def __init__(self, *, mean_func=Zero(), cov_func=Constant(0.0), nu=None):
if nu is None:
raise ValueError(
"Student's T process requires a degrees of freedom parameter, 'nu'"
)
self.nu = nu
super().__init__(mean_func=mean_func, cov_func=cov_func)
def __init__(self, *, mean_func=Zero(), cov_funcs=(Constant(0.0))):
try:
self.cov_funcs = list(cov_funcs)
except TypeError:
self.cov_funcs = [cov_funcs]
cov_func = pm.gp.cov.Kron(self.cov_funcs)
super().__init__(mean_func=mean_func, cov_func=cov_func)
def __init__(self,
approx="VFE",
*,
mean_func=Zero(),
cov_func=Constant(0.0)):
if approx not in self._available_approx:
raise NotImplementedError(approx)
self.approx = approx
super().__init__(mean_func=mean_func, cov_func=cov_func)
def __init__(self,
approx="VFE",
*,
mean_func=Zero(),
cov_func=Constant(0.0)):
warnings.warn(
"gp.MarginalSparse has been renamed to gp.MarginalApprox.",
FutureWarning,
)
super().__init__(mean_func=mean_func, cov_func=cov_func, approx=approx)
def __init__(self, *, mean_func=Zero(), cov_func=Constant(0.0)):
self.mean_func = mean_func
self.cov_func = cov_func
def __init__(self, *, mean_func=Zero(), cov_func=Constant(0.0)):
super().__init__(mean_func=mean_func, cov_func=cov_func)