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[normalize]

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This commit is contained in:
mzwiessele 2014-08-27 13:07:23 -07:00
parent 7ec0e75c0e
commit a3c8739f9e
2 changed files with 8 additions and 5 deletions
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12
GPy/core/gp.py
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@ -159,17 +159,17 @@ class GP(Model):
"""
#predict the latent function values
mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)
if self.normalizer is not None:
mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var)
# now push through likelihood
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata)
if self.normalizer is not None:
return self.normalizer.inverse_mean(mean), self.normalizer.inverse_variance(var)
else:
return mean, var
return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
m, v = self._raw_predict(X, full_cov=False)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)
def predictive_gradients(self, Xnew):
@ -207,6 +207,8 @@ class GP(Model):
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
m, v = self._raw_predict(X, full_cov=full_cov)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
v = v.reshape(m.size,-1) if len(v.shape)==3 else v
if not full_cov:
Ysim = np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T