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update the set_XY function

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Zhenwen Dai 2014-11-03 17:26:31 +00:00
parent 105a6c5377
commit c885af7fbb
1 changed files with 44 additions and 16 deletions
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60
GPy/core/gp.py
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@ -93,21 +93,51 @@ class GP(Model):
self.link_parameter(self.kern)
self.link_parameter(self.likelihood)
def set_XY(self, X=None, Y=None):
"""
Set the input / output of the model
:param X: input observations
:param Y: output observations
"""
self.update_model(False)
if Y is not None:
if self.normalizer is not None:
self.normalizer.scale_by(Y)
self.Y_normalized = ObsAr(self.normalizer.normalize(Y))
self.Y = Y
else:
self.Y = ObsAr(Y)
self.Y_normalized = self.Y
if X is not None:
if self.X in self.parameters:
# LVM models
from ..core.parameterization.variational import VariationalPosterior
if isinstance(self.X, VariationalPosterior):
assert isinstance(X, type(self.X), "The given X must have the same type as the X in the model!")
self.unlink_parameter(self.X)
self.X = X
self.link_parameters(self.X)
else:
self.unlink_parameter(self.X)
from ..core import Param
self.X = Param('latent mean',X)
self.link_parameters(self.X)
else:
self.X = ObsAr(X)
self.update_model(True)
def set_X(self,X):
# TODO: it does not work with BGPLVM
if isinstance(X, ObsAr):
self.X = X
else:
self.X = ObsAr(X)
"""
Set the input of the model
"""
self.set_XY(X=X)
def set_Y(self,Y):
if self.normalizer is not None:
self.normalizer.scale_by(Y)
self.Y_normalized = ObsAr(self.normalizer.normalize(Y))
self.Y = Y
else:
self.Y = ObsAr(Y)
self.Y_normalized = self.Y
"""
Set the input of the model
"""
self.set_XY(Y=Y)
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.Y_metadata)
@ -359,14 +389,12 @@ class GP(Model):
"""
Infer the distribution of X for the new observed data *Y_new*.
:param model: the GPy model used in inference
:type model: GPy.core.Model
:param Y_new: the new observed data for inference
:type Y_new: numpy.ndarray
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the estimated posterior distribution of X and the model that optimize X
:rtype: (GPy.core.parameterization.variational.VariationalPosterior, GPy.core.Model)
:return: a tuple containing the posterior estimation of X and the model that optimize X
:rtype: (GPy.core.parameterization.variational.VariationalPosterior or numpy.ndarray, GPy.core.Model)
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)