Le 4 avril 2012 07:31, Mathieu Blondel <[email protected]> a écrit : > Creating a new language has its advantages (e.g., tailor the language to > scientific computing) but the main disadvantage is that they need to > recreate a complete ecosystem (plotting, interactive shell, ...). I wonder > if we could get most of the advantages of julia in Python with Numba > (https://github.com/ContinuumIO/numba) or PyPy.
That ability to do lisp-style macro manipulating the AST can be very interesting for runtime-dependent code generation / optimization (e.g. leveraging GPU when there, switch between cluster distribution and multiple cores depending on both the infrastructure and the dataset size...). This can be hacked in python but this will never feel as natural as in lisp (or using compiler extensions as in scala). And I agree that it's probably a good idea to keep python as simple as it is in that respect (no macros / runtime AST modification / DSL support). It can make julia very powerful in some situations though. I don't like the default namespace pollution with domain specific functions and the lack of a clean module system. IMHO, explicit imports of symbols in a namespace is very important for maintainability once you pass the "single experimental script" step in a project and readability counts, explicit is better than implicit... -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
