eigen | This is a mirror of the latest stable version of Eigen

One option is to create a logical array and then call Eigen::select on it. Inspired by https://forum.kde.org/viewtopic.php?f=74&t=91378

In this case:

When calculating an eigenvector you may change its sign and the solution will also be a valid one.

So any PCA axis can be reversed and the solution will be valid.

Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

Looks like the TensorFlow Lite version is too old to be supported. Please consider using TF 2.5 or beyonds.

The first thing I'd try is ctest's regex selectors. From ctest --help

I found a solution which is at least working for me.

The idea is to increase the error vector once the parameters are leaving their sanity boundaries.

This can be achieved by the following function:

In my experience, these errors on Unix often stem from missing external libraries. For example, installing the R xml2 package requires libxml2-dev to be installed via the system package manager (i.e. outside R) otherwise installation will fail.

I can't read French, but it looks to me as though the dependency jpeg failed, due to a missing external jpeg library, and then everything cascaded from there. You could try installing some version of the libjpeg library. I know it comes pre-installed in Ubuntu which may be why that worked for you. I'm a little surprised it doesn't come installed already in OpenSUSE, but I have no experience with OpenSUSE.

This is just a matter of numerical accuracy. As pointed out by @Damien in the comment, the matrix is ill-conditioned, and thus a small difference in the input can lead to a large change in the results. By copying from the output only the first five digits and using them to manually define the second matrix, a significant part is discarded that is handled internally but not displayed with the standard accuracy of std::cout.

Take for instance the entry ATA(0,0). By using is ATA << 1.36154e+13,..., any value of the order of 1.e7 or lower is not considered. This is, however, the order of the other entries in the matrix.

In short, both results are correct, but your manually defined matrix ATA is not the same as the one that is calculated in the first part. (You can take the difference between the two to verify this).

As the parameter N is a non-type template parameter, it must be a value known at compile time. This means that you cannot really store an Eigen::Tensor with unspecified N in a variable, as each instantiation with a different size is a different type.

You can work around this by using containers such as std::variant and std::any. They make it possible to store an object of a type from either a closed set of types or any type respectively. As an example, you could use std::any to create a helper tensor_holder class like the following: