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)