neureka VS AssertJ

I am using https://github.com/Gleethos/neureka to do some personal machine learning and also basic data science stuff at my workplace. It is inspired by PyTorch and it's dynamic autograd system (which records the computation graph to then traverse it for backpropagation eagerly). The library is super lightweight, has a nice API and documentation, but it's still very young and not as feature rich.

A lightweight option: Neureka - https://github.com/Gleethos/neureka

https://github.com/Gleethos/neureka A tensor/nd-array library inspired by Numpy and PyTorch. Supports:

If you are into deep learning: I work on an nd-array / tensor library with autograd support. https://github.com/Gleethos/neureka

Maybe not the feedback you want, but would you consider developing something that looks like plain old (and frankly beautiful) AssertJ?

AssertJ - fluent assertions

AssertJ https://github.com/assertj/assertj

Truth or AssertJ for easier assertions in tests with better exceptions

While we are at it: AssertJ is very powerful for writing assertions.

In OO you can make beautiful DSLs that allow really declarative use within that domain, e.g. test assertions in AssertJ, but everybody in the OO world is sensible enough to not try and claim OO as such being declarative. I guess they don't feel a need to try to prove the superiority of the paradigm.

AssertJ comes with a variety of assertions that can be chained together and are specific to the type of your "actual" variable.

At any rate it comes from a library called assertj.

While Hamcrest add some fluentidity to unit tests ä, I prefer the fluent assertions of AssertJ.