def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate,
step_size)
return instance
def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size)
return instance
def get_KernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=1., m=m, D=D)
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
instance = KernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size)
return instance
def get_KernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=1., m=m, D=D)
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
instance = KernelAdaptiveLangevin(D, target_log_pdf, surrogate,
step_size)
return instance