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added priors behaviour as intended and issue #38 closed and fixed

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Max Zwiessele 2013-06-04 18:09:02 +01:00
parent 29790e327a
commit 75f4e26b23
5 changed files with 16 additions and 16 deletions
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8
GPy/likelihoods/Gaussian.py
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@ -19,12 +19,12 @@ class Gaussian(likelihood):
# normalization
if normalize:
self._bias = data.mean(0)[None, :]
self._offset = data.mean(0)[None, :]
self._scale = data.std(0)[None, :]
# Don't scale outputs which have zero variance to zero.
self._scale[np.nonzero(self._scale == 0.)] = 1.0e-3
else:
self._bias = np.zeros((1, self.D))
self._offset = np.zeros((1, self.D))
self._scale = np.ones((1, self.D))
self.set_data(data)
@ -36,7 +36,7 @@ class Gaussian(likelihood):
self.data = data
self.N, D = data.shape
assert D == self.D
self.Y = (self.data - self._bias) / self._scale
self.Y = (self.data - self._offset) / self._scale
if D > self.N:
self.YYT = np.dot(self.Y, self.Y.T)
self.trYYT = np.trace(self.YYT)
@ -66,7 +66,7 @@ class Gaussian(likelihood):
"""
Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
"""
mean = mu * self._scale + self._bias
mean = mu * self._scale + self._offset
if full_cov:
if self.D > 1:
raise NotImplementedError, "TODO"