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52 lines
2.2 KiB
ReStructuredText
52 lines
2.2 KiB
ReStructuredText
.. _creating_new_models:
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*******************
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Creating new Models
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*******************
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In GPy all models inherit from the base class :py:class:`~GPy.core.parameterized.Parameterized`. :py:class:`~GPy.core.parameterized.Parameterized` is a class which allows for parameterization of objects. All it holds is functionality for tying, bounding and fixing of parameters. It also provides the functionality of searching and manipulating parameters by regular expression syntax. See :py:class:`~GPy.core.parameterized.Parameterized` for more information.
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The :py:class:`~GPy.core.model.Model` class provides parameter introspection, objective function and optimization.
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In order to fully use all functionality of
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:py:class:`~GPy.core.model.Model` some methods need to be implemented
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/ overridden. And the model needs to be told its parameters, such
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that it can provide optimized parameter distribution and handling.
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In order to explain the functionality of those methods
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we will use a wrapper to the numpy ``rosen`` function, which holds
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input parameters :math:`\mathbf{X}`. Where
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:math:`\mathbf{X}\in\mathbb{R}^{N\times 1}`.
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Obligatory methods
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==================
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:py:meth:`~GPy.core.model.Model.__init__` :
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Initialize the model with the given parameters. These need to
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be added to the model by calling
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`self.add_parameter(<param>)`, where param needs to be a
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parameter handle (See parameterized_ for details).::
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self.X = GPy.core.Param("input", X)
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self.add_parameter(self.X)
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:py:meth:`~GPy.core.model.Model.log_likelihood` :
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Returns the log-likelihood of the new model. For our example
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this is just the call to ``rosen`` and as we want to minimize
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it, we need to negate the objective.::
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return -scipy.optimize.rosen(self.X)
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:py:meth:`~GPy.core.model.Model.parameters_changed` :
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Updates the internal state of the model and sets the gradient of
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each parameter handle in the hierarchy with respect to the
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log_likelihod. Thus here we need to set the negative derivative of
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the rosenbrock function for the parameters. In this case it is the
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gradient for self.X:
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self.X.gradient = -scipy.optimize.rosen_der(self.X)
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Optional methods
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================
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Currently none.
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