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40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
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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 .. import likelihoods
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from .. import kern
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from .. import util
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class GPHeteroscedasticRegression(GP):
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"""
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Gaussian Process model for heteroscedastic regression
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This is a thin wrapper around the models.GP class, with a set of sensible defaults
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:param X: input observations
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:param Y: observed values
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:param kernel: a GPy kernel, defaults to rbf
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NB: This model does not make inference on the noise outside the training set
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"""
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def __init__(self, X, Y, kernel=None, Y_metadata=None):
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Ny = Y.shape[0]
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if Y_metadata is None:
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Y_metadata = {'output_index':np.arange(Ny)[:,None]}
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else:
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assert Y_metadata['output_index'].shape[0] == Ny
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if kernel is None:
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kernel = kern.RBF(X.shape[1])
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#Likelihood
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likelihood = likelihoods.HeteroscedasticGaussian(Y_metadata)
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super(GPHeteroscedasticRegression, self).__init__(X,Y,kernel,likelihood, Y_metadata=Y_metadata)
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def plot(self,*args):
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return NotImplementedError
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