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Changes in plot function: sampling vs numerical approximation

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Ricardo 2013-11-11 14:23:10 +00:00
parent b37dd01299
commit 4be40da23a
1 changed files with 3 additions and 2 deletions
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5
GPy/core/gp_base.py
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@ -173,7 +173,8 @@ class GPBase(Model):
upper = m + 2*np.sqrt(v) upper = m + 2*np.sqrt(v)
Y = self.likelihood.Y Y = self.likelihood.Y
else: else:
m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts) m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts,sampling=False) #Compute the exact mean
m_, v_, lower, upper = self.predict(Xgrid, which_parts=which_parts,sampling=True,num_samples=15000) #Apporximate the percentiles
Y = self.likelihood.data Y = self.likelihood.data
for d in which_data_ycols: for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol) gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
@ -210,7 +211,7 @@ class GPBase(Model):
m, _ = self._raw_predict(Xgrid, which_parts=which_parts) m, _ = self._raw_predict(Xgrid, which_parts=which_parts)
Y = self.likelihood.Y Y = self.likelihood.Y
else: else:
m, _, _, _ = self.predict(Xgrid, which_parts=which_parts,num_samples=100) #FIXME we need a balance between accuracy and speed to define num_samples m, _, _, _ = self.predict(Xgrid, which_parts=which_parts,sampling=False)
Y = self.likelihood.data Y = self.likelihood.data
for d in which_data_ycols: for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T m_d = m[:,d].reshape(resolution, resolution).T