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[minor] minor changes

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This commit is contained in:
mzwiessele 2014-08-05 08:27:40 -07:00
parent ece85f02fe
commit 93b92111f8
3 changed files with 4 additions and 30 deletions
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2
GPy/core/model.py
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@ -349,7 +349,7 @@ class Model(Parameterized):
numerical_gradient = (f1 - f2) / (2 * step) numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind] if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
else: ratio = (f1 - f2) / (2 * step * gradient[xind]) else: ratio = (f1 - f2) / (2 * step * gradient[xind])
difference = np.abs((f1 - f2) / 2 / step - gradient[xind]) difference = np.abs(numerical_gradient - gradient[xind])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance: if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[nind]) formatted_name = "\033[92m {0} \033[0m".format(names[nind])

28
GPy/core/parameterization/parameter_core.py
View file

@ -699,36 +699,10 @@ class OptimizationHandlable(Indexable):
def _get_params_transformed(self): def _get_params_transformed(self):
raise DeprecationWarning, "_get|set_params{_optimizer_copy_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)
# p = self.param_array.copy()
# [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
# if self.has_parent() and self.constraints[__fixed__].size != 0:
# fixes = np.ones(self.size).astype(bool)
# fixes[self.constraints[__fixed__]] = FIXED
# return p[fixes]
# elif self._has_fixes():
# return p[self._fixes_]
# return p
# #
def _set_params_transformed(self, p): def _set_params_transformed(self, p):
raise DeprecationWarning, "_get|set_params{_optimizer_copy_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.
# This means, the optimizer sees p, whereas the model sees transformed(p),
# such that, the parameters the model sees are in the right domain.
# """
# if not(p is self.param_array):
# if self.has_parent() and self.constraints[__fixed__].size != 0:
# fixes = np.ones(self.size).astype(bool)
# fixes[self.constraints[__fixed__]] = FIXED
# self.param_array.flat[fixes] = p
# elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
# else: self.param_array.flat = p
# [np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
# for c, ind in self.constraints.iteritems() if c != __fixed__]
# self._trigger_params_changed()
def _trigger_params_changed(self, trigger_parent=True): def _trigger_params_changed(self, trigger_parent=True):
""" """
First tell all children to update, First tell all children to update,
@ -736,7 +710,7 @@ class OptimizationHandlable(Indexable):
If trigger_parent is True, we will tell the parent, otherwise not. If trigger_parent is True, we will tell the parent, otherwise not.
""" """
[p._trigger_params_changed(trigger_parent=False) for p in self.parameters] [p._trigger_params_changed(trigger_parent=False) for p in self.parameters if not p.is_fixed]
self.notify_observers(None, None if trigger_parent else -np.inf) self.notify_observers(None, None if trigger_parent else -np.inf)
def _size_transformed(self): def _size_transformed(self):

2
GPy/kern/_src/add.py
View file

@ -40,7 +40,7 @@ class Add(CombinationKernel):
return reduce(np.add, (p.Kdiag(X) for p in which_parts)) return reduce(np.add, (p.Kdiag(X) for p in which_parts))
def update_gradients_full(self, dL_dK, X, X2=None): def update_gradients_full(self, dL_dK, X, X2=None):
[p.update_gradients_full(dL_dK, X, X2) for p in self.parts] [p.update_gradients_full(dL_dK, X, X2) for p in self.parts if not p.is_fixed]
def update_gradients_diag(self, dL_dK, X): def update_gradients_diag(self, dL_dK, X):
[p.update_gradients_diag(dL_dK, X) for p in self.parts] [p.update_gradients_diag(dL_dK, X) for p in self.parts]