James Chapman

Ergh! And this worked in a previous version?

It does seem to be a problem elsewhere: https://stackoverflow.com/questions/53757856/segmentation-fault-when-creating-multiprocessing-array

Agree about testing with different OS - my ‘hack’ has been that I develop on windows and the automatic tests here use Ubuntu. although weirdly that suggests the package does...

So this passes all the tests on Ubuntu: Installing numpy (1.24.4) Installing scipy (1.9.3) Installing scikit-learn (1.3.0) If that works then I’ll make the dependencies hard to avoid your issue...

Ah no because I haven’t been testing the jobs>1 behaviour. Will add to the tests

On the representations

(And trust me the EY loss is going to work better! I'm almost thinking about changing the default in this package to that solver)

Loss is applied to representations because we are calling self.objective with representations as argument

I actually think in future I will change the callable loss classes like TCCALoss just class methods of DTCCA. It's just a throwback to an older version where it was...

Sorry half my response got lost so that looked really weird like I was just offering up something totally random 😅 The answer to your question is NO! I'm assuming...