On 10/5/13 9:10 AM, Joseph Gwinn wrote:
Re: time-nuts Digest, Vol 111, Issue 21
On Sat, 05 Oct 2013 11:54:08 -0400, time-nuts-requ...@febo.com wrote:
Message: 6
Date: Sat, 05 Oct 2013 08:53:52 -0700
From: Jim Lux <jim...@earthlink.net>
To: time-nuts@febo.com
Subject: Re: [time-nuts] exponential+linear fit
Message-ID: <52503610.4040...@earthlink.net>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
On 10/5/13 8:47 AM, Tim Shoppa wrote:
How slow of a processor are you working with? A modern PC using a general
purpose graphing and fitting tool (e.g. gnuplot) will fit tens of thousands
of points in a fraction of a second.
http://people.duke.edu/~hpgavin/gnuplot.html
If you want to do this in your own code, there are lots of least-squares
fitting examples in every first year numerical analysis textbook in every
computer language ever written (I learned this in FORTRAN 4 naturally).
http://en.wikipedia.org/wiki/Least_squares
If you have hardware substantially more primitive (e.g. a PIC, a bag of
2N2222's and 555's, etc.) you have to do the fit on, then it's an
interesting problem :-)
In the future, more the latter than the former. 48 MHz ARM Cortex-M4,
for instance.
For now, though, yeah, it's on a not very fast PC (Windows Experience
Rating around 2), but compiled matlab does it very quickly. But, given
that sooner or later I'm going to be heading for a more resource
constrained environment, or trying to FPGA it, I'm looking for simple
algorithms to implement the simplified models.
And, of course, I don't want to drag in all the libraries that one gets
with Matlab,Octave,gnuplot, etc.
They're great for figuring out what's going on and trying out the
algorithms.
There are lots of suitable subroutines available in "Numerical Recipes
in C" by Press et al, that can be used as is, or as the starting point.
<http://apps.nrbook.com/c/index.html>
Yes, I have that (and in FORTRAN, too)..
It's not so much the implementation details (although efficient
implementations are of use) as the general strategy.
e.g. do a nonlinear iterative fit to find the 4 parameters of interest
OR do some sort of sequential "remove this" then "remove that"
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