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Revert "changed to 'update_gradients_q_variational'"

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This reverts commit f311bfdf17.
This commit is contained in:
Max Zwiessele 2014-02-24 11:45:18 +00:00
parent 811fcc3423
commit d90d67a8c1
3 changed files with 8 additions and 7 deletions
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4
GPy/core/parameterization/variational.py
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@ -63,7 +63,7 @@ class NormalPosterior(VariationalPosterior):
from ...plotting.matplot_dep import variational_plots
return variational_plots.plot(self,*args)
class SpikeAndSlabPosterior(VariationalPosterior):
class SpikeAndSlab(VariationalPosterior):
'''
The SpikeAndSlab distribution for variational approximations.
'''
@ -71,7 +71,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
"""
binary_prob : the probability of the distribution on the slab part.
"""
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
super(SpikeAndSlab, self).__init__(means, variances, name)
self.gamma = Param("binary_prob",binary_prob,)
self.add_parameter(self.gamma)

7
GPy/kern/_src/rbf.py
View file

@ -182,7 +182,7 @@ class RBF(Kern):
return grad
def update_gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
mu = posterior_variational.mean
S = posterior_variational.variance
self._psi_computations(Z, mu, S)
@ -194,9 +194,8 @@ class RBF(Kern):
tmp = self._psi2[:, :, :, None] / self.lengthscale2 / self._psi2_denom
grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * self._psi2_mudist).sum(1).sum(1)
grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*self._psi2_mudist_sq - 1)).sum(1).sum(1)
posterior_variational.mean.gradient = grad_mu
posterior_variational.variance.gradient = grad_S
return grad_mu, grad_S
def gradients_X(self, dL_dK, X, X2=None):
#if self._X is None or X.base is not self._X.base or X2 is not None:

4
GPy/models/bayesian_gplvm.py
View file

@ -63,7 +63,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
super(BayesianGPLVM, self).parameters_changed()
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.q)
self.kern.update_gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
# TODO: This has to go into kern
# maybe a update_gradients_q_variational?
self.q.mean.gradient, self.q.variance.gradient = self.kern.gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.q)