Re: [boost] Review Request: Accumulators Framework and Statistical Accumulators Library

From: Michael Stevens (list-boost_at_[hidden])
Date: 2006-11-23 15:13:30

On Monday, 20. November 2006 17:35, Eric Niebler wrote:
> Michael Stevens wrote:
> > I an very interested in this framework so I have started to take a look.
> >
> > General Accumulators are something I could make use of myself. I really
> > like the conceptual design of the framework, and how it allows
> > accumulators to be inter-dependant..
>
> Great! Glad you like it.
>
> > After a quick browse through the documentation I decided to take a look
> > at the code. In particular I was interested in the numerics.
> >
> > I think the current implementation has some serious numerical weaknesses.
> > I looked at 2 algorithms 'sum' and 'variance':
> >
> > In 'sum' I expected to see a compensated summation, this is numerically a
> > lot better then just adding the numbers together.
>
> 'sum' is one I implemented. I'm not surprised to learn there are better
> approaches.
>
> The framework allows for different implementation strategies for the
> statistics, though. Using the extensibility features, you can define
> your own "compensated_sum" accumulator and declare that it satisfies the
> "sum" feature (so that "compensated_sum" and "sum" are indistinguishable
> from the POV of dependency resolution), and even come up with clever
> syntax for it, like:
>
>    accumulator_set< double, features< sum(compensated) > > acc;

Cool! I will have to give it a try·

> You might even try writing "compensated_sum" yourself and submitting it,
> just to see what happens. :-)
>
> The questions of what the default "sum" should do, and what alternate
> implementations should be provided, are open.

I guess what people expect depends on their technical background!

All the best with the library,
        Michael
-- 
___________________________________
Michael Stevens Systems Engineering
34128 Kassel, Germany
Phone/Fax: [phone]
Navigation Systems, Estimation  and
                 Bayesian Filtering
    http://bayesclasses.sf.net
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