Vojtech Pavlik <[email protected]> writes:
> Btw, what I don't completely understand is why you need linear
> regression, when you're not trying to detect motion or something like
> that. Basic floating average, or even simpler filtering like the input
> core uses for fuzz could work well enough I believe.
Indeed, this function doesn't make much sense:
+static inline int smooth_history(int x0, int x1, int x2, int x3)
+{
+ return x0 - ( x0 * 3 + x1 - x2 - x3 * 3 ) / 10;
+}
In the X driver, a derivative estimate is computed from the last 4
absolute positions, and in that case the least squares estimate is
given by the factors [.3 .1 -.1 -.3]. However, in this case you want
to compute an absolute position estimate from the last 4 absolute
positions, and in this case the least squares estimate is given by the
factors [.25 .25 .25 .25], ie a floating average. If the function is
changed to this:
+static inline int smooth_history(int x0, int x1, int x2, int x3)
+{
+ return (x0 + x1 + x2 + x3) / 4;
+}
the standard deviation of the noise will be reduced by a factor of 2
compared to the unfiltered values. With the old smooth_history()
function, the noise reduction will only be a factor of 1.29.
--
Peter Osterlund - [email protected]
http://web.telia.com/~u89404340
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