers = []
burnin=50000
num_iterations=500000
#mcmc_samplers.append(KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin))
#mean_est = zeros(distribution.dimension, dtype="float64")
#cov_est = 1.0 * eye(distribution.dimension)
#cov_est[0, 0] = distribution.V
#mcmc_samplers.append(AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est))
#mcmc_samplers.append(AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est))
mcmc_samplers.append(StandardMetropolis(distribution))
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start, num_iterations=num_iterations, burnin=burnin)
mcmc_chains = [MCMCChain(mcmc_sampler, mcmc_params) for mcmc_sampler in mcmc_samplers]
for mcmc_chain in mcmc_chains:
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiments = [SingleChainExperiment(mcmc_chain, experiment_dir) for mcmc_chain in mcmc_chains]
for experiment in experiments:
ClusterTools.submit_experiment(experiment)