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34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
# Copyright (c) 2014, James Hensman, Alan Saul
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from ..core import GP
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from ..core.parameterization.param import Param
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from ..inference.latent_function_inference import VarGauss
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log_2_pi = np.log(2*np.pi)
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class GPVariationalGaussianApproximation(GP):
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"""
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The Variational Gaussian Approximation revisited
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@article{Opper:2009,
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title = {The Variational Gaussian Approximation Revisited},
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author = {Opper, Manfred and Archambeau, C{\'e}dric},
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journal = {Neural Comput.},
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year = {2009},
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pages = {786--792},
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}
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"""
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def __init__(self, X, Y, kernel, likelihood, Y_metadata=None):
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num_data = Y.shape[0]
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self.alpha = Param('alpha', np.zeros((num_data,1))) # only one latent fn for now.
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self.beta = Param('beta', np.ones(num_data))
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inf = VarGauss(self.alpha, self.beta)
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super(GPVariationalGaussianApproximation, self).__init__(X, Y, kernel, likelihood, name='VarGP', inference_method=inf)
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self.link_parameter(self.alpha)
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self.link_parameter(self.beta)
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