Re: [math] Example using EstimationProblem, WeightedMeasurement from apache.commons.math?

[mi...@TaosNet.com]

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());

> mickeydog@TaosNet.com 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 mickeydog@TaosNet.com:
>>>
>>>> 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: user-unsubscribe@commons.apache.org
>> For additional commands, e-mail: user-help@commons.apache.org
>>
>>
>
>
>
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Re: [math] Example using EstimationProblem, WeightedMeasurement from apache.commons.math?

[mi...@TaosNet.com]

oic. I stand corrected!

> mickeydog@TaosNet.com 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
>>
>> > mickeydog@TaosNet.com 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 mickeydog@TaosNet.com:
>> >>>
>> >>>> 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: user-unsubscribe@commons.apache.org
>> >> For additional commands, e-mail: user-help@commons.apache.org
>> >>
>> >>
>> >
>> >
>> > ---------------------------------------------------------------------
>> > To unsubscribe, e-mail: user-unsubscribe@commons.apache.org
>> > For additional commands, e-mail: user-help@commons.apache.org
>> >
>>
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscribe@commons.apache.org
>> For additional commands, e-mail: user-help@commons.apache.org
>>
>
>
>
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Re: [math] Example using EstimationProblem, WeightedMeasurement from apache.commons.math?

[Luc Maisonobe <Lu...@free.fr>]

mickeydog@TaosNet.com 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
>
> > mickeydog@TaosNet.com 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 mickeydog@TaosNet.com:
> >>>
> >>>> 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: user-unsubscribe@commons.apache.org
> >> For additional commands, e-mail: user-help@commons.apache.org
> >>
> >>
> >
> >
> > ---------------------------------------------------------------------
> > To unsubscribe, e-mail: user-unsubscribe@commons.apache.org
> > For additional commands, e-mail: user-help@commons.apache.org
> >
>
>
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