def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
instance = StaticMetropolis(D, target_log_pdf, step_size)
# oracle scaling
instance.L_C = np.linalg.cholesky(true_cov)
return instance
def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
instance = StaticMetropolis(D, target_log_pdf, step_size)
# oracle scaling
instance.L_C = np.linalg.cholesky(true_cov)
return instance
def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
acc_star = None
schedule = None
instance = StaticMetropolis(D, target_log_pdf, step_size, schedule, acc_star)
# give proposal variance a meaningful shape from previous samples
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname, benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0, len(benchmark_samples), step=50)]
instance.L_C = np.linalg.cholesky(np.cov(benchmark_samples.T))
return instance
def get_StaticMetropolis_instance(D, target_log_pdf):
step_size = 0.002
acc_star = None
schedule = None
instance = StaticMetropolis(D, target_log_pdf, step_size, schedule,
acc_star)
# give proposal variance a meaningful shape from previous samples
benchmark_samples_fname = "pmc_sv_benchmark_samples.txt"
benchmark_samples_sha1 = "d53e505730c41fbe413188530916d9a402e21a87"
assert_file_has_sha1sum(benchmark_samples_fname,
benchmark_samples_sha1)
benchmark_samples = np.loadtxt(benchmark_samples_fname)
benchmark_samples = benchmark_samples[np.arange(0,
len(benchmark_samples),
step=50)]
instance.L_C = np.linalg.cholesky(np.cov(benchmark_samples.T))
return instance