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