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all parameters in memory

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This commit is contained in:
Max Zwiessele 2014-03-10 08:21:13 +00:00
parent f7223ea377
commit 546d5dfff3
9 changed files with 135 additions and 103 deletions
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30
GPy/core/parameterization/variational.py
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@ -21,7 +21,7 @@ class VariationalPrior(Parameterized):
updates the gradients for mean and variance **in place**
"""
raise NotImplementedError, "override this for variational inference of latent space"
class NormalPrior(VariationalPrior):
def KL_divergence(self, variational_posterior):
var_mean = np.square(variational_posterior.mean).sum()
@ -63,20 +63,38 @@ class SpikeAndSlabPrior(VariationalPrior):
class VariationalPosterior(Parameterized):
def __init__(self, means=None, variances=None, name=None, **kw):
super(VariationalPosterior, self).__init__(name=name, **kw)
def __init__(self, means=None, variances=None, name=None, *a, **kw):
super(VariationalPosterior, self).__init__(name=name, *a, **kw)
self.mean = Param("mean", means)
self.variance = Param("variance", variances, Logexp())
self.ndim = self.mean.ndim
self.shape = self.mean.shape
self.variance = Param("variance", variances, Logexp())
self.add_parameters(self.mean, self.variance)
self.num_data, self.input_dim = self.mean.shape
self.add_parameters(self.mean, self.variance)
if self.has_uncertain_inputs():
assert self.variance.shape == self.mean.shape, "need one variance per sample and dimenion"
def has_uncertain_inputs(self):
return not self.variance is None
def __getitem__(self, s):
if isinstance(s, (int, slice, tuple, list, np.ndarray)):
import copy
n = self.__new__(self.__class__, self.name)
dc = self.__dict__.copy()
dc['mean'] = self.mean[s]
dc['variance'] = self.variance[s]
dc['_parameters_'] = copy.copy(self._parameters_)
n.__dict__.update(dc)
n._parameters_[dc['mean']._parent_index_] = dc['mean']
n._parameters_[dc['variance']._parent_index_] = dc['variance']
n.ndim = n.mean.ndim
n.shape = n.mean.shape
n.num_data = n.mean.shape[0]
n.input_dim = n.mean.shape[1] if n.ndim != 1 else 1
return n
else:
return super(VariationalPrior, self).__getitem__(s)
class NormalPosterior(VariationalPosterior):
'''
@ -107,7 +125,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
self.gamma = Param("binary_prob",binary_prob, Logistic(1e-10,1.-1e-10))
self.add_parameter(self.gamma)
def plot(self, *args):
"""
Plot latent space X in 1D: