Hi dhruva, Thanks for the quick reply.
As a user of this library in production software, I will try to answer a few of the queries that I had asked or solved during my development.
Thanks.
1. I sometimes need to clear the contents of an accumulator. What is the best way to do this?
Since a pristine or unused accumulator object is quite small (I have not done actual measurements), I just kept 2 variables of the same accumulator type in my C++ wrapper class. I just copied the unused when ever I wanted to reset the accumulator to its original state. One is used for doing all the math and other is used to restore the state.
I see. Thanks.
2. If my platform does not support HW floating point ops, and all my sample values are, e.g. ints, is there a way to specify that the *computation *and *return value itself* (i.e. result_type, e.g. the mean) will be done in ints (naturally, at the cost of precision)?
I have used UDT as I needed some extra information from the results (as I was working on data from a time series). The reverse, using a POD like int must be possible. Instead of the first argument being float/double, use an int. If that does not work, you can wrap an int in a UDT, implement some of the operators like '=', '<', '/' depending on the accumulator you plan to use (I did that as I needed to send a UDT).
Using an int still returns a float -- which is quite sensible. By UDT do you mean User Defined Type? This is an interesting approach. I didn't realize that the result_type depends on the value type in that way, I didn't see any UDTs mentioned in the docs.
3. Is there any info about numerical stability of the algorithms used? For example, there are several waysto calculate the variance, not all being equal. The same goes for algorithmic efficiency and memory requirements.
No clue on this, I would like to some complexity information so that I can choose or know the computation impact.
Maybe someone else knows.
4. On the same note, is there anywhere that explains/describes the actual algorithms used?
The docs show the formula used to an extent.
5. Finally, is there an implementation of a rolling (sliding window) incremental variance computation?
Check the mean using circular buffer. I had a similar request and the author was kind enough to implement and share it (Thanks Eric for the prompt guiding you did). See under "boost/accumulators/statistics/rolling*" in Boost 1.38+ "include" folder.
Yes, I saw the post and Eric's reply, and the latest ver. 1.39 has rolling_count, rolling_sum and rolling_mean built in (based, I guess, on that implementation). I was wondering if there is a rolling_variance implementation somewhere. Thanks, Adi