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variational returns now the right raveled indices

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Max Zwiessele 2014-03-24 09:06:48 +00:00
parent ae81cbce6c
commit ed2aaab4bb
1 changed files with 15 additions and 9 deletions
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10
GPy/core/parameterization/variational.py
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@ -60,8 +60,6 @@ class SpikeAndSlabPrior(VariationalPrior):
S.gradient -= (1. - (1. / (S))) * gamma /2.
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0)
class VariationalPosterior(Parameterized):
def __init__(self, means=None, variances=None, name=None, *a, **kw):
super(VariationalPosterior, self).__init__(name=name, *a, **kw)
@ -75,6 +73,14 @@ class VariationalPosterior(Parameterized):
if self.has_uncertain_inputs():
assert self.variance.shape == self.mean.shape, "need one variance per sample and dimenion"
def _raveled_index(self):
index = np.empty(dtype=int, shape=0)
size = 0
for p in self._parameters_:
index = np.hstack((index, p._raveled_index()+size))
size += p._realsize_ if hasattr(p, '_realsize_') else p.size
return index
def has_uncertain_inputs(self):
return not self.variance is None