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Hello,
the gaussian process are by default fitted using the fmin_cobyal optimizer. The optimizer is started from various random inital conditions to avoid local extrema. The problem is, the worst of those extrema is chosen in the end.
optimal_minus_rlf_value, optimal_par = self.reduced_likelihood_function(theta=optimal_theta)
# sign error
# reduced_likelihood_function return the likelihood
# not the negative likelihood
optimal_rlf_value = - optimal_minus_rlf_value(theta=optimal_theta)The reason it did not show up in the unit test is, that they been to easy. Even the worst local maximum was good enough to pass the test. I added a test which is more "tough" and manage to prove the current solution wrong.
Here is the solution to the unit test plotted with the current implementation:
and fixed:
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