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add trigger update to set_{X,Y,Z}

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This commit is contained in:
Zhenwen Dai 2015-03-13 09:47:36 +00:00
parent c6cf0bc121
commit 7f30fef698
2 changed files with 12 additions and 12 deletions
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16
GPy/core/gp.py
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@ -89,7 +89,7 @@ class GP(Model):
self.link_parameter(self.kern)
self.link_parameter(self.likelihood)
def set_XY(self, X=None, Y=None):
def set_XY(self, X=None, Y=None, trigger_update=True):
"""
Set the input / output data of the model
This is useful if we wish to change our existing data but maintain the same model
@ -99,7 +99,7 @@ class GP(Model):
:param Y: output observations
:type Y: np.ndarray
"""
self.update_model(False)
if trigger_update: self.update_model(False)
if Y is not None:
if self.normalizer is not None:
self.normalizer.scale_by(Y)
@ -123,26 +123,26 @@ class GP(Model):
self.link_parameters(self.X)
else:
self.X = ObsAr(X)
self.update_model(True)
self._trigger_params_changed()
if trigger_update: self.update_model(True)
if trigger_update: self._trigger_params_changed()
def set_X(self,X):
def set_X(self,X, trigger_update=True):
"""
Set the input data of the model
:param X: input observations
:type X: np.ndarray
"""
self.set_XY(X=X)
self.set_XY(X=X, trigger_update=trigger_update)
def set_Y(self,Y):
def set_Y(self,Y, trigger_update=True):
"""
Set the output data of the model
:param X: output observations
:type X: np.ndarray
"""
self.set_XY(Y=Y)
self.set_XY(Y=Y, trigger_update=trigger_update)
def parameters_changed(self):
"""

8
GPy/core/sparse_gp.py
View file

@ -63,14 +63,14 @@ class SparseGP(GP):
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)
def set_Z(self, Z):
self.update_model(False)
def set_Z(self, Z, trigger_update=True):
if trigger_update: self.update_model(False)
self.unlink_parameter(self.Z)
from ..core import Param
self.Z = Param('inducing inputs',Z)
self.link_parameter(self.Z, index=0)
self.update_model(True)
self._trigger_params_changed()
if trigger_update: self.update_model(True)
if trigger_update: self._trigger_params_changed()
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata)