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slicing support for kernel input dimension

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This commit is contained in:
Max Zwiessele 2014-03-07 16:59:41 +00:00
parent 5f3524e7da
commit db5fd17609
10 changed files with 178 additions and 65 deletions
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16
GPy/core/parameterization/variational.py
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@ -66,10 +66,10 @@ class VariationalPosterior(Parameterized):
def __init__(self, means=None, variances=None, name=None, **kw):
super(VariationalPosterior, self).__init__(name=name, **kw)
self.mean = Param("mean", means)
self.ndim = self.mean.ndim
self.shape = self.mean.shape
self.variance = Param("variance", variances, Logexp())
self.add_parameters(self.mean, self.variance)
self.ndim = self.mean.ndim
self.shape = self.mean.shape
self.num_data, self.input_dim = self.mean.shape
if self.has_uncertain_inputs():
assert self.variance.shape == self.mean.shape, "need one variance per sample and dimenion"
@ -77,6 +77,18 @@ class VariationalPosterior(Parameterized):
def has_uncertain_inputs(self):
return not self.variance is None
def __getitem__(self, s):
import copy
n = self.__new__(self.__class__)
dc = copy.copy(self.__dict__)
dc['mean'] = dc['mean'][s]
dc['variance'] = dc['variance'][s]
dc['shape'] = dc['mean'].shape
dc['ndim'] = dc['ndim']
dc['num_data'], dc['input_dim'] = self.mean.shape[0], self.mean.shape[1] if dc['ndim'] > 1 else 1
n.__dict__ = dc
return n
class NormalPosterior(VariationalPosterior):
'''