oic. I stand corrected! > [EMAIL PROTECTED] a écrit : >> Wow, thanks Luc. >> >> One correction, I think. In the theoreticalValue() method, this: >> return ((a.getEstimate() * x + b.getEstimate()) * x + c.getEstimate()); >> should be: >> return ((a.getEstimate() * x * x + b.getEstimate()) * x + >> c.getEstimate()); > No. This is Hörner's way to evaluate polynomials, an efficient way. The > second x in my statement is applied to the sum a.getEstimate() * x + > b.getEstimate(), so at the end we really have a * x² + b * x + c as > required for a quadratic polynomial. The trick is in the parentheses. > > Luc >> >> > [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 >> >> >> >> --------------------------------------------------------------------- >> >> To unsubscribe, e-mail: [EMAIL PROTECTED] >> >> For additional commands, e-mail: [EMAIL PROTECTED] >> >> >> >> >> > >> > >> > --------------------------------------------------------------------- >> > To unsubscribe, e-mail: [EMAIL PROTECTED] >> > For additional commands, e-mail: [EMAIL PROTECTED] >> > >> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: [EMAIL PROTECTED] >> For additional commands, e-mail: [EMAIL PROTECTED] >> > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] >
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