[dir] structure preserved

GPy/__init__.py

@@ -15,7 +15,8 @@ from . import kern
 from . import plotting
 
 # Direct imports for convenience:
-from .core import ProbabilisticModel, priors
+from .core import Model
+from GPy.core.parameterization import priors
 from paramz import Param, Parameterized, ObsAr, transformations as constraints
 
 from .__version__ import __version__

GPy/core/__init__.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from .probabilistic_model import ProbabilisticModel
+from GPy.core.model import Model
 from .parameterization import Param, Parameterized
 
 from .gp import GP

GPy/core/gp.py

@@ -3,19 +3,19 @@
 
 import numpy as np
 from .. import kern
-from .probabilistic_model import ProbabilisticModel
+from GPy.core.model import Model
 from paramz import ObsAr
 from .mapping import Mapping
 from .. import likelihoods
 from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
-from .variational import VariationalPosterior
+from GPy.core.parameterization.variational import VariationalPosterior
 
 import logging
 import warnings
 from GPy.util.normalizer import MeanNorm
 logger = logging.getLogger("GP")
 
-class GP(ProbabilisticModel):
+class GP(Model):
     """
     General purpose Gaussian process model
 
@@ -541,7 +541,7 @@ class GP(ProbabilisticModel):
         :param optimize: whether to optimize the location of new X (True by default)
         :type optimize: boolean
         :return: a tuple containing the posterior estimation of X and the model that optimize X
-        :rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` and numpy.ndarray, :class:`~GPy.core.probabilistic_model.Model`)
+        :rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` and numpy.ndarray, :class:`~GPy.core.model.Model`)
         """
         from ..inference.latent_function_inference.inferenceX import infer_newX
         return infer_newX(self, Y_new, optimize=optimize)

GPy/core/model.py

@@ -1,12 +1,12 @@
 # Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 from .parameterization.priorizable import Priorizable
-from paramz import Model
+from paramz import Model as ParamzModel
 
-class ProbabilisticModel(Model, Priorizable):
+class Model(ParamzModel, Priorizable):
 
     def __init__(self, name):
-        super(ProbabilisticModel, self).__init__(name)  # Parameterized.__init__(self)
+        super(Model, self).__init__(name)  # Parameterized.__init__(self)
 
     def log_likelihood(self):
         raise NotImplementedError("this needs to be implemented to use the model class")

GPy/core/parameterization/__init__.py

@@ -2,4 +2,5 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from .param import Param
-from .parameterized import Parameterized
+from .parameterized import Parameterized
+from paramz import transformations

GPy/core/parameterization/priors.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 from scipy.special import gammaln, digamma
-from ..util.linalg import pdinv
+from ...util.linalg import pdinv
 from paramz.domains import _REAL, _POSITIVE
 import warnings
 import weakref

GPy/core/parameterization/variational.py

@@ -5,8 +5,8 @@ Created on 6 Nov 2013
 '''
 
 import numpy as np
-from .parameterization.parameterized import Parameterized
-from .parameterization.param import Param
+from .parameterized import Parameterized
+from .param import Param
 from paramz.transformations import Logexp, Logistic,__fixed__
 
 class VariationalPrior(Parameterized):

GPy/core/sparse_gp.py

@@ -6,7 +6,7 @@ from .gp import GP
 from .parameterization.param import Param
 from ..inference.latent_function_inference import var_dtc
 from .. import likelihoods
-from .variational import VariationalPosterior
+from GPy.core.parameterization.variational import VariationalPosterior
 
 import logging
 logger = logging.getLogger("sparse gp")

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)

GPy/kern/src/psi_comp/__init__.py

@@ -3,7 +3,7 @@
 
 from paramz.core.pickleable import Pickleable
 from paramz.caching import Cache_this
-from ....core import variational
+from GPy.core.parameterization import variational
 #from linear_psi_comp import LINEAr
 
 class PSICOMP(Pickleable):

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

GPy/plotting/gpy_plot/plot_util.py

@@ -334,7 +334,7 @@ def x_frame1D(X,plot_limits=None,resolution=None):
     """
     assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
     if plot_limits is None:
-        from ...core.variational import VariationalPosterior
+        from GPy.core.parameterization.variational import VariationalPosterior
         if isinstance(X, VariationalPosterior):
             xmin,xmax = X.mean.min(0),X.mean.max(0)
         else:

GPy/testing/examples_tests.py

@@ -25,7 +25,7 @@ def model_checkgrads(model):
     return model.checkgrad(step=1e-4)
 
 def model_instance(model):
-    return isinstance(model, GPy.core.probabilistic_model.Model)
+    return isinstance(model, GPy.core.model.Model)
 
 def flatten_nested(lst):
     result = []

GPy/testing/gp_tests.py

@@ -5,7 +5,7 @@ Created on 4 Sep 2015
 '''
 import unittest
 import numpy as np, GPy
-from ..core.variational import NormalPosterior
+from GPy.core.parameterization.variational import NormalPosterior
 
 class Test(unittest.TestCase):
 

GPy/testing/kernel_tests.py

@@ -16,7 +16,7 @@ except ImportError:
     config.set('cython', 'working', 'False')
 
 
-class Kern_check_model(GPy.core.ProbabilisticModel):
+class Kern_check_model(GPy.core.Model):
     """
     This is a dummy model class used as a base class for checking that the
     gradients of a given kernel are implemented correctly. It enables
@@ -456,7 +456,7 @@ class KernelTestsProductWithZeroValues(unittest.TestCase):
 class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
 
     def setUp(self):
-        from GPy.core.variational import NormalPosterior
+        from GPy.core.parameterization.variational import NormalPosterior
         N,M,Q = 100,20,3
 
         X = np.random.randn(N,Q)

GPy/testing/mapping_tests.py

@@ -5,7 +5,7 @@ import unittest
 import numpy as np
 import GPy
 
-class MappingGradChecker(GPy.core.ProbabilisticModel):
+class MappingGradChecker(GPy.core.Model):
     """
     This class has everything we need to check the gradient of a mapping. It
     implement a simple likelihood which is a weighted sum of the outputs of the

GPy/testing/pickle_tests.py

@@ -9,7 +9,7 @@ import pickle
 import numpy as np
 import tempfile
 from GPy.examples.dimensionality_reduction import mrd_simulation
-from GPy.core.variational import NormalPosterior
+from GPy.core.parameterization.variational import NormalPosterior
 from GPy.models.gp_regression import GPRegression
 from functools import reduce
 

GPy/testing/rv_transformation_tests.py

@@ -10,12 +10,12 @@ import scipy.stats as st
 import GPy
 
 
-class TestModel(GPy.core.ProbabilisticModel):
+class TestModel(GPy.core.Model):
     """
     A simple GPy model with one parameter.
     """
     def __init__(self, theta=1.):
-        GPy.core.ProbabilisticModel.__init__(self, 'test_model')
+        GPy.core.Model.__init__(self, 'test_model')
         theta = GPy.core.Param('theta', theta)
         self.link_parameter(theta)