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52ba8e4ba3
slices are now handles by special indexing kern parts, such as coregionalisation, independent_outputs. The old slicing functionality has been removed simply to clean up the code a little. Now that input_slices still exist (and will continue to be useful) in kern.py. They do need a little work though, for the psi-statistics
46 lines
1.6 KiB
Python
46 lines
1.6 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 sparse_GP 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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from GP_regression import GP_regression
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class sparse_GP_regression(sparse_GP):
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"""
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Gaussian Process model for regression
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This is a thin wrapper around the sparse_GP 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, normalize_X=False, normalize_Y=False, Z=None, M=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]) + 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])[: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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#likelihood defaults to Gaussian
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likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
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sparse_GP.__init__(self, X, likelihood, kernel, Z, normalize_X=normalize_X)
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