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The module pickle is your friend here!

My walkaround to this problema is detailed in the update 2, but it's basically what I explained here: I’ve seen in this link (Error: C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe failed with exit code: 1) that some people tried a downgrade in the node version, I was using originally the version 12 and some say that with version 10 should work. After that I can perform the four steps provided in the answer:

You already have an omp parallel for pragma on the innermost loop. To give that effect, you probably need to enable OpenMP support in your compiler by setting a compiler flag (for example, with the GCC compiler suite, that would be the -fopenmp flag). You may also need to #include the omp.h header.

But with that said, I doubt you're going to gain much from this parallelization, because one run of the loop you are parallelizing just doesn't do much work. There is runtime overhead associated with parallelization that offsets the gains from running multiple loop iterations at the same time, so I don't think you're going to net very much.

There is no real answer to this question, but I'd like to distill some of the more important optimizations discussed in the comments. Let's focus on just the inner loops.

Primarily, you need to avoid excessive multiplications and function calls. And there are some tricks that aren't guaranteed to be optimized by compilers. For example, we know intuitively that pow(x, 2) just squares a value, but if your compiler doesn't optimize this, then it's much less efficient than simply x * x.

Further, it was identified that the O(N²) loop actually can be reduced to O(N²/2) because distances are symmetric. This is a big deal, if you're calling expensive things like pow and sqrt. You can just scale the final result of E1 by 2 to compensate for halving the number of calculations.

And on the subject of sqrt, it was also identified that you don't need to do that before your distance test. Do it after, because the test sqrt(d) < 5 is the same as d < 25.

Let's go even further, beyond the comments. Notice that the < 5 test actually relies on a multiplication involving kes. If you precomputed a distance-squared value that also incorporates the kes scaling, then you have even fewer multiplications.

You can also remove the kk value from the E1 calculation. That doesn't need to happen in a loop... probably. By that, I mean you're likely to have floating point error in all these calculations. So every time you change something, your final result might be slightly different. I'm gonna do it anyway.

So... After that introduction, let's go!

GPflow uses a single tf.Variable for each parameter - such as a kernel's lengthscales - and TensorFlow only allows you to change the trainable status of a Variable as a whole. Having a separate parameter per dimension would not be easy to implement for arbitrary dimensions, but you can easily subclass the kernel you want and override lengthscales with a property as follows:

I actually found a solution for this issue. Since the bugged version of pip gets installed as soon as your create the virtualenv, I forcibly uninstalled it and then installed the version that worked when I built my app. Here is the code that I used:

you have defined the kernel with X of dimention (-1, 4) and Y of dimension (-1, 1) but you are giving it X_pred of dimension (1, 1) (the first element of x_pred reshaped to (1, 1))

give the x_pred to the model for prediction (an input with dimension of (-1, 4))

I have ESP32 so my config sample will be ESP32 based. Download https://github.com/lixas/ESP32-Stubs-VSCode

OR

Use following to generate for your board: https://github.com/Josverl/micropython-stubber and download those files from board

My settings.json file:

That would depend on what you are actually doing. The very basic GPs should be similar, just that GPflow relies on tensorflow for the gradients (if used) and probably some technical implementation differences.

For the other more advanced models, both libraries provide references to the respective papers in the docs. In my opinion, GPflow's design is mainly centered around the SVGP framework from [1] and [2] (and many other extensions.. I can really recommend [2] if you are interested in the theory). But they still do provide some other implementations.

I use GPflow since it works on the GPU and offers a lot of state-of-the-art implementations. However, the disadvantage would be that it is under a lot of change.

If you want to use classic GPs and are not too concerned with performance or very up-to-date methods I'd say GPy should be sufficient and the more stable variant.

[1] Hensman, James, Alexander Matthews, and Zoubin Ghahramani. "Scalable variational Gaussian process classification." (2015).

[2] Matthews, Alexander Graeme de Garis. Scalable Gaussian process inference using variational methods. Diss. University of Cambridge, 2017.