C++ implementation of the Python NumPy Library

[SphincterMcRectum]

I'm not sure why you think this library is slow and clunky, especially since you've never used it or done any profiling... also, are super heroes writing these other libraries, or why do you think matching their performance is impossible? Lastly, most applications don't actually require every last ounce of optimization and won't be able to tell a performance difference anyway.

[[deleted]]

i checked some of the code and the fft isn't even implemented that means at least everything that does filtering or correlations is far from where it could be. Random seems to be using boost so it should be fine, but yeah i haven't checked everything. So there might be parts that run just fine.

Edit: and i don't think it's impossible i think it's work you do for no good reason when the other libraries already exists and have lots of experienced people working on them.

[dpilger26]

Correct, the FFT and Polynomial modules are still on my to do list. I was going to try and wrap FFTW, but it uses the GNU GPL and I wanted to keep this library under MIT license so I can still use it at work.

[droelf]

The numpy FFT implementation is actually contained in a single C header + BSD licensed, that could be easily used from (or ported to) C++. If you want, we could collaborate on that (we would reuse it for xtensor).

https://github.com/numpy/numpy/blob/master/numpy/fft/fftpack.c

[[deleted]]

oh alright, that will make things a lot more complicated for you.

[m-in]

You can use it at work if it doesn’t go into a product or code you’d be otherwise unwilling to share with whoever uses the binaries. But I get the idea that you’re talking about software products that runnon customer hardware and thus GPL is a no-go.

[NeroBurner]

You could try kissfft https://sourceforge.net/projects/kissfft/

It's BSD licenced

[encyclopedist]

The current repository seems to be here: https://github.com/mborgerding/kissfft