On Fri, Dec 19, 2008 at 6:53 AM, John Hunter <jdh2358@gmail.com> wrote:

Some sort of Kalman filter?

Chuck

I think an incremental stats module would be a boon to numpy or scipy.On Thu, Dec 18, 2008 at 8:27 PM, Bradford Cross

<bradford.n.cross@gmail.com> wrote:

> This is a new project I just released.

>

> I know it is C#, but some of the design and idioms would be nice in

> numpy/scipy for working with discrete event simulators, time series, and

> event stream processing.

>

> http://code.google.com/p/incremental-statistics/

Eric Firing has a nice module wrtten in C with a pyrex wrapper

(ringbuf) that does trailing incremental mean, median, std, min, max,

and percentile. It maintains a sorted queue to do the last three

efficiently, and handles NaN inputs. I would like to see this

extended to include exponential or other weightings to do things like

incremental trailing exponential moving averages and variances. I

don't know what the licensing terms are of this module, but it might

be a good starting point for an incremental numpy stats module, at

least if you were thinking about supporting a finite lookback window.

We have a copy of this in the py4science examples dir if you want to

take a look:

svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/py4science/examples/pyrex/trailstats

cd trailstats/

make

python movavg_ringbuf.py

Other things that would be very useful are incremental covariance and

regression.

Some sort of Kalman filter?

Chuck