On Tue, Apr 12, 2011 at 09:32:31PM +0100, Ed W wrote:
> On 12/04/2011 15:05, Miroslav Lichvar wrote:
> > Hm, I just had a crash while I was messing with the tick value outside
> > chronyd. The sourcestats stddev ended up as -nan which caused assert
> > failure in find_best_sample_index. It seems to
On 13/04/2011 12:10, Miroslav Lichvar wrote:
> On Tue, Apr 12, 2011 at 09:32:31PM +0100, Ed W wrote:
>> On 12/04/2011 15:05, Miroslav Lichvar wrote:
>>> Hm, I just had a crash while I was messing with the tick value outside
>>> chronyd. The sourcestats stddev ended up as -nan which caused assert
>>
On Wed, Apr 13, 2011 at 01:30:28PM +0100, Ed W wrote:
> On 13/04/2011 12:10, Miroslav Lichvar wrote:
> > It seems the problem is that in the weights calculation is used
> > weighted variance, which can create the positive feedback. Using
> > unweighted variance instead should fix it nicely.
> >
>
On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
On Wed, Apr 13, 2011 at 01:30:28PM +0100, Ed W wrote:
On 13/04/2011 12:10, Miroslav Lichvar wrote:
It seems the problem is that in the weights calculation is used
weighted variance, which can create the positive feedback. Using
unweighted variance i
On Wed, Apr 13, 2011 at 09:01:43AM -0700, Bill Unruh wrote:
> Hm, whether one used normalised or unnormalised weights should not make a
> difference to the fit, or the variance in the slope. I looked at
> that code a few years ago, but have forgotten it by now. Will have
> to look again.
It doesn'
On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
On Wed, Apr 13, 2011 at 09:01:43AM -0700, Bill Unruh wrote:
Hm, whether one used normalised or unnormalised weights should not make a
difference to the fit, or the variance in the slope. I looked at
that code a few years ago, but have forgotten it by
On Wed, Apr 13, 2011 at 09:48:46AM -0700, Bill Unruh wrote:
> On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
> >sd = sqrt(inst->variance)
> >sd_weight = 1.0 + SD_TO_DIST_RATIO * (peer_distances[i] - min_distance) / sd;
> >weights[i] = sd_weight * sd_weight;
>
> That use of sd in the weights is new i
On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
On Wed, Apr 13, 2011 at 09:48:46AM -0700, Bill Unruh wrote:
On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
sd = sqrt(inst->variance)
sd_weight = 1.0 + SD_TO_DIST_RATIO * (peer_distances[i] - min_distance) / sd;
weights[i] = sd_weight * sd_weight;
Th
On Wed, Apr 13, 2011 at 11:16:48AM -0700, Bill Unruh wrote:
> Adding a constant to the
> distance (assuming this did not change the min_distance) still changes the
> weights in yours (and a lot if sd gets small) .
I meant adding a constant to all distances.
> If you wanted you
> could put in sd+m
On Wed, 13 Apr 2011, Miroslav Lichvar wrote:
On Wed, Apr 13, 2011 at 11:16:48AM -0700, Bill Unruh wrote:
Adding a constant to the
distance (assuming this did not change the min_distance) still changes the
weights in yours (and a lot if sd gets small) .
I meant adding a constant to all distanc
Ok I think I have figured out what is happening. The way in which the weights
are used in the program is as if a set of w_i measurements were made at the
point x_i, with each of them yielding the value y_i. But the estimate of the
standard deviation goes as 1/sqrt(N) where N is the total number of
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