max_log_marginal_likelihood(self,
hyp_initial_guess,
maxiter=1,
optimization_algorithm="scipy_cg",
ftol=1.0e-3,
fixedHypers=None,
use_gradient=False,
logscale=False)
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Set up the optimization problem in order to maximize
the log_marginal_likelihood.
NOTE: the maximization of log_marginal_likelihood is a non-linear
optimization problem (NLP). This fact is confirmed by Dmitrey,
author of OpenOpt.
- Parameters:
parametric_model (Classifier) - the actual parameteric model to be optimized.
hyp_initial_guess (numpy.ndarray) - set of hyperparameters' initial values where to start
optimization.
optimization_algorithm (string) - actual name of the optimization algorithm. See
http://scipy.org/scipy/scikits/wiki/NLP
for a comprehensive/updated list of available NLP solvers.
(Defaults to 'ralg')
ftol (float) - threshold for the stopping criterion of the solver,
which is mapped in OpenOpt NLP.ftol
(Defaults to 1.0e-3)
fixedHypers (numpy.ndarray (boolean array)) - boolean vector of the same size of hyp_initial_guess;
'False' means that the corresponding hyperparameter must
be kept fixed (so not optimized).
(Defaults to None, which during means all True)
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