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3c3ec60dea
* Update setup.py remove special handling of scipy dependencies for old python versions * Update __init__.py replace numpy type by native type * replace np.bool by bool
88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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"""
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Introduction
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^^^^^^^^^^^^
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This module contains the fundamental classes of GPy - classes that are
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inherited by objects in other parts of GPy in order to provide a
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consistent interface to major functionality.
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.. inheritance-diagram:: GPy.core.gp.GP
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:top-classes: paramz.core.parameter_core.Parameterizable
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:py:class:`GPy.core.model` is inherited by
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:py:class:`GPy.core.gp.GP`. And :py:class:`GPy.core.model` itself
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inherits :py:class:`paramz.model.Model` from the `paramz`
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package. `paramz` essentially provides an inherited set of properties
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and functions used to manage state (and state changes) of the model.
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:py:class:`GPy.core.gp.GP` represents a GP model. Such an entity is
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typically passed variables representing known (x) and observed (y)
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data, along with a kernel and other information needed to create the
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specific model. It exposes functions which return information derived
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from the inputs to the model, for example predicting unobserved
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variables based on new known variables, or the log marginal likelihood
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of the current state of the model.
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:py:func:`~GPy.core.gp.GP.optimize` is called to optimize
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hyperparameters of the model. The optimizer argument takes a string
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which is used to specify non-default optimization schemes.
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Various plotting functions can be called against :py:class:`GPy.core.gp.GP`.
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.. inheritance-diagram:: GPy.core.gp_grid.GpGrid GPy.core.sparse_gp.SparseGP GPy.core.sparse_gp_mpi.SparseGP_MPI GPy.core.svgp.SVGP
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:top-classes: GPy.core.gp.GP
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:py:class:`GPy.core.gp.GP` is used as the basis for classes supporting
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more specialized types of Gaussian Process model. These are however
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generally still not specific enough to be called by the user and are
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inhereted by members of the :py:class:`GPy.models` package.
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"""
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from GPy.core.model import Model
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from .parameterization import Param, Parameterized
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from . import parameterization
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from .gp import GP
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from .svgp import SVGP
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from .sparse_gp import SparseGP
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from .gp_grid import GpGrid
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from .mapping import *
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#===========================================================================
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# Handle priors, this needs to be
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# cleaned up at some point
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#===========================================================================
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def randomize(self, rand_gen=None, *args, **kwargs):
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"""
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Randomize the model.
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Make this draw from the prior if one exists, else draw from given random generator
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:param rand_gen: np random number generator which takes args and kwargs
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:param flaot loc: loc parameter for random number generator
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:param float scale: scale parameter for random number generator
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:param args, kwargs: will be passed through to random number generator
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"""
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if rand_gen is None:
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rand_gen = np.random.normal
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# first take care of all parameters (from N(0,1))
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x = rand_gen(size=self._size_transformed(), *args, **kwargs)
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updates = self.update_model()
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self.update_model(False) # Switch off the updates
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self.optimizer_array = x # makes sure all of the tied parameters get the same init (since there's only one prior object...)
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# now draw from prior where possible
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x = self.param_array.copy()
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[np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.items() if not p is None]
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unfixlist = np.ones((self.size,),dtype=bool)
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from paramz.transformations import __fixed__
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unfixlist[self.constraints[__fixed__]] = False
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self.param_array.flat[unfixlist] = x.view(np.ndarray).ravel()[unfixlist]
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self.update_model(updates)
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Model.randomize = randomize
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Param.randomize = randomize
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Parameterized.randomize = randomize
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