# 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