I should add that I'm looking into the possibility of writing a Fortran -> RPython compiler in order to mechanically port many of the rest of the functions scipy and get them into the JIT.<div><br></div><div>j<br><br>
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On Fri, Jan 20, 2012 at 7:16 AM, Joseph Perla <span dir="ltr"><<a href="mailto:josephjavierperla@gmail.com" target="_blank">josephjavierperla@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hello everyone,<div><br></div><div>I want to add functions to numpypy and also start making scipypy useful to scientists. How do I commit my code?<br><div><br></div><div>First, a little bit about myself: I have been following PyPy's development for 5 years. I met Armin Rigo and other PyPy devs at EuroPython 2011 in Florence this past year. I gave a talk about minimalist Python web templates: weby templates.</div>
<div><br></div><div>PyPy always seemed like a hugely complicated project far above my talents. I look forward to finally contributing code myself.</div><div><br></div><div>My goal: I am developing probabilistic models along the lines of Latent Dirichlet Allocation for artificial intelligence applications. I love Python, so I'm developing my models in Python. Unfortunately, it is slow. Fortunately, my models are numerical calculation and loop heavy. It will be easy to run my code on pypy once the numpy and scipy support is stronger.</div>
</div><div><br></div><div>So, I downloaded the nightly build. It nearly works! It is missing a few necessary functions: scipypy.special.gammaln, scipy.special.psi, numpy.reshape, numpy.matrix, and the numpy.random module.</div>
<div><br></div><div>So, I implemented gammaln and psi. It seems to be within 2x speed of the Fortran77 code in scipy (it's hard to measure! how do i do this?). I didn't see anywhere on the web about a scipypy project existing. I think I want to start it now, and I want to contribute these functions. An incomplete scipypy will be useful to a lot of people, and will encourage more new developers to add to it. You probably have a plan about how you want to integrate the original scipy code, but I think we should start moving forward with whatever we have as soon as available.</div>
<div><br></div><div>I also know I can implement much of the numpy.random module (as well as matrix and reshape) easily once I know how to get the codebase and push changes. I've been using Python and Numpy for years. </div>
<div><br></div><div>Of course I'll use the original numpy code when it's pure python.</div><div><br></div><div>I'm excited to submit, just please let me know how to do that. These improvements will do a lot for machine learning research, I think.</div>
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<div>j</div>
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