Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

GPy/kern/constructors.py

@@ -600,3 +600,20 @@ def ODE_1(input_dim=1, varianceU=1.,  varianceY=1., lengthscaleU=None,  lengthsc
     """
     part = parts.ODE_1.ODE_1(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
     return kern(input_dim, [part])
+
+def ODE_UY(input_dim=2, varianceU=1.,  varianceY=1., lengthscaleU=None,  lengthscaleY=None):
+    """
+    kernel resultiong from a first order ODE with OU driving GP
+    :param input_dim: the number of input dimension, has to be equal to one
+    :type input_dim: int
+    :param input_lengthU: the number of input U length
+    :param varianceU: variance of the driving GP
+    :type varianceU: float
+    :param varianceY: 'variance' of the transfer function
+    :type varianceY: float
+    :param lengthscaleY: 'lengthscale' of the transfer function
+    :type lengthscaleY: float
+    :rtype: kernel object
+    """
+    part = parts.ODE_UY.ODE_UY(input_dim, varianceU, varianceY, lengthscaleU, lengthscaleY)
+    return kern(input_dim, [part])

GPy/kern/parts/__init__.py

@@ -14,6 +14,7 @@ import Matern32
 import Matern52
 import mlp
 import ODE_1
+import ODE_UY
 import periodic_exponential
 import periodic_Matern32
 import periodic_Matern52

GPy/models/__init__.py

@@ -1,18 +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
-from gp_classification import GPClassification
-from sparse_gp_regression import SparseGPRegression
-from svigp_regression import SVIGPRegression
-from sparse_gp_classification import SparseGPClassification
-from fitc_classification import FITCClassification
-from gplvm import GPLVM
-from bcgplvm import BCGPLVM
-from sparse_gplvm import SparseGPLVM
-from warped_gp import WarpedGP
-from bayesian_gplvm import BayesianGPLVM
-from mrd import MRD
-from gradient_checker import GradientChecker
-from gp_multioutput_regression import GPMultioutputRegression
-from sparse_gp_multioutput_regression import SparseGPMultioutputRegression
+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/util/warping_functions.py

@@ -222,7 +222,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         """
 
 
-        mpsi = psi.coSpy()
+        mpsi = psi.copy()
         d = psi[-1]
         mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)