Initialize the kernel. Args: input_dim: Input dimension of the training data. covariance_matrix: The fixed covariance matrix. active_dims: Active dimensions. name: Name of the kernel.
(
self,
input_dim: int,
covariance_matrix: np.ndarray,
active_dims: List[int] = None,
name="PosteriorCov",
)
| 87 | """ |
| 88 | |
| 89 | def __init__( |
| 90 | self, |
| 91 | input_dim: int, |
| 92 | covariance_matrix: np.ndarray, |
| 93 | active_dims: List[int] = None, |
| 94 | name="PosteriorCov", |
| 95 | ): |
| 96 | """Initialize the kernel. |
| 97 | |
| 98 | Args: |
| 99 | input_dim: Input dimension of the training data. |
| 100 | covariance_matrix: The fixed covariance matrix. |
| 101 | active_dims: Active dimensions. |
| 102 | name: Name of the kernel. |
| 103 | """ |
| 104 | super(FixedKernel, self).__init__( |
| 105 | input_dim=input_dim, |
| 106 | variance=1.0, |
| 107 | covariance_matrix=covariance_matrix, |
| 108 | active_dims=active_dims, |
| 109 | name=name, |
| 110 | ) |
| 111 | self.variance.fix() |
| 112 | |
| 113 | def to_dict(self) -> dict: |
| 114 | """Save the kernel as a dictionary.""" |