def compute_log_likelihood(self, hyps):
r"""Compute the objective_type measure at the specified hyperparameters.
:return: value of log_likelihood evaluated at hyperparameters (``LL(y | X, \theta)``)
:rtype: float64
"""
# Bound the hyperparameter space to keep things sane. Note all
# hyperparameters live on a log scale
if numpy.any((-20 > hyps) + (hyps > 20)):
return -numpy.inf
if not self.noisy:
hyps[(self.dim+1):] = numpy.log((1+self._num_derivatives)*[1.e-8])
posterior = 1
if self.prior is not None:
posterior = self.prior.lnprob(hyps)
hyps = numpy.exp(hyps)
cov_hyps = hyps[:(self.dim+1)]
noise = hyps[(self.dim+1):]
if posterior == -numpy.inf:
return -numpy.inf
else:
return posterior + C_GP.compute_log_likelihood(
cpp_utils.cppify(self._points_sampled),
cpp_utils.cppify(self._points_sampled_value),
self.dim,
self._num_sampled,
self.objective_type,
cpp_utils.cppify_hyperparameters(cov_hyps),
cpp_utils.cppify(self._derivatives), self._num_derivatives,
cpp_utils.cppify(noise),
)
def compute_log_likelihood(self):
r"""Compute the objective_type measure at the specified hyperparameters.
:return: value of log_likelihood evaluated at hyperparameters (``LL(y | X, \theta)``)
:rtype: float64
"""
return C_GP.compute_log_likelihood(
cpp_utils.cppify(self._points_sampled),
cpp_utils.cppify(self._points_sampled_value),
self.dim,
self._num_sampled,
self.objective_type,
cpp_utils.cppify_hyperparameters(self.hyperparameters),
cpp_utils.cppify(self._points_sampled_noise_variance),
)
def compute_log_likelihood(self):
r"""Compute the objective_type measure at the specified hyperparameters.
:return: value of log_likelihood evaluated at hyperparameters (``LL(y | X, \theta)``)
:rtype: float64
"""
return C_GP.compute_log_likelihood(
cpp_utils.cppify(self._points_sampled),
cpp_utils.cppify(self._points_sampled_value),
self.dim,
self._num_sampled,
self.objective_type,
cpp_utils.cppify_hyperparameters(self.hyperparameters),
cpp_utils.cppify(self._points_sampled_noise_variance),
)