merge with current GPy devel

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.parameterization.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

@@ -2,14 +2,12 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
+import logging
 from .. import kern
 from ..likelihoods import Gaussian
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
-from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
-import logging
-from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
-from GPy.core.parameterization.param import Param
-from GPy.core.parameterization.observable_array import ObsAr
+from GPy.core.parameterization.variational import NormalPosterior, NormalPrior
+from .sparse_gp_minibatch import SparseGPMiniBatch
+from ..core.parameterization.param import Param
 
 class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
     """

GPy/models/dpgplvm.py

@@ -1,10 +1,7 @@
 # Copyright (c) 2015 the GPy Austhors (see AUTHORS.txt)
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import numpy as np
-from .. import kern
 from .bayesian_gplvm import BayesianGPLVM
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
 
 class DPBayesianGPLVM(BayesianGPLVM):
     """

GPy/models/gp_coregionalized_regression.py

@@ -36,7 +36,9 @@ class GPCoregionalizedRegression(GP):
 
         #Kernel
         if kernel is None:
-            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
+            kernel = kern.RBF(X.shape[1]-1)
+            
+            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=1,name=kernel_name)
 
         #Likelihood
         likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)

GPy/models/gp_kronecker_gaussian_regression.py

@@ -2,8 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ..core.model import Model
-from ..core.parameterization import ObsAr
+from ..core import Model
+from paramz import ObsAr
 from .. import likelihoods
 
 class GPKroneckerGaussianRegression(Model):

GPy/models/gp_var_gauss.py

@@ -3,8 +3,6 @@
 
 import numpy as np
 from ..core import GP
-from ..core.parameterization import ObsAr
-from .. import kern
 from ..core.parameterization.param import Param
 from ..inference.latent_function_inference import VarGauss
 

GPy/models/gradient_checker.py

@@ -1,11 +1,11 @@
 # ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from ..core.model import Model
-import itertools
 import numpy
-from ..core.parameterization import Param
 np = numpy
+
+from ..core.parameterization import Param
+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):
@@ -62,7 +62,7 @@ class GradientChecker(Model):
                 grad.randomize()
                 grad.checkgrad(verbose=1)
         """
-        Model.__init__(self, 'GradientChecker')
+        super(GradientChecker, self).__init__(name='GradientChecker')
         if isinstance(x0, (list, tuple)) and names is None:
             self.shapes = [get_shape(xi) for xi in x0]
             self.names = ['X{i}'.format(i=i) for i in range(len(x0))]

GPy/models/mrd.py

@@ -5,18 +5,14 @@ import numpy as np
 import itertools, logging
 
 from ..kern import Kern
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
-from ..core.parameterization import Param, Parameterized
-from ..core.parameterization.observable_array import ObsAr
+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
 from ..inference.latent_function_inference import InferenceMethodList
 from ..likelihoods import Gaussian
 from ..util.initialization import initialize_latent
-from ..core.sparse_gp import SparseGP, GP
-from GPy.core.parameterization.variational import VariationalPosterior
 from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
-from GPy.models.bayesian_gplvm import BayesianGPLVM
-from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
 
 class MRD(BayesianGPLVMMiniBatch):
     """

GPy/models/one_vs_all_classification.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from ..core import GP
 from . import SparseGPClassification
 from .. import likelihoods
 from .. import kern

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.parameterization.variational import NormalPosterior
+        from GPy.core.parameterization.variational import NormalPosterior
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 

GPy/models/sparse_gp_coregionalized_regression.py

@@ -4,7 +4,6 @@
 import numpy as np
 from ..core import SparseGP
 from ..inference.latent_function_inference import VarDTC
-from .. import likelihoods
 from .. import kern
 from .. import util
 
@@ -43,7 +42,9 @@ class SparseGPCoregionalizedRegression(SparseGP):
 
         #Kernel
         if kernel is None:
-            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kern.RBF(X.shape[1]-1), W_rank=1,name=kernel_name)
+            kernel = kern.RBF(X.shape[1]-1)
+            
+            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=1,name=kernel_name)
 
         #Likelihood
         likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)

GPy/models/sparse_gp_minibatch.py

@@ -4,18 +4,15 @@
 from __future__ import print_function
 import numpy as np
 from ..core.parameterization.param import Param
+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
 from .. import likelihoods
-from ..core.parameterization.variational import VariationalPosterior
 
 import logging
-from GPy.inference.latent_function_inference.posterior import Posterior
-from GPy.inference.optimization.stochastics import SparseGPStochastics,\
-    SparseGPMissing
-#no stochastics.py file added! from GPy.inference.optimization.stochastics import SparseGPStochastics,\
-    #SparseGPMissing
+from ..inference.latent_function_inference.posterior import Posterior
+from ..inference.optimization.stochastics import SparseGPStochastics, SparseGPMissing
 logger = logging.getLogger("sparse gp")
 
 class SparseGPMiniBatch(SparseGP):

GPy/models/sparse_gp_regression.py

@@ -3,12 +3,11 @@
 
 
 import numpy as np
-from ..core import SparseGP
 from ..core.sparse_gp_mpi import SparseGP_MPI
 from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
-from ..core.parameterization.variational import NormalPosterior
+from GPy.core.parameterization.variational import NormalPosterior
 
 class SparseGPRegression(SparseGP_MPI):
     """

GPy/models/sparse_gplvm.py

@@ -2,9 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import numpy as np
 import sys
-from GPy.models.sparse_gp_regression import SparseGPRegression
+from .sparse_gp_regression import SparseGPRegression
 
 class SparseGPLVM(SparseGPRegression):
     """

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.parameterization.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
 
@@ -19,7 +19,7 @@ class IBPPosterior(SpikeAndSlabPosterior):
         """
         binary_prob : the probability of the distribution on the slab part.
         """
-        from ..core.parameterization.transformations import Logexp
+        from paramz.transformations import Logexp
         super(IBPPosterior, self).__init__(means, variances, binary_prob, group_spike=True, name=name)
         self.sharedX = sharedX
         if sharedX:
@@ -60,7 +60,7 @@ class IBPPosterior(SpikeAndSlabPosterior):
 class IBPPrior(VariationalPrior):
     def __init__(self, input_dim, alpha =2., name='IBPPrior', **kw):
         super(IBPPrior, self).__init__(name=name, **kw)
-        from ..core.parameterization.transformations import Logexp, __fixed__  
+        from paramz.transformations import Logexp, __fixed__  
         self.input_dim = input_dim
         self.variance = 1.
         self.alpha = Param('alpha', alpha, __fixed__)
@@ -224,4 +224,4 @@ class SSGPLVM(SparseGP_MPI):
         
         
 
-        
+        

GPy/models/ss_mrd.py

@@ -5,7 +5,7 @@ The Maniforld Relevance Determination model with the spike-and-slab prior
 import numpy as np
 from ..core import Model
 from .ss_gplvm import SSGPLVM
-from ..core.parameterization.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
@@ -214,7 +214,7 @@ class SpikeAndSlabPrior_SSMRD(SpikeAndSlabPrior):
 class IBPPrior_SSMRD(VariationalPrior):
     def __init__(self, nModels, input_dim, alpha =2., tau=None, name='IBPPrior', **kw):
         super(IBPPrior_SSMRD, self).__init__(name=name, **kw)
-        from ..core.parameterization.transformations import Logexp, __fixed__  
+        from paramz.transformations import Logexp, __fixed__  
         self.nModels = nModels
         self._b_prob_all = 0.5
         self.input_dim = input_dim