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46 lines
1.8 KiB
Python
46 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 SparseGP
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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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from ..inference.latent_function_inference import expectation_propagation_dtc
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class SparseGPClassification(SparseGP):
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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, num_inducing=10):
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def __init__(self, X, Y=None, likelihood=None, kernel=None, Z=None, num_inducing=10, Y_metadata=None):
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if kernel is None:
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kernel = kern.RBF(X.shape[1])
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likelihood = likelihoods.Bernoulli()
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if Z is None:
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i = np.random.permutation(X.shape[0])[:num_inducing]
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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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SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=expectation_propagation_dtc.EPDTC(), name='SparseGPClassification',Y_metadata=Y_metadata)
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#def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp', Y_metadata=None):
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