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47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
# Copyright (c) 2013, Ricardo Andrade
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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 sparse_GP
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from .. import likelihoods
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from .. import kern
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from ..likelihoods import likelihood
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class sparse_GP_classification(sparse_GP):
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"""
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sparse Gaussian Process model for classification
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This is a thin wrapper around the sparse_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 likelihood: a GPy likelihood, defaults to Binomial with probit link_function
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:param kernel: a GPy kernel, defaults to rbf+white
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:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_X: False|True
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:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_Y: False|True
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:rtype: model object
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"""
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def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
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if kernel is None:
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kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
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if likelihood is None:
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distribution = likelihoods.likelihood_functions.Binomial()
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likelihood = likelihoods.EP(Y, distribution)
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elif Y is not None:
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if not all(Y.flatten() == likelihood.data.flatten()):
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raise Warning, 'likelihood.data and Y are different.'
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if Z is None:
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i = np.random.permutation(X.shape[0])[:M]
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Z = X[i].copy()
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else:
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assert Z.shape[1]==X.shape[1]
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sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
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self._set_params(self._get_params())
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