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efficient handling of Y and YYT

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Nicolo Fusi 2013-02-15 13:54:01 +00:00
parent cc158d64a9
commit eddb677446
2 changed files with 9 additions and 6 deletions
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12
GPy/likelihoods/Gaussian.py
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@ -19,12 +19,12 @@ class Gaussian(likelihood):
self._std = np.ones((1,D)) self._std = np.ones((1,D))
self.Y = self.data self.Y = self.data
# if self.D > self.N: if D > self.N:
self.YYT = np.dot(self.Y,self.Y.T) self.YYT = np.dot(self.Y,self.Y.T)
self.trYYT = np.trace(self.YYT) self.trYYT = np.trace(self.YYT)
# else: else:
# self.YYT = None self.YYT = None
# self.trYYT = None self.trYYT = None
self._set_params(np.asarray(variance)) self._set_params(np.asarray(variance))

3
GPy/models/sparse_GP.py
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@ -49,6 +49,9 @@ class sparse_GP(GP):
self.has_uncertain_inputs=True self.has_uncertain_inputs=True
self.X_uncertainty = X_uncertainty self.X_uncertainty = X_uncertainty
if not self.likelihood.is_heteroscedastic:
self.likelihood.trYYT = np.trace(np.dot(self.likelihood.Y, self.likelihood.Y.T)) # TODO: something more elegant here?
GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X, Xslices=Xslices) GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X, Xslices=Xslices)
#normalise X uncertainty also #normalise X uncertainty also