Alex Martelli wrote: > for x in whatever_other_iterable: yield x > > into (say) > > yield from whatever_other_iterable > > is minute and not worth changing the syntax (even though something like > 'yield from' would mean no keywords would need to be added).
I agree that the improvement is minor, but I'll take what I can get. Although, I can think that perhaps there could be a potential efficiency improvement as well - right now, each result has to crawl its way up the stack of yields. For an 8 x 8 board, each final result gets yielded 8 times. A 'yield from' might potentially be able to splice the results directly into the output of the generator using some kind of iterator chain logic. I'm not sure how feasible this is, however. A more compelling benefit would be some means of yielding a value across multiple stack frames. Currently the generator semantics are limited, in that they only allow co-routine behavior between a caller and its directly called subroutine. What I am more interested in, however, is the use of backtracking search in Python, and its application to more complex search problems. The main benefit of the generator approach is in the elimination of callbacks, allowing the consumer of the search results to retain its local state in a convenient way, (Anyone who's ever struggled with the SAX API for XML parsing knows what I am talking about.) One difference between these more complex problems and the simple example I posted is that it isn't just simple recursion at work. Each level of search may invoke a variety of different search strategies for each sub-part of the problem, which themselves will do the same with their own portion. Here's a thought experiment: What would it take to make Python the premier language for AI research? (Not that I am proposing that this should be the Python community's goal, this is more in the way of a brainstorm session.) -- http://mail.python.org/mailman/listinfo/python-list
