rename models to _models and import models in models.py

GPy/_models/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+# from gp_regression import GPRegression; _gp_regression = gp_regression ; del gp_regression 
+# from gp_classification import GPClassification; _gp_classification = gp_classification ; del gp_classification 
+# from sparse_gp_regression import SparseGPRegression; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression 
+# from svigp_regression import SVIGPRegression; _svigp_regression = svigp_regression ; del svigp_regression 
+# from sparse_gp_classification import SparseGPClassification; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification 
+# from fitc_classification import FITCClassification; _fitc_classification = fitc_classification ; del fitc_classification 
+# from gplvm import GPLVM; _gplvm = gplvm ; del gplvm 
+# from bcgplvm import BCGPLVM; _bcgplvm = bcgplvm; del bcgplvm
+# from sparse_gplvm import SparseGPLVM; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm 
+# from warped_gp import WarpedGP; _warped_gp = warped_gp ; del warped_gp 
+# from bayesian_gplvm import BayesianGPLVM; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm 
+# from mrd import MRD; _mrd = mrd ; del mrd 
+# from gradient_checker import GradientChecker; _gradient_checker = gradient_checker ; del gradient_checker 
+# from gp_multioutput_regression import GPMultioutputRegression; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression 
+# from sparse_gp_multioutput_regression import SparseGPMultioutputRegression; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression 
+

GPy/_models/bayesian_gplvm.py

@@ -2,14 +2,14 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from ..core import SparseGP
+from ..core.sparse_gp import SparseGP
 from ..likelihoods import Gaussian
 from .. import kern
 import itertools
 from matplotlib.colors import colorConverter
 from GPy.inference.optimization import SCG
 from GPy.util import plot_latent, linalg
-from GPy.models.gplvm import GPLVM
+from .gplvm import GPLVM
 from GPy.util.plot_latent import most_significant_input_dimensions
 from matplotlib import pyplot
 

GPy/_models/gp_regression.py

@@ -2,7 +2,6 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import numpy as np
 from ..core import GP
 from .. import likelihoods
 from .. import kern

GPy/_models/gplvm.py

@@ -4,15 +4,11 @@
 
 import numpy as np
 import pylab as pb
-import sys, pdb
 from .. import kern
-from ..core import Model
-from ..util.linalg import pdinv, PCA
-from ..core.priors import Gaussian as Gaussian_prior
+from ..core import priors
 from ..core import GP
 from ..likelihoods import Gaussian
 from .. import util
-from GPy.util import plot_latent
 
 
 class GPLVM(GP):
@@ -34,12 +30,13 @@ class GPLVM(GP):
             kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
         likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
         GP.__init__(self, X, likelihood, kernel, normalize_X=False)
-        self.set_prior('.*X', Gaussian_prior(0, 1))
+        self.set_prior('.*X', priors.Gaussian(0, 1))
         self.ensure_default_constraints()
 
     def initialise_latent(self, init, input_dim, Y):
         Xr = np.random.randn(Y.shape[0], input_dim)
         if init == 'PCA':
+            from ..util.linalg import PCA
             PC = PCA(Y, input_dim)[0]
             Xr[:PC.shape[0], :PC.shape[1]] = PC
         return Xr
@@ -62,15 +59,15 @@ class GPLVM(GP):
     def jacobian(self,X):
         target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         for i in range(self.output_dim):
-        	target[:,:,i] = self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
+            target[:,:,i] = self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
         return target
    
     def magnification(self,X):
         target=np.zeros(X.shape[0])
         J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
-    	J=self.jacobian(X)
+        J=self.jacobian(X)
         for i in range(X.shape[0]):
-		    target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
+            target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
         return target
 
     def plot(self):

GPy/_models/mrd.py

@@ -9,8 +9,8 @@ from GPy.util.linalg import PCA
 import numpy
 import itertools
 import pylab
-from GPy.kern.kern import kern
-from GPy.models.bayesian_gplvm import BayesianGPLVM
+from ..kern import kern
+from bayesian_gplvm import BayesianGPLVM
 
 class MRD(Model):
     """

