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However, during training I get some numerical instabilities "NotPSDError: Matrix not positive definite after repeatedly adding jitter up to 1.0e-04." and even when I try to evaluate the model on a test set of size 100 steps, I get an error
"RuntimeError: The size of tensor a (100) must match the size of tensor b (180) at non-singleton dimension 0"
The text was updated successfully, but these errors were encountered:
馃殌 Feature Request
I would like to implement a changepoint kernel in my GP model. An example is given in Duvenaud's thesis in Eq. 2.19
where we can dress two kernels with sigmoid functions. Is this something that can be done with GPyTorch?
For example, I would like to take two SpectralMixtureKernel and dress them with sigmoid functions.
I have tried by defining a two sigmoid kernels and dressing the SpectralMixtureKernels in my model
However, during training I get some numerical instabilities "NotPSDError: Matrix not positive definite after repeatedly adding jitter up to 1.0e-04." and even when I try to evaluate the model on a test set of size 100 steps, I get an error
"RuntimeError: The size of tensor a (100) must match the size of tensor b (180) at non-singleton dimension 0"
The text was updated successfully, but these errors were encountered: