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Multioutput models added

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Ricardo 2013-08-02 20:10:02 +01:00
parent 1c2a4c5c64
commit 4c7ebb6601
9 changed files with 251 additions and 62 deletions
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7
GPy/core/gp.py
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@ -173,7 +173,7 @@ class GP(GPBase):
This is to allow for different normalizations of the output dimensions.
"""
assert isinstance(self.likelihood,EP_Mixed_Noise)
assert hasattr(self,'multioutput')
index = np.ones_like(Xnew)*output
Xnew = np.hstack((Xnew,index))
@ -182,7 +182,10 @@ class GP(GPBase):
mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
# now push through likelihood
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
if isinstance(self.likelihood,EP_Mixed_Noise):
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
else:
mean, var, _025pm, _975pm = self.likelihood_list[output].predictive_values(mu, var, full_cov)
return mean, var, _025pm, _975pm
def _raw_predict_single_output(self, _Xnew, output=0, which_parts='all', full_cov=False,stop=False):