to enable defining bounds for theta and noise, the value of the nugget and a noise variance per design input #257
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smt/surrogate_models/krg_based.py
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| if not self.options["is_noise_het"]: | ||
| self.noise = self.optimal_theta[self.D.shape[1] :] |
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Why the case heteroscedastic is not treated like the homoscedastic one ?
Can't we uniformize these two ?
Just by adding the length ?
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We can do it but that forces the initialisation of the noise variances in a list, p.e. : noise0 = nt * [1e-6] ... and check the option using len(noise). In that case, we need to check that len(noise) is correct (equal to nt). I can do it if everyone is ok with that.
smt/surrogate_models/krg_based.py
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| @@ -833,18 +883,28 @@ def grad_minus_reduced_likelihood_function(log10t): | |||
| k, incr, stop, best_optimal_rlf_value, max_retry = 0, 0, 1, -1e20, 10 | |||
| while k < stop: | |||
| # Use specified starting point as first guess | |||
| if self.options["eval_noise"]: | |||
| if self.options["eval_noise"] and not self.options["is_noise_het"]: | |||
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Same remark as before
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In this initial implementation, the noise variances are pointwise sensible estimates via (Ankenman et al., 2010), and they are not estimated via ML, i.e. they are fixed for the MLE. In future developments, they will be integrated in the MLE.
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