Source code for GPy.models.one_vs_all_sparse_classification

# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
import GPy

[docs]class OneVsAllSparseClassification(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,num_inducing=10): if kernel is None: kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1]) + GPy.kern.Bias(X.shape[1]) likelihood = GPy.likelihoods.Bernoulli() assert Y.shape[1] == 1, 'Y should be 1 column vector' labels = np.unique(Y.flatten()) self.results = {} for yj in labels: print('Class %s vs all' %yj) Ynew = Y.copy() Ynew[Y.flatten()!=yj] = 0 Ynew[Y.flatten()==yj] = 1 m = GPy.models.SparseGPClassification(X,Ynew,kernel=kernel.copy(),Y_metadata=Y_metadata,num_inducing=num_inducing) m.optimize(messages=messages) self.results[yj] = m.predict(X)[0] del m del Ynew