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44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from ..core import GP
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from . import SparseGPClassification
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from .. import likelihoods
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from .. import kern
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from ..inference.latent_function_inference.expectation_propagation import EP
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import numpy as np
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class OneVsAllClassification(object):
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"""
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Gaussian Process classification: One vs all
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This is a thin wrapper around the models.GPClassification class, with a set of sensible defaults
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:param X: input observations
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:param Y: observed values, can be None if likelihood is not None
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:param kernel: a GPy kernel, defaults to rbf
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.. Note:: Multiple independent outputs are not allowed
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"""
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def __init__(self, X, Y, kernel=None,Y_metadata=None,messages=True):
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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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assert Y.shape[1] == 1, 'Y should be 1 column vector'
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labels = np.unique(Y.flatten())
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self.results = {}
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for yj in labels:
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Ynew = Y.copy()
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Ynew[Y.flatten()!=yj] = 0
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Ynew[Y.flatten()==yj] = 1
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m = SparseGPClassification(X,Ynew,kernel=kernel,Y_metadata=Y_metadata)
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m.optimize(messages=messages)
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stop
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self.results[yj] = m.predict(X)
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