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Have most of the likelihood testing working, laplace likelihood parameters need fixing, some of the signs are wrong I believe

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Alan Saul 2014-02-10 12:28:24 +00:00
parent 625943ef27
commit 0f263d2ff2
5 changed files with 122 additions and 71 deletions
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13
GPy/core/parameterization/parameterized.py
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@ -256,6 +256,16 @@ class Parameterized(Constrainable, Pickleable, Observable):
cPickle.dump(self, f, protocol)
def copy(self):
"""Returns a (deep) copy of the current model """
#dc = dict()
#for k, v in self.__dict__.iteritems():
#if k not in ['_highest_parent_', '_direct_parent_']:
#dc[k] = copy.deepcopy(v)
#dc = copy.deepcopy(self.__dict__)
#dc['_highest_parent_'] = None
#dc['_direct_parent_'] = None
#s = self.__class__.new()
#s.__dict__ = dc
return copy.deepcopy(self)
def __getstate__(self):
if self._has_get_set_state():
@ -419,6 +429,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Convenience for fixed, tied checking of param:
#===========================================================================
def fixed_indices(self):
return np.array([x.is_fixed for x in self._parameters_])
def _is_fixed(self, param):
# returns if the whole param is fixed
if not self._has_fixes():
@ -449,7 +461,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
# if removing constraints before adding new is not wanted, just delete the above line!
self.constraints.add(transform, rav_i)
param = self._get_original(param)
#FIXME: Max, is this the right thing to do to handle fixed?
if not (transform == __fixed__):
param._set_params(transform.initialize(param._get_params()), update=False)
if warning and any(reconstrained):