Covariance matrix
The object holds and processes information about the covariance \(\left(\Sigma\right)_{i j} = k(\mathbf{x}_i, \mathbf{x}_j)\) between all data points \(\mathbf{x}_1, \ldots, \mathbf{x}_N\). One important purpose is to determine the Cholesky decomposition of the covariance matrix, \(\Sigma=L L^T\) which is used to make predictions. The decomposition takes usually \(\mathcal{O}(N^3)\) steps. By using the Cholesky decomposition of a previous iteration, an iterative update can be computed in only \(\mathcal{O}(N^2)\) steps.
resize_step (int)
Step size at which the matrices for doing iterative updates are enlarged.
Default:
1000
iterative_start_size (int)
Size of covariance matrix at which the Cholesky decomposition is determined iteratively.
Default:
500
cuda_start_size (int)
Size of covariance matrix at which computations are performed on GPU instead of CPU, if available.
Default:
2000
num_iterative_solver_steps (int)
Gaussian process regression requires to solve systems of linear equations. In order to improve the accuracy of direct solution methods, an iterative refinement can be performed with the given number of iterations.
Default:
0
online_start_size (int)
Size of covariance matrix at which computations are switched from offline to online.
Online: \(L^{-1} \mathbf{x}\) is computed using forward substitution for each \(\mathbf{x}\).
Offline: \(L^{-1}\) is computed explicitly.
Default:
500
max_data_hyper_derivs (bool)
Maximum number of observations for which hyperparameter derivatives are determined. A smaller number limits the memory consumption but limits the possibility to perform hyperparameter optimizations. This number should be always larger or equal to
optimization_step_maxof the corresponding Gaussian process.Default:
10000