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52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
# Copyright (c) 2012, James Hensman
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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 SVIGP
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from .. import likelihoods
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from .. import kern
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class SVIGPRegression(SVIGP):
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"""
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Gaussian Process model for regression
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This is a thin wrapper around the SVIGP class, with a set of sensible defalts
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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+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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.. Note:: Multiple independent outputs are allowed using columns of Y
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"""
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def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10):
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# kern defaults to rbf (plus white for stability)
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if kernel is None:
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kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
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# Z defaults to a subset of the data
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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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# likelihood defaults to Gaussian
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likelihood = likelihoods.Gaussian(Y, normalize=False)
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SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
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self.load_batch()
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def getstate(self):
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return GPBase.getstate(self)
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def setstate(self, state):
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return GPBase.setstate(self, state)
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