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Kernel compute #120
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Kernel compute #120
Commits on Sep 23, 2022
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Minimal Kronecker and more (read comment!!).
- Minimal Kronecker kernel. The inverse is highly inefficient - this can be improved! - Added white noise kernel. - Added diagonal computation abstraction. - Added minimal computation abstraction for kernel combinations (i.e., for * and +). Main issues: - Though we can replace the computations in the gps.py, we might miss out with caching stuff e.g., the Cholesky decomposition in the dense computation case. Is there a sensible way around this? - As a further point to the preceding point, we should not be computing the gram matrix in general (yes I known we need to in the dense setting). - Think about computation savings for KL divergences - how can we resolve these? Minor issues: - Need to discuss the best way to add some computation methods - it might be tidier than just adding a load of static methods altogether. - Need tests.
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Commits on Oct 10, 2022
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Remove tf.LinearOperator and add custom CovarianceOperator
Need to add/update tests/ check code is correct.
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Commits on Oct 12, 2022
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Commits on Oct 20, 2022
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Need to write complete tests for covariance operators, then can get on with revamping the rest of the codebase and tests for other modules.
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Initial covariance operator tests.
Missing matmul, and need to improve the solve tests to ensure there are now issues with shapes.
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Commits on Oct 22, 2022
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Commits on Oct 23, 2022
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Commits on Oct 31, 2022
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Commits on Nov 3, 2022
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