Class Model

x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__

(inherited documentation)

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:

:param max_iters: maximum number of function evaluations :type max_iters: int :messages: True: Display messages during optimisation, "ipython_notebook": :type messages: bool"string :param optimizer: which optimizer to use (defaults to self.preferred optimizer) :type optimizer: string

Valid optimizers are:

• 'scg': scaled conjugate gradient method, recommended for stability. See also GPy.inference.optimization.scg
• 'fmin_tnc': truncated Newton method (see scipy.optimize.fmin_tnc)
• 'simplex': the Nelder-Mead simplex method (see scipy.optimize.fmin),
• 'lbfgsb': the l-bfgs-b method (see scipy.optimize.fmin_l_bfgs_b),
• 'lbfgs': the bfgs method (see scipy.optimize.fmin_bfgs),
• 'sgd': stochastic gradient decsent (see scipy.optimize.sgd). For experts only!

Perform random restarts of the model, and set the model to the best seen solution.

If the robust flag is set, exceptions raised during optimizations will be handled silently. If _all_ runs fail, the model is reset to the existing parameter values.

\*\*kwargs are passed to the optimizer.

:param num_restarts: number of restarts to use (default 10) :type num_restarts: int :param robust: whether to handle exceptions silently or not (default False) :type robust: bool :param parallel: whether to run each restart as a separate process. It relies on the multiprocessing module. :type parallel: bool :param num_processes: number of workers in the multiprocessing pool :type numprocesses: int :param max_f_eval: maximum number of function evaluations :type max_f_eval: int :param max_iters: maximum number of iterations :type max_iters: int :param messages: whether to display during optimisation :type messages: bool

.. note:

   If num_processes is None, the number of workes in the
   multiprocessing pool is automatically set to the number of processors
   on the current machine.

The objective function for the given algorithm.

This function is the true objective, which wants to be minimized. Note that all parameters are already set and in place, so you just need to return the objective function here.

For probabilistic models this is the negative log_likelihood (including the MAP prior), so we return it here. If your model is not probabilistic, just return your objective to minimize here!

The gradients for the objective function for the given algorithm. The gradients are w.r.t. the *negative* objective function, as this framework works with *negative* log-likelihoods as a default.

You can find the gradient for the parameters in self.gradient at all times. This is the place, where gradients get stored for parameters.

This function is the true objective, which wants to be minimized. Note that all parameters are already set and in place, so you just need to return the gradient here.

For probabilistic models this is the gradient of the negative log_likelihood (including the MAP prior), so we return it here. If your model is not probabilistic, just return your *negative* gradient here!

Representation of the model in html for notebook display.

Overrides: parameterized.Parameterized._repr_html_

str(x)

Overrides: object.__str__

(inherited documentation)