KernelFunctions.jl
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JuliaGaussianProcesses
Julia package for kernel functions for machine learning
## Introduction: How would you simulate gradients If you want to simulate the gradient of a random function $Z$, it turns out that you simply need to take derivatives of...
Following this [discourse discussion](https://discourse.julialang.org/t/gps-sum-of-independent-kernels/98542). Currently, there is no building block to sum independent Kernels, analog to `KernelTensorProduct` but with addition instead of multiplication: For inputs $x = (x_1,\dots,x_n)$ and $x'...
To agree with our style guide, we should have underscores separating words (snake_case), and `kernelmatrix` (as well as other functions) should follow this rule
Testing locally on 1.8.5, I find that https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/test/mokernels/independent.jl#L17 occasionally fails, ```Julia independent: Test Failed at .../KernelFunctions.jl/test/mokernels/independent.jl:17 Expression: kernelmatrix(k, x, y) == kernelmatrix(k, collect(x), collect(y)) Evaluated: [0.6308251776147069 0.7710805544766418 … 0.0 0.0;...
It would be nice to define `isequal` and/or `==` as widely as possible between `Kernel`s and `Transform`s. At the moment some basic cases seem to be missing, such as ```Julia...
Hi All, Thanks for the wonderful package. I'm just starting to explore the source code. I was curious about a very small decision. If I do: ```julia k = SqExponentialKernel()...
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Julia package for kernel functions for machine learning