Source code for GPy.models.one_vs_all_classification

# 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)