def _samplehyper(self, mean_ln_yminob_minus_eta, var_ln_yminob_minus_eta):
Nsamples = self.burnin + self.mc_samples
L_d = np.ones(self.X_dim)
# define initial guess for hyperparameters
init = np.hstack((np.log(0.3*L_d), np.log(10.0), np.log(1e-2), mean_ln_yminob_minus_eta))
prior_mu = np.zeros(self.X_dim+3)
prior_cov = np.diag(1.0 * np.ones(self.X_dim+3))
prior_cov_chol = sp.linalg.cholesky(prior_cov, lower=True)
params_array = init
log_params = np.zeros((Nsamples, len(params_array)))
sampler_options = {"cur_log_like": None, "angle_range": 0}
extra_para = (mean_ln_yminob_minus_eta, var_ln_yminob_minus_eta)
for i in range(Nsamples):
params_array, current_ll = elliptical_slice(
params_array,
self._log_posterior,
prior_cov_chol,
prior_mu,
* extra_para,
**sampler_options)
log_params[i,:] = params_array.ravel()
current_ll = current_ll # for diagnostics QUESTION: what is this for?
self.params = np.exp(log_params[self.burnin:, :])
def _samplehyper(self, mean_ln_yminob_minus_eta, var_ln_yminob_minus_eta):
# Takes any samples and finds maximum
Nsamples = self.burnin + self.mc_samples + 200
L_d = np.ones(self.X_dim)
# define initial guess for hyperparameters
init = np.hstack((np.log(0.3 * L_d), np.log(10.0), np.log(1e-2),
mean_ln_yminob_minus_eta))
prior_mu = np.zeros(self.X_dim + 3)
prior_cov = np.diag(1.0 * np.ones(self.X_dim + 3))
prior_cov_chol = sp.linalg.cholesky(prior_cov, lower=True)
params_array = init
log_params = np.zeros((Nsamples, len(params_array)))
ll_record = np.zeros((Nsamples, 1))
sampler_options = {"cur_log_like": None, "angle_range": 0}
extra_para = (mean_ln_yminob_minus_eta, var_ln_yminob_minus_eta)
for i in range(Nsamples):
params_array, current_ll = elliptical_slice(
params_array, self._log_posterior, prior_cov_chol, prior_mu,
*extra_para, **sampler_options)
log_params[i, :] = params_array.ravel()
ll_record[i] = current_ll
self.params = np.exp(log_params[self.burnin:, :])
max_ll_index = np.argmax(ll_record) - self.burnin
self.params = np.atleast_2d(self.params[max_ll_index])
