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Working One vs All sparse gp classification wrapper

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Andreas 2014-11-14 21:48:46 +00:00
parent 031aa7b315
commit aa473178f7
1 changed files with 5 additions and 3 deletions
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8
GPy/models/one_vs_all_sparse_classification.py
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@ -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: 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() likelihood = GPy.likelihoods.Bernoulli()
@ -35,6 +35,8 @@ class OneVsAllSparseClassification(object):
Ynew[Y.flatten()!=yj] = 0 Ynew[Y.flatten()!=yj] = 0
Ynew[Y.flatten()==yj] = 1 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) m.optimize(messages=messages)
self.results[yj] = m.predict(X)[0] self.results[yj] = m.predict(X)[0]
del m
del Ynew