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extend inference X for all gp models

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Zhenwen Dai 2014-11-03 16:04:15 +00:00
parent 3eb2fa3a8f
commit 1840b7e6b8
5 changed files with 78 additions and 37 deletions
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16
GPy/models/bayesian_gplvm.py
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@ -141,22 +141,6 @@ class BayesianGPLVM(SparseGP_MPI):
resolution, ax, marker, s,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
def infer_newX(self, Y_new, optimize=True, ):
"""
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)
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)
def do_test_latents(self, Y):
"""

3
GPy/models/sparse_gplvm.py
View file

@ -26,7 +26,8 @@ class SparseGPLVM(SparseGPRegression):
def parameters_changed(self):
super(SparseGPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
self.X.gradient = self.kern.gradients_X_diag(self.grad_dict['dL_dKdiag'], self.X)
self.X.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,