[dir] structure preserved

GPy/inference/latent_function_inference/inferenceX.py

@@ -2,8 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ...core import ProbabilisticModel
-from ...core import variational
+from ...core import Model
+from GPy.core.parameterization import variational
 from ...util.linalg import tdot
 
 def infer_newX(model, Y_new, optimize=True, init='L2'):
@@ -26,7 +26,7 @@ def infer_newX(model, Y_new, optimize=True, init='L2'):
 
     return infr_m.X, infr_m
 
-class InferenceX(ProbabilisticModel):
+class InferenceX(Model):
     """
     The model class for inference of new X with given new Y. (replacing the "do_test_latent" in Bayesian GPLVM)
     It is a tiny inference model created from the original GP model. The kernel, likelihood (only Gaussian is supported at the moment) 

GPy/inference/latent_function_inference/var_dtc.py

@@ -4,7 +4,7 @@
 from .posterior import Posterior
 from ...util.linalg import mdot, jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify
 from ...util import diag
-from ...core.variational import VariationalPosterior
+from GPy.core.parameterization.variational import VariationalPosterior
 import numpy as np
 from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)

GPy/inference/latent_function_inference/var_dtc_parallel.py

@@ -4,7 +4,7 @@
 from .posterior import Posterior
 from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri,pdinv
 from ...util import diag
-from ...core.variational import VariationalPosterior
+from GPy.core.parameterization.variational import VariationalPosterior
 import numpy as np
 from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)