cpp_gp_loglikelihood = cppGaussianProcessLogLikelihoodMCMC(
historical_data=init_data,
derivatives=derivatives,
prior=prior,
chain_length=1000,
burnin_steps=2000,
n_hypers=2 ** 4,
noisy=noisy,
)
cpp_gp_loglikelihood.train()
py_sgd_params_ps = pyGradientDescentParameters(
max_num_steps=1000,
max_num_restarts=3,
num_steps_averaged=15,
gamma=0.7,
pre_mult=1.0,
max_relative_change=0.02,
tolerance=1.0e-10,
)
cpp_sgd_params_ps = cppGradientDescentParameters(
num_multistarts=1,
max_num_steps=6,
max_num_restarts=1,
num_steps_averaged=3,
gamma=0.0,
pre_mult=1.0,
max_relative_change=0.1,
tolerance=1.0e-10,
)
# noisy = False means the underlying function being optimized is noise-free
cpp_gp_loglikelihood = cppGaussianProcessLogLikelihoodMCMC(
historical_data=init_data,
derivatives=derivatives,
prior=prior,
chain_length=1000,
burnin_steps=2000,
n_hypers=2**4,
noisy=True)
cpp_gp_loglikelihood.train()
py_sgd_params_ps = pyGradientDescentParameters(max_num_steps=100,
max_num_restarts=3,
num_steps_averaged=15,
gamma=0.7,
pre_mult=0.01,
max_relative_change=0.02,
tolerance=1.0e-8)
py_sgd_params_acquisition = pyGradientDescentParameters(
max_num_steps=50,
max_num_restarts=1,
num_steps_averaged=0,
gamma=0.7,
pre_mult=1.0,
max_relative_change=0.1,
tolerance=1.0e-8)
cpp_sgd_params_ps = cppGradientDescentParameters(num_multistarts=1,
max_num_steps=6,
