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Allow the default constraint of a Param object to be 'fixed'

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This commit is contained in:
Zhenwen Dai 2014-09-02 11:52:09 +01:00
parent e332c1e246
commit 808cfb0501
3 changed files with 12 additions and 7 deletions
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7
GPy/core/parameterization/parameter_core.py
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@ -13,7 +13,7 @@ Observable Pattern for patameterization
"""
from transformations import Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
from transformations import Transformation,Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
import numpy as np
import re
import logging
@ -541,7 +541,8 @@ class Indexable(Nameable, Observable):
Constrain the parameter to the given
:py:class:`GPy.core.transformations.Transformation`.
"""
self.param_array[...] = transform.initialize(self.param_array)
if isinstance(transform, Transformation):
self.param_array[...] = transform.initialize(self.param_array)
reconstrained = self.unconstrain()
added = self._add_to_index_operations(self.constraints, reconstrained, transform, warning)
self.notify_observers(self, None if trigger_parent else -np.inf)
@ -617,7 +618,7 @@ class Indexable(Nameable, Observable):
"""
Helper preventing copy code.
This adds the given what (transformation, prior etc) to parameter index operations which.
revonstrained are reconstrained indices.
reconstrained are reconstrained indices.
warn when reconstraining parameters if warning is True.
TODO: find out which parameters have changed specifically
"""

11
GPy/core/parameterization/variational.py
View file

@ -7,7 +7,7 @@ Created on 6 Nov 2013
import numpy as np
from parameterized import Parameterized
from param import Param
from transformations import Logexp, Logistic
from transformations import Logexp, Logistic,__fixed__
class VariationalPrior(Parameterized):
def __init__(self, name='latent space', **kw):
@ -35,12 +35,15 @@ class NormalPrior(VariationalPrior):
class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, pi=None, learnPi=False, variance = 1.0, name='SpikeAndSlabPrior', **kw):
super(VariationalPrior, self).__init__(name=name, **kw)
self.pi = Param('pi', pi, Logistic(1e-10,1.-1e-10))
super(SpikeAndSlabPrior, self).__init__(name=name, **kw)
self.variance = Param('variance',variance)
self.learnPi = learnPi
if learnPi:
self.add_parameters(self.pi)
self.pi = Param('Pi', pi, Logistic(1e-10,1.-1e-10))
else:
self.pi = Param('Pi', pi, __fixed__)
self.add_parameter(self.pi)
def KL_divergence(self, variational_posterior):
mu = variational_posterior.mean