[EMAIL PROTECTED] a écrit :
Thanks for the reply and my apologies for omitting the [math] marker.
Afa the model goes, I'm not sure how to answer. What I am doing is
smoothing a curve using the loess function, and the last step is to use a
weighted least square regression on each point and its neighborhood.
In addition to my previous message where I gave an implementation based
on EstimationProblem and WeightedMeasurement as specified, I would like
to say that in this very simple case, using these classes is probably
overkill. Low degree polynomials fitting in one dimension only can be
done very simply with a single loop updating some sums as each sample
point is added and performing a simple direct computation to retrieve
the polynomials coefficients at the end of the loop.
EstimationProblem, EstimatedParameters and WeightedParameters are more
suited for non-linear problems with several different measurements types
and parameters and complex models. The reference use case for which this
class was created was to perform spacecraft orbit determination from
range, range-rate, angular and more exotic measurements with a numerical
model taking into account several perturbing forces. This requires some
features that add to the complexity of the classes. I'm not sure using
such heavyweight component is wise for your case. You may have
performance issues with them.
Luc
Hi,
First of all, I have added a [math] marker on the subject line. This list
is shared among all commons projects and this type of markers help people
filter the messages.
I will send a usage example on the list in a few hours (late evening,
european time), when I'm back home. Would you like to have anything
special in this example ? For example what kind of model do you want to
be fitted to the x,ydata ?
Luc
Selon [EMAIL PROTECTED]:
Can anybody show me an example of a weighted least squares regression
using classes like EstimationProblem, WeightedMeasurement from
apache.commons.math?
I have data that looks like this: (x,y,weight), e.g.
1,1,0.2
2,3, 0.4
3,2, 1.0
4,6, 0.8
5,4, 0.3
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