(self, with_transform: bool)
| 95 | log_scale = 0.4 |
| 96 | |
| 97 | def __init__(self, with_transform: bool) -> None: |
| 98 | super().__init__() |
| 99 | |
| 100 | prior = tfp.distributions.Normal(to_default_float(1.0), to_default_float(1.0)) |
| 101 | |
| 102 | scale = np.exp(self.log_scale) |
| 103 | if with_transform: |
| 104 | transform = tfp.bijectors.Shift(to_default_float(0.0))( |
| 105 | tfp.bijectors.Scale(to_default_float(scale)) |
| 106 | ) |
| 107 | else: |
| 108 | transform = None |
| 109 | |
| 110 | self.theta = gpflow.Parameter(self.value, prior=prior, transform=transform) |
| 111 | |
| 112 | def maximum_log_likelihood_objective(self, *args: Any, **kwargs: Any) -> tf.Tensor: |
| 113 | assert not args |
nothing calls this directly
no test coverage detected