Thanks Gael. My thinking was to implement "Basic LSH with basic data
structures" and then spend some of the time working on seeing if moderate
improvements (i.e. a more complex data structure) can deliver benefits.
This way, we get the key deliverable, and spend some time trying to see if
we can do better.
I'd also like to see scalability added to the evaluation criteria!
On 14 March 2014 03:59, Gael Varoquaux <[email protected]>wrote:
> On Thu, Mar 13, 2014 at 09:57:27AM -0700, Gael Varoquaux wrote:
> > On Thu, Mar 13, 2014 at 10:12:04AM +0200, Daniel Vainsencher wrote:
> > > Other data structures worth evaluating (without depending on them) are
> > > BTrees
>
> > Let's really avoid this for now. It's not like we have too many people
> > who understand the codebase and are available to maintain it.
>
> To stress things again: we often see projects, whether they are GSOC or
> elsewhere, that fail to deliver, because people implement plenty of fancy
> things, and do not focus on the quality and maintainability. At the end
> of the day, these projects leave _nothing_ for scikit-learn.
>
> There is no point shooting first for the moon. Remember the 80/20
> tradeoff rules: 80% of the benefits (in term of usecases, speed, ...) can
> be achieved with 20% of the efforts.
>
> Gaël
>
>
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