[dir] structure preserved

GPy/models/bayesian_gplvm.py

@@ -5,7 +5,7 @@ import numpy as np
 from .. import kern
 from ..core.sparse_gp_mpi import SparseGP_MPI
 from ..likelihoods import Gaussian
-from ..core.variational import NormalPosterior, NormalPrior
+from GPy.core.parameterization.variational import NormalPosterior, NormalPrior
 from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
 import logging
 

GPy/models/bayesian_gplvm_minibatch.py

@@ -5,7 +5,7 @@ import numpy as np
 import logging
 from .. import kern
 from ..likelihoods import Gaussian
-from ..core.variational import NormalPosterior, NormalPrior
+from GPy.core.parameterization.variational import NormalPosterior, NormalPrior
 from .sparse_gp_minibatch import SparseGPMiniBatch
 from ..core.parameterization.param import Param
 

GPy/models/gp_kronecker_gaussian_regression.py

@@ -2,11 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ..core import ProbabilisticModel
+from ..core import Model
 from paramz import ObsAr
 from .. import likelihoods
 
-class GPKroneckerGaussianRegression(ProbabilisticModel):
+class GPKroneckerGaussianRegression(Model):
     """
     Kronecker GP regression
 
@@ -29,7 +29,7 @@ class GPKroneckerGaussianRegression(ProbabilisticModel):
 
     """
     def __init__(self, X1, X2, Y, kern1, kern2, noise_var=1., name='KGPR'):
-        ProbabilisticModel.__init__(self, name=name)
+        Model.__init__(self, name=name)
         # accept the construction arguments
         self.X1 = ObsAr(X1)
         self.X2 = ObsAr(X2)

GPy/models/gradient_checker.py

@@ -5,7 +5,7 @@ import numpy
 np = numpy
 
 from ..core.parameterization import Param
-from ..core.probabilistic_model import ProbabilisticModel
+from GPy.core.model import Model
 from ..util.block_matrices import get_blocks, get_block_shapes, unblock, get_blocks_3d, get_block_shapes_3d
 
 def get_shape(x):
@@ -21,7 +21,7 @@ def at_least_one_element(x):
 def flatten_if_needed(x):
     return numpy.atleast_1d(x).flatten()
 
-class GradientChecker(ProbabilisticModel):
+class GradientChecker(Model):
 
     def __init__(self, f, df, x0, names=None, *args, **kwargs):
         """

GPy/models/mrd.py

@@ -5,7 +5,7 @@ import numpy as np
 import itertools, logging
 
 from ..kern import Kern
-from ..core.variational import NormalPrior
+from GPy.core.parameterization.variational import NormalPrior
 from ..core.parameterization import Param
 from paramz import ObsAr
 from ..inference.latent_function_inference.var_dtc import VarDTC

GPy/models/sparse_gp_classification.py

@@ -62,7 +62,7 @@ class SparseGPClassificationUncertainInput(SparseGP):
     .. Note:: Multiple independent outputs are allowed using columns of Y
     """
     def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, Y_metadata=None, normalizer=None):
-        from ..core.variational import NormalPosterior
+        from GPy.core.parameterization.variational import NormalPosterior
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 

GPy/models/sparse_gp_minibatch.py

@@ -4,7 +4,7 @@
 from __future__ import print_function
 import numpy as np
 from ..core.parameterization.param import Param
-from ..core.variational import VariationalPosterior
+from GPy.core.parameterization.variational import VariationalPosterior
 from ..core.sparse_gp import SparseGP
 from ..core.gp import GP
 from ..inference.latent_function_inference import var_dtc

GPy/models/sparse_gp_regression.py

@@ -7,7 +7,7 @@ from ..core.sparse_gp_mpi import SparseGP_MPI
 from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
-from ..core.variational import NormalPosterior
+from GPy.core.parameterization.variational import NormalPosterior
 
 class SparseGPRegression(SparseGP_MPI):
     """

GPy/models/ss_gplvm.py

@@ -7,7 +7,7 @@ from ..core.sparse_gp_mpi import SparseGP_MPI
 from .. import kern
 from ..core.parameterization import Param
 from ..likelihoods import Gaussian
-from ..core.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior,VariationalPrior
+from GPy.core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior,VariationalPrior
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 from ..kern.src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU
 

GPy/models/ss_mrd.py

@@ -3,15 +3,15 @@ The Maniforld Relevance Determination model with the spike-and-slab prior
 """
 
 import numpy as np
-from ..core import ProbabilisticModel
+from ..core import Model
 from .ss_gplvm import SSGPLVM
-from ..core.variational import SpikeAndSlabPrior,NormalPosterior,VariationalPrior
+from GPy.core.parameterization.variational import SpikeAndSlabPrior,NormalPosterior,VariationalPrior
 from ..util.misc import param_to_array
 from ..kern import RBF
 from ..core import Param
 from numpy.linalg.linalg import LinAlgError
 
-class SSMRD(ProbabilisticModel):
+class SSMRD(Model):
     
     def __init__(self, Ylist, input_dim, X=None, X_variance=None, Gammas=None, initx = 'PCA_concat', initz = 'permute', 
                  num_inducing=10, Zs=None, kernels=None, inference_methods=None, likelihoods=None, group_spike=True,
@@ -117,13 +117,13 @@ class SSMRD(ProbabilisticModel):
                 Gammas.append(gamma)
         return X, X_variance, Gammas, fracs
 
-    @ProbabilisticModel.optimizer_array.setter
+    @Model.optimizer_array.setter
     def optimizer_array(self, p):
         if self.mpi_comm != None:
             if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
                 self.mpi_comm.Bcast(np.int32(1),root=0)
             self.mpi_comm.Bcast(p, root=0)        
-        ProbabilisticModel.optimizer_array.fset(self,p)
+        Model.optimizer_array.fset(self,p)
         
     def optimize(self, optimizer=None, start=None, **kwargs):
         self._IN_OPTIMIZATION_ = True