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Added Multioutput derivative kernel for adding derivatives easy and changed it to default kernel in multioutput gp model

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Siivola Eero 2018-09-05 17:49:33 +03:00
parent 0daad96da1
commit 9a6e645bc6
3 changed files with 84 additions and 3 deletions
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5
GPy/models/multioutput_gp.py
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@ -42,7 +42,7 @@ class MultioutputGP(GP):
Ny = len(Y_list)
assert isinstance(kernel_list, list)
kernel = kern.MultioutputKern(kernels=kernel_list, cross_covariances=kernel_cross_covariances)
kernel = kern.MultioutputDerivativeKern(kernels=kernel_list, cross_covariances=kernel_cross_covariances)
assert isinstance(likelihood_list, list)
likelihood = likelihoods.MultioutputLikelihood(likelihood_list)
@ -53,8 +53,7 @@ class MultioutputGP(GP):
else:
inference_method = expectation_propagation.EP()
super(MultioutputGP, self).__init__(X,Y,kernel,likelihood, Y_metadata={'output_index':self.output_index, 'trials':np.ones(self.output_index.shape)}, inference_method = inference_method)# expectation_propagation.MultioutputEP()) # expectation_propagation.EP())
#expectation_propagation.MultioutputEP())
super(MultioutputGP, self).__init__(X,Y,kernel,likelihood, Y_metadata={'output_index':self.output_index, 'trials':np.ones(self.output_index.shape)}, inference_method = inference_method)
def predict_noiseless(self, Xnew, full_cov=False, Y_metadata=None, kern=None):
if isinstance(Xnew, list):