text: # 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
  
  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
  