Exception fixes for Python 3 compat

GPy/kern/_src/ODE_UY.py

@@ -114,7 +114,7 @@ class ODE_UY(Kern):
                 elif i==1:
                     Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
                 else:
-                    raise ValueError, "invalid input/output index"
+                    raise ValueError("invalid input/output index")
         #Kdiag[slices[0][0]]+= self.variance_U   #matern32 diag
         #Kdiag[slices[1][0]]+= self.variance_U*self.variance_Y*(k1+k2+k3)  #  diag
         return Kdiag

GPy/kern/_src/ODE_UYC.py

@@ -115,7 +115,7 @@ class ODE_UYC(Kern):
                 elif i==1:
                     Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
                 else:
-                    raise ValueError, "invalid input/output index"
+                    raise ValueError("invalid input/output index")
         #Kdiag[slices[0][0]]+= self.variance_U   #matern32 diag
         #Kdiag[slices[1][0]]+= self.variance_U*self.variance_Y*(k1+k2+k3)  #  diag
         return Kdiag

GPy/kern/_src/ODE_st.py

@@ -135,7 +135,7 @@ class ODE_st(Kern):
                     Kdiag[s1]+= b**2*k1 - 2*a*c*k2 + a**2*k3 + c**2*vyt*vyx
                     #Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
                 else:
-                    raise ValueError, "invalid input/output index"
+                    raise ValueError("invalid input/output index")
 
         return Kdiag
         

GPy/kern/_src/ODE_t.py

@@ -85,7 +85,7 @@ class ODE_t(Kern):
                             Kdiag[s1]+= k1 + vyt+self.ubias
                             #Kdiag[s1]+= Vu*Vy*(k1+k2+k3)
                         else:
-                            raise ValueError, "invalid input/output index"
+                            raise ValueError("invalid input/output index")
 
                 return Kdiag
 

GPy/kern/_src/add.py

@@ -111,7 +111,7 @@ class Add(CombinationKernel):
                 psi2 += np.einsum('nm,no->mo',tmp1,tmp2)+np.einsum('nm,no->mo',tmp2,tmp1)
                 #(tmp1[:, :, None] * tmp2[:, None, :]) + (tmp2[:, :, None] * tmp1[:, None, :])
             else:
-                raise NotImplementedError, "psi2 cannot be computed for this kernel"
+                raise NotImplementedError("psi2 cannot be computed for this kernel")
         return psi2
 
     def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):

GPy/kern/_src/psi_comp/__init__.py

@@ -17,7 +17,7 @@ class PSICOMP_RBF(Pickleable):
         elif isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
             return ssrbf_psi_comp.psicomputations(variance, lengthscale, Z, variational_posterior)
         else:
-            raise ValueError, "unknown distriubtion received for psi-statistics"
+            raise ValueError("unknown distriubtion received for psi-statistics")
 
     @Cache_this(limit=2, ignore_args=(0,1,2,3))
     def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
@@ -26,7 +26,7 @@ class PSICOMP_RBF(Pickleable):
         elif isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
             return ssrbf_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior)
         else:
-            raise ValueError, "unknown distriubtion received for psi-statistics"
+            raise ValueError("unknown distriubtion received for psi-statistics")
 
     def _setup_observers(self):
         pass
@@ -40,7 +40,7 @@ class PSICOMP_Linear(Pickleable):
         elif isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
             return sslinear_psi_comp.psicomputations(variance, Z, variational_posterior)
         else:
-            raise ValueError, "unknown distriubtion received for psi-statistics"
+            raise ValueError("unknown distriubtion received for psi-statistics")
 
     @Cache_this(limit=2, ignore_args=(0,1,2,3))
     def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, Z, variational_posterior):
@@ -49,7 +49,7 @@ class PSICOMP_Linear(Pickleable):
         elif isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
             return sslinear_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, Z, variational_posterior)
         else:
-            raise ValueError, "unknown distriubtion received for psi-statistics"
+            raise ValueError("unknown distriubtion received for psi-statistics")
 
     def _setup_observers(self):
         pass

GPy/kern/_src/stationary.py

@@ -65,10 +65,10 @@ class Stationary(Kern):
         self.link_parameters(self.variance, self.lengthscale)
 
     def K_of_r(self, r):
-        raise NotImplementedError, "implement the covariance function as a fn of r to use this class"
+        raise NotImplementedError("implement the covariance function as a fn of r to use this class")
 
     def dK_dr(self, r):
-        raise NotImplementedError, "implement derivative of the covariance function wrt r to use this class"
+        raise NotImplementedError("implement derivative of the covariance function wrt r to use this class")
 
     @Cache_this(limit=5, ignore_args=())
     def K(self, X, X2=None):

GPy/kern/_src/symbolic.py

@@ -11,7 +11,7 @@ class Symbolic(Kern, Symbolic_core):
     def __init__(self, input_dim, k=None, output_dim=1, name='symbolic', parameters=None, active_dims=None, operators=None, func_modules=[]):
 
         if k is None:
-            raise ValueError, "You must provide an argument for the covariance function."
+            raise ValueError("You must provide an argument for the covariance function.")
 
         Kern.__init__(self, input_dim, active_dims, name=name)
         kdiag = k