Hello,
after reading a lot through the tutorial this is the code that i came up
with regarding the implementation of a gaussian process regression
optimisation (File appended):
initCovarianceAndGradients(): initialisation of matrices and
calculations which are needed by both marginal likelihood calculation
and gradient calculation:
Within this function i calculate some things globally which are strongly
reused by the value() and gradient() functions. What i do not really
understand is the passing of the double[] argument to the value()
function and the value() function of the gradient() method. Are those
methods called by the optimizer with the updated parameters? If this is
the case i have to recalculate the global calculations with each call to
the value() and gradient() methods.
Thanks for clarification
Am 14.05.2012 12:53, schrieb Gilles Sadowski:
> Hello.
>
>>>
>>> thanks for the reply. But i wonder what is the input for value and gradient.
>>> in DifferentiableMultivariateRealFunction this needs to be a double array
>>> but what needs to be provided there? The parameters for the function to
>>> optimize?
>>>
>>> Thank you very much again
>>>
>>> Andreas
>>>
>> Do please have a look to the examples, as your question (and my
>> answer) is too vague if not supported by proper code. I guess the
>> answer to your question is 'yes', the double[] array is indeed the set
>> of parameters, but again, do check the examples, I would not like to
>> be misguiding you. Besides the user guide which should provide you
>> with the answer, have a look to this implementation [1], line 153. In
>> this implementation, x[i] and y[i] are the data points, yhat[i] are
>> the model predictions, and a[] are the parameters. You should be able
>> to find your way with this example.
>
> I've also just added another bit of code showcasing the usage of the
> "nonlinear leastsquares" optimizers (svn revision 1338144).
>
>
> Best regards,
> Gilles
>
> 
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Dipl.Ing. (FH) Andreas Niekler
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