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Merge branch 'feature-multioutput' of https://github.com/esiivola/GPy into esiivola-feature-multioutput

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mzwiessele 2018-07-27 14:56:31 +02:00
commit 179ffa76dd
3 changed files with 254 additions and 2 deletions
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22
GPy/testing/likelihood_tests.py
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@ -128,7 +128,16 @@ class TestNoiseModels(object):
censored[random_inds] = 1
self.Y_metadata = dict()
self.Y_metadata['censored'] = censored
self.Y_metadata['output_index'] = np.zeros((self.N,1), dtype=int)
self.Y_metadata2 = dict()
self.Y_metadata2['censored'] = censored
inds = np.zeros((self.N,1), dtype=int)
inds[5:10] = 1
inds[10:] = 2
self.Y_metadata2['output_index'] = inds
self.combY = self.Y
self.combY[10:] = np.where(self.binary_Y[10:] >0, self.binary_Y[10:], 0)
print(self.combY)
#Make a bigger step as lower bound can be quite curved
self.step = 1e-4
@ -292,6 +301,15 @@ class TestNoiseModels(object):
"Y": self.positive_Y,
"Y_metadata": self.Y_metadata,
"laplace": True
},
"multioutput_default": {
"model": GPy.likelihoods.MultioutputLikelihood([GPy.likelihoods.Gaussian(), GPy.likelihoods.Poisson(), GPy.likelihoods.Bernoulli()]),
"link_f_constraints": [partial(self.constrain_bounded, lower=0, upper=1)],
"laplace": True,
"Y": self.combY,
"Y_metadata": self.Y_metadata2,
"ep": True,
"variational_expectations": True,
}
#,
#GAMMA needs some work!"Gamma_default": {
@ -618,7 +636,7 @@ class TestNoiseModels(object):
# Y = Y/Y.max()
white_var = 1e-4
kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
ep_inf = GPy.inference.latent_function_inference.EP()
ep_inf = GPy.inference.latent_function_inference.EP(always_reset=True)
m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, Y_metadata=Y_metadata, inference_method=ep_inf)
m['.*white'].constrain_fixed(white_var)