| 2 | | * LINQ is used a lot in combination with matrix operations. This is often slow because of memory allocations required for enumerators. (DONE) |
| 3 | | * There should be an option to scale the input variables (scaling should be active by default) (DONE) |
| 4 | | * RBF regression does not support noise. If there are duplicate x vectors, model building fails. An option would be to add a diagonal matrix to the gram matrix (leading to kernel ridge regression?) (DONE) |
| 5 | | * I have not found a source for the calculation of variance and LOO error (DONE, removed LOO calculation) |
| 6 | | * Don't know how to best unify covariance functions and kernel functions (there is some duplication) (DONE). |
| 7 | | * The calculation of the covariance matrix takes a lot of time (10x longer than the equivalent calculation when using an equivalent covariance matrix). I suspect that the reason is the rather general implementation for distance calculation. (DONE) |
| 8 | | * Beta should be a parameter of the algorithm instead of the kernel to make it easier to run a grid test. (DONE) |
| 9 | | * Multiple of the implemented kernels are only conditionally positive definite. See http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf for a definition of the kernels and valid beta-values. Additionally, it is necessary to extend the basis functions for these kernels depending on the value of beta. |
| | 2 | * ~~LINQ is used a lot in combination with matrix operations. This is often slow because of memory allocations required for enumerators.~~ (DONE) |
| | 3 | * ~~There should be an option to scale the input variables (scaling should be active by default)~~ (DONE) |
| | 4 | * ~~RBF regression does not support noise. If there are duplicate x vectors, model building fails. An option would be to add a diagonal matrix to the gram matrix (leading to kernel ridge regression?)~~ (DONE) |
| | 5 | * ~~I have not found a source for the calculation of variance and LOO error~~ (DONE, fixed LOO calculation based on GPML book) |
| | 6 | * ~~Don't know how to best unify covariance functions and kernel functions (there is some duplication)~~ (DONE, only use RBF 'kernels' with a beta parameter). |
| | 7 | * ~~The calculation of the covariance matrix takes a lot of time (10x longer than the equivalent calculation when using an equivalent covariance matrix). I suspect that the reason is the rather general implementation for distance calculation.~~ (DONE) |
| | 8 | * ~~Beta should be a parameter of the algorithm instead of the kernel to make it easier to run a grid test.~~ (DONE) |
| | 9 | * ~~Multiple of the implemented kernels are only conditionally positive definite. See http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf for a definition of the kernels and valid beta-values. Additionally, it is necessary to extend the basis functions for these kernels depending on the value of beta.~~ (Handled, by trying to use Cholesky decompose first and if it fails trying to calculate the inverse via LU decomposition). |