GPy/_models/sparse_gplvm.py

@@ -5,8 +5,8 @@
 import numpy as np
 import pylab as pb
 import sys, pdb
-from GPy.models.sparse_gp_regression import SparseGPRegression
-from GPy.models.gplvm import GPLVM
+from sparse_gp_regression import SparseGPRegression
+from gplvm import GPLVM
 # from .. import kern
 # from ..core import model
 # from ..util.linalg import pdinv, PCA

GPy/models.py

@@ -0,0 +1,22 @@
+'''
+Created on 14 Nov 2013
+
+@author: maxz
+'''
+
+from _models.bayesian_gplvm import BayesianGPLVM
+from _models.gp_regression import GPRegression
+from _models.gp_classification import GPClassification#; _gp_classification = gp_classification ; del gp_classification 
+from _models.sparse_gp_regression import SparseGPRegression#; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression 
+from _models.svigp_regression import SVIGPRegression#; _svigp_regression = svigp_regression ; del svigp_regression 
+from _models.sparse_gp_classification import SparseGPClassification#; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification 
+from _models.fitc_classification import FITCClassification#; _fitc_classification = fitc_classification ; del fitc_classification 
+from _models.gplvm import GPLVM#; _gplvm = gplvm ; del gplvm 
+from _models.bcgplvm import BCGPLVM#; _bcgplvm = bcgplvm; del bcgplvm
+from _models.sparse_gplvm import SparseGPLVM#; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm 
+from _models.warped_gp import WarpedGP#; _warped_gp = warped_gp ; del warped_gp 
+from _models.bayesian_gplvm import BayesianGPLVM#; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm 
+from _models.mrd import MRD#; _mrd = mrd; del mrd 
+from _models.gradient_checker import GradientChecker#; _gradient_checker = gradient_checker ; del gradient_checker 
+from _models.gp_multioutput_regression import GPMultioutputRegression#; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression 
+from _models.sparse_gp_multioutput_regression import SparseGPMultioutputRegression#; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression 

GPy/models/__init__.py

@@ -1,19 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-from gp_regression import GPRegression; _gp_regression = gp_regression ; del gp_regression 
-from gp_classification import GPClassification; _gp_classification = gp_classification ; del gp_classification 
-from sparse_gp_regression import SparseGPRegression; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression 
-from svigp_regression import SVIGPRegression; _svigp_regression = svigp_regression ; del svigp_regression 
-from sparse_gp_classification import SparseGPClassification; _sparse_gp_classification = sparse_gp_classification ; del sparse_gp_classification 
-from fitc_classification import FITCClassification; _fitc_classification = fitc_classification ; del fitc_classification 
-from gplvm import GPLVM; _gplvm = gplvm ; del gplvm 
-from bcgplvm import BCGPLVM; _bcgplvm = bcgplvm; del bcgplvm
-from sparse_gplvm import SparseGPLVM; _sparse_gplvm = sparse_gplvm ; del sparse_gplvm 
-from warped_gp import WarpedGP; _warped_gp = warped_gp ; del warped_gp 
-from bayesian_gplvm import BayesianGPLVM; _bayesian_gplvm = bayesian_gplvm ; del bayesian_gplvm 
-from mrd import MRD; _mrd = mrd ; del mrd 
-from gradient_checker import GradientChecker; _gradient_checker = gradient_checker ; del gradient_checker 
-from gp_multioutput_regression import GPMultioutputRegression; _gp_multioutput_regression = gp_multioutput_regression ; del gp_multioutput_regression 
-from sparse_gp_multioutput_regression import SparseGPMultioutputRegression; _sparse_gp_multioutput_regression = sparse_gp_multioutput_regression ; del sparse_gp_multioutput_regression 
-