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[SSGPLVM] Learn prior parameters

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This commit is contained in:
Zhenwen Dai 2014-03-04 10:39:56 +00:00
parent 0f6004034e
commit 0258abf5c4
2 changed files with 8 additions and 3 deletions
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6
GPy/core/parameterization/variational.py
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@ -34,12 +34,12 @@ class NormalPrior(VariationalPrior):
variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, variance = 1.0, pi = 0.5, name='SpikeAndSlabPrior', **kw):
def __init__(self, pi, variance = 1.0, name='SpikeAndSlabPrior', **kw):
super(VariationalPrior, self).__init__(name=name, **kw)
assert variance==1.0, "Not Implemented!"
self.pi = Param('pi', pi)
self.variance = Param('variance',variance)
self.add_parameters(self.pi, self.variance)
self.add_parameters(self.pi)
def KL_divergence(self, variational_posterior):
mu = variational_posterior.mean
@ -58,6 +58,8 @@ class SpikeAndSlabPrior(VariationalPrior):
gamma.gradient -= np.log((1-self.pi)/self.pi*gamma/(1.-gamma))+(np.square(mu)+S-np.log(S)-1.)/2.
mu.gradient -= gamma*mu
S.gradient -= (1. - (1. / (S))) * gamma /2.
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0)
class VariationalPosterior(Parameterized):