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models can now specify a preferred optimser (defaults to tnc)

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James Hensman 2012-12-04 13:10:17 -08:00
parent 1a9872c187
commit 4dcf2b85ce
1 changed files with 7 additions and 2 deletions
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9
GPy/core/model.py
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@ -18,6 +18,7 @@ class model(parameterised):
self.optimization_runs = []
self.sampling_runs = []
self.set_param(self.get_param())
self.preferred_optimizer = 'tnc'
def get_param(self):
raise NotImplementedError, "this needs to be implemented to utilise the model class"
def set_param(self,x):
@ -161,15 +162,19 @@ class model(parameterised):
else:
self.expand_param(initial_parameters)
def optimize(self, optimizer = 'tnc', start = None, **kwargs):
def optimize(self, optimizer=None, start=None, **kwargs):
"""
Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
kwargs are passed to the optimizer. They can be:
:max_f_eval: maximum number of function evaluations
:messages: whether to display during optimisatio
:messages: whether to display during optimisation
:param optimzer: whice optimizer to use (defaults to self.preferred optimizer)
:type optimzer: string TODO: valid strings?
"""
if optimizer is None:
optimizer = self.preferred_optimizer
def f(x):
self.expand_param(x)