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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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James Hensman 2013-05-03 14:01:40 +01:00
commit bc99d57f8d
12 changed files with 668 additions and 138 deletions
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5
GPy/models/Bayesian_GPLVM.py
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@ -309,6 +309,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
Slatentgrads = ax3.quiver(xlatent, S, Ulatent, Sg, color=colors,
units=quiver_units, scale_units=quiver_scale_units,
scale=quiver_scale)
ax3.set_ylim(0, 1.)
xZ = np.tile(np.arange(0, Z.shape[0])[:, None], Z.shape[1])
UZ = np.zeros_like(Z)
@ -428,11 +429,11 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
cbarkmmdl.update_normal(imkmmdl)
ax2.relim()
ax3.relim()
# ax3.relim()
ax4.relim()
ax5.relim()
ax2.autoscale()
ax3.autoscale()
# ax3.autoscale()
ax4.autoscale()
ax5.autoscale()

12
GPy/models/sparse_GP.py
View file

@ -205,13 +205,13 @@ 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):