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auto_scale option for heteroscedastic noise

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Ricardo 2013-05-02 15:53:38 +01:00
parent 9ad17cb811
commit 485665241f
1 changed files with 6 additions and 6 deletions
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12
GPy/models/sparse_GP.py
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@ -200,13 +200,13 @@ class sparse_GP(GP):
self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
self._compute_kernel_matrices()
if self.auto_scale_factor:
self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
#if self.auto_scale_factor:
# if self.likelihood.is_heteroscedastic:
# self.scale_factor = max(1,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
# else:
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
if self.auto_scale_factor:
if self.likelihood.is_heteroscedastic:
self.scale_factor = max(100,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
else:
self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
self._computations()
def _get_params(self):