apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-30 14:35:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

some little changes

Browse source
This commit is contained in:
Zhenwen Dai 2014-09-03 18:32:25 +01:00
parent 39327e0789
commit 59939bd50c
3 changed files with 10 additions and 16 deletions
Download patch file Download diff file Expand all files Collapse all files

16
GPy/core/parameterization/parameter_core.py
View file

@ -515,19 +515,12 @@ class Indexable(Nameable, Observable):
#===========================================================================
# Tie parameters together
#===========================================================================
def _has_ties(self):
if self._highest_parent_.ties.tied_param is None:
return False
if self.has_parent():
return self._highest_parent_.ties.label_buf[self._highest_parent_._raveled_index_for(self)].sum()>0
return True
def tie_together(self, *plist):
plist = list(plist)
plist.append(self)
self._highest_parent_.ties.tie_together(plist)
self._highest_parent_._set_fixed(self,self._raveled_index())
# self._highest_parent_._set_fixed(self,self._raveled_index())
self._trigger_params_changed()
#===========================================================================
@ -683,9 +676,10 @@ class OptimizationHandlable(Indexable):
if self.has_parent() and (self.constraints[__fixed__].size != 0 or self._has_ties()):
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
return self._optimizer_copy_[np.logical_and(fixes, self._highest_parent_.ties.getTieFlag(self))]
fixes[self._highest_parent_.ties.label_buf[self._highest_parent_._raveled_index_for(self)]] = FIXED
return self._optimizer_copy_[fixes]
elif self._has_fixes():
return self._optimizer_copy_[self._fixes_]
return self._optimizer_copy_[np.logical_and(self._fixes_,self.ties.label_buf==0)]
self._optimizer_copy_transformed = True

7
GPy/core/parameterization/ties_and_remappings.py
View file

@ -55,7 +55,7 @@ class Tie(Parameterized):
TODO:
1. Add the support for multiple parameter tie_together and tie_vector
2. Properly handling parameters with constraints
3. Properly handling the merging of two models
3. Properly handling the merging of two models [DONE]
4. Properly handling initialization
"""
@ -64,7 +64,6 @@ class Tie(Parameterized):
# If ture, it does not need to check consistency
self._PROPAGATE_VAL_ = False
super(Tie, self).__init__(name)
self.size = sum(p.size for p in self.parameters)
self.tied_param = None
# The buffer keeps track of tie status
self.label_buf = None
@ -146,7 +145,6 @@ class Tie(Parameterized):
self.tied_param.tie[:old_size] = old_tie_
self.tied_param.tie[old_size:] = range(start_label,start_label+num)
self.add_parameter(self.tied_param)
self.size = sum(p.size for p in self.parameters)
return range(start_label,start_label+num)
def _remove_tie_param(self, labels):
@ -163,7 +161,6 @@ class Tie(Parameterized):
self.tied_param = Param('tied',new_buf)
self.tied_param.tie[:] = old_tie_[idx]
self.add_parameter(self.tied_param)
self.size = sum(p.size for p in self.parameters)
def _merge_tie_labels(self, labels):
"""Merge all the labels in the list to the first one"""
@ -177,7 +174,7 @@ class Tie(Parameterized):
self.label_buf = None
self.buf_idx = None
else:
self.label_buf = np.zeros((self._highest_parent_.size,),dtype=np.uint32)
self.label_buf = np.zeros((self._highest_parent_.param_array.size,),dtype=np.uint32)
self._traverse_param(lambda x:np.put(self.label_buf,self._highest_parent_._raveled_index_for(x),x.tie), self._highest_parent_, [])
self.buf_idx = self._highest_parent_._raveled_index_for(self.tied_param)

3
GPy/kern/_src/psi_comp/__init__.py
View file

@ -28,6 +28,9 @@ class PSICOMP_RBF(Pickleable):
return ssrbf_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior)
else:
raise ValueError, "unknown distriubtion received for psi-statistics"
def _setup_observers(self):
pass
class PSICOMP_Linear(Pickleable):