From aa473178f79c4df9b504a44b18f164d0512d219e Mon Sep 17 00:00:00 2001 From: Andreas Date: Fri, 14 Nov 2014 21:48:46 +0000 Subject: [PATCH] Working One vs All sparse gp classification wrapper --- GPy/models/one_vs_all_sparse_classification.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/GPy/models/one_vs_all_sparse_classification.py b/GPy/models/one_vs_all_sparse_classification.py index 3db382ec..3bdd2647 100644 --- a/GPy/models/one_vs_all_sparse_classification.py +++ b/GPy/models/one_vs_all_sparse_classification.py @@ -18,9 +18,9 @@ class OneVsAllSparseClassification(object): """ - def __init__(self, X, Y, kernel=None,Y_metadata=None,messages=True): + 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]) + kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1]) + GPy.kern.Bias(X.shape[1]) likelihood = GPy.likelihoods.Bernoulli() @@ -35,6 +35,8 @@ class OneVsAllSparseClassification(object): Ynew[Y.flatten()!=yj] = 0 Ynew[Y.flatten()==yj] = 1 - m = GPy.models.SparseGPClassification(X,Ynew,kernel=kernel,Y_metadata=Y_metadata) + 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