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