On Jun 14, 2012, at 1:53 PM, James Bergstra wrote: > On Thu, Jun 14, 2012 at 11:01 AM, Nathaniel Smith <n...@pobox.com> wrote: > >>> Indeed that would be great as sympy already has already excellent math >>> expression rendering. >>> >>> An alternative would be to output mathml or something similar that >>> could be understood by the mathjax rendering module of the IPython >>> notebook. >> >> I'd find it quite useful if it could spit out the derivative as Python >> code that I could check and integrate into my source. I often have a >> particular function that I need to optimize in many different >> situations, but would rather not pull in a whole (complex and perhaps >> fragile) bytecode introspection library just to repeatedly recompute >> the same function on every run... >> >> -N > > I was hoping to get by with bytecode-> bytecode interface, are there > bytecode -> source tools that could help here? >
There have been some attempts in the past. The most advanced tool I've seen here is by Sean Ross-Ross: https://github.com/srossross/meta Here's an example: import meta # get some code object (i.e. from compile or from func.func_code --- with no return statement) mod2 = meta.decompile(code) meta.dump_python_source(mod2) -Travis > Otherwise it might be possible to appeal to the symbolic intermediate > representation to produce more legible source. > > With regards to "pulling in a whole bytecode introspection library" I > don't really see what you mean. If the issue is that you want some way > to verify that the output function is actually computing the right > thing, then I hear you - that's an issue. If the issue that autodiff > itself is slow, then I'd like to hear more about the application, > because in minimization you usually have to call the function many > times (hundreds) so the autodiff overhead should be relatively small > (I'm not counting Theano's function compilation time here, which still > can be significant... but that's a separate concern.) > > - James > -- > http://www-etud.iro.umontreal.ca/~bergstrj > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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