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clfs.model_selector
Module: clfs.model_selector
Inheritance diagram for mvpa.clfs.model_selector:
Model selction.
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class mvpa.clfs.model_selector.ModelSelector(parametric_model, dataset)
Bases: object
Model selection facility.
Select a model among multiple models (i.e., a parametric model,
parametrized by a set of hyperparamenters).
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max_log_marginal_likelihood(hyp_initial_guess, maxiter=1, optimization_algorithm='scipy_cg', ftol=0.001, fixedHypers=None, use_gradient=False, logscale=False)
Set up the optimization problem in order to maximize
the log_marginal_likelihood.
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)
|
NOTE: the maximization of log_marginal_likelihood is a non-linear
optimization problem (NLP). This fact is confirmed by Dmitrey,
author of OpenOpt.
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solve(problem=None)
Solve the maximization problem, check outcome and collect results.