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[optimizer&fixing] optimizer has only one optimizer copy and fixing remembers old constraint

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mzwiessele 2014-05-22 12:44:50 +01:00
parent 5a2bc4863b
commit 9bf7e1baf6
1 changed files with 22 additions and 25 deletions
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47
GPy/core/parameterization/parameter_core.py
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@ -245,7 +245,7 @@ class Pickleable(object):
def __setstate__(self, state): def __setstate__(self, state):
self.__dict__.update(state) self.__dict__.update(state)
self._transformed = True self._optimizer_copy_transformed = False
class Gradcheckable(Pickleable, Parentable): class Gradcheckable(Pickleable, Parentable):
@ -400,7 +400,7 @@ class Indexable(Nameable, Observable):
self[:] = value self[:] = value
index = self._raveled_index() index = self._raveled_index()
reconstrained = self.unconstrain() #reconstrained = self.unconstrain()
index = self._add_to_index_operations(self.constraints, index, __fixed__, warning) index = self._add_to_index_operations(self.constraints, index, __fixed__, warning)
self._highest_parent_._set_fixed(self, index) self._highest_parent_._set_fixed(self, index)
self.notify_observers(self, None if trigger_parent else -np.inf) self.notify_observers(self, None if trigger_parent else -np.inf)
@ -624,12 +624,12 @@ class OptimizationHandlable(Indexable):
""" """
This enables optimization handles on an Object as done in GPy 0.4. This enables optimization handles on an Object as done in GPy 0.4.
`..._transformed`: make sure the transformations and constraints etc are handled `..._optimizer_copy_transformed`: make sure the transformations and constraints etc are handled
""" """
def __init__(self, name, default_constraint=None, *a, **kw): def __init__(self, name, default_constraint=None, *a, **kw):
super(OptimizationHandlable, self).__init__(name, default_constraint=default_constraint, *a, **kw) super(OptimizationHandlable, self).__init__(name, default_constraint=default_constraint, *a, **kw)
self._optimizer_copy_ = None self._optimizer_copy_ = None
self._transformed = True self._optimizer_copy_transformed = False
#=========================================================================== #===========================================================================
# Optimizer copy # Optimizer copy
@ -651,7 +651,7 @@ class OptimizationHandlable(Indexable):
if self.__dict__.get('_optimizer_copy_', None) is None or self.size != self._optimizer_copy_.size: if self.__dict__.get('_optimizer_copy_', None) is None or self.size != self._optimizer_copy_.size:
self._optimizer_copy_ = np.empty(self.size) self._optimizer_copy_ = np.empty(self.size)
if self._transformed: if not self._optimizer_copy_transformed:
self._optimizer_copy_.flat = self.param_array.flat self._optimizer_copy_.flat = self.param_array.flat
[np.put(self._optimizer_copy_, ind, c.finv(self.param_array[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] [np.put(self._optimizer_copy_, ind, c.finv(self.param_array[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
if self.has_parent() and self.constraints[__fixed__].size != 0: if self.has_parent() and self.constraints[__fixed__].size != 0:
@ -660,7 +660,7 @@ class OptimizationHandlable(Indexable):
return self._optimizer_copy_[fixes] return self._optimizer_copy_[fixes]
elif self._has_fixes(): elif self._has_fixes():
return self._optimizer_copy_[self._fixes_] return self._optimizer_copy_[self._fixes_]
self._transformed = False self._optimizer_copy_transformed = True
return self._optimizer_copy_ return self._optimizer_copy_
@ -672,29 +672,26 @@ class OptimizationHandlable(Indexable):
Also we want to update param_array in here. Also we want to update param_array in here.
""" """
if self.__dict__.get('_optimizer_copy_', None) is None or self.size != self._optimizer_copy_.size: f = None
self._optimizer_copy_ = np.empty(self.size)
self._optimizer_copy_.flat = self.param_array.flat
if self.has_parent() and self.constraints[__fixed__].size != 0: if self.has_parent() and self.constraints[__fixed__].size != 0:
fixes = np.ones(self.size).astype(bool) f = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED f[self.constraints[__fixed__]] = FIXED
self._optimizer_copy_.flat[fixes] = p elif self._has_fixes():
elif self._has_fixes(): self._optimizer_copy_.flat[self._fixes_] = p f = self._fixes_
else: self._optimizer_copy_.flat = p if f is None:
self.param_array.flat = p
self.param_array.flat = self._optimizer_copy_.flat [np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
for c, ind in self.constraints.iteritems() if c != __fixed__]
[np.put(self.param_array, ind, c.f(self._optimizer_copy_.flat[ind])) else:
for c, ind in self.constraints.iteritems() if c != __fixed__] self.param_array.flat[f] = p
[np.put(self.param_array, ind[f[ind]], c.f(self.param_array.flat[ind[f[ind]]]))
self._transformed = True for c, ind in self.constraints.iteritems() if c != __fixed__]
self._optimizer_copy_transformed = False
self._trigger_params_changed() self._trigger_params_changed()
def _get_params_transformed(self): def _get_params_transformed(self):
raise DeprecationWarning, "_get|set_params{_transformed} is deprecated, use self.optimizer array insetad!" raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
# # transformed parameters (apply un-transformation rules) # # transformed parameters (apply un-transformation rules)
# p = self.param_array.copy() # p = self.param_array.copy()
# [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__] # [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
@ -707,7 +704,7 @@ class OptimizationHandlable(Indexable):
# return p # return p
# #
def _set_params_transformed(self, p): def _set_params_transformed(self, p):
raise DeprecationWarning, "_get|set_params{_transformed} is deprecated, use self.optimizer array insetad!" raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
# """ # """
# Set parameters p, but make sure they get transformed before setting. # Set parameters p, but make sure they get transformed before setting.