[pickling] _src -> src

GPy/kern/__init__.py

@@ -1,10 +1,8 @@
 """
 Kernel module the kernels to sit in.
 
-.. automodule:: .src
-   :members:
-   :private-members:
 """
+from . import src
 from .src.kern import Kern
 from .src.add import Add
 from .src.prod import Prod

GPy/kern/src/kern.py

@@ -54,13 +54,22 @@ class Kern(Parameterized):
         self.active_dims = active_dims
         self._all_dims_active = np.atleast_1d(active_dims).astype(int)
 
-        assert self._all_dims_active.size == self.input_dim, "input_dim={} does not match len(active_dim)={}, _all_dims_active={}".format(self.input_dim, self._all_dims_active.size, self._all_dims_active)
+        assert self._all_dims_active.size == self.input_dim, "input_dim={} does not match len(active_dim)={}, active_dim={}".format(self.input_dim, self._all_dims_active.size, self._all_dims_active)
 
         self._sliced_X = 0
         self.useGPU = self._support_GPU and useGPU
 
         from .psi_comp import PSICOMP_GH
-        self.psicomp = PSICOMP_GH()        
+        self.psicomp = PSICOMP_GH()
+
+    @property
+    def _all_dims_active(self):
+        if not hasattr(self, '__all_dims_active'):
+            self.__all_dims_active = np.asanyarray(self.active_dims)
+        return self.__all_dims_active
+    @_all_dims_active.setter
+    def _all_dims_active(self, active_dims):
+        self.__all_dims_active = np.asanyarray(active_dims)
 
     @property
     def _effective_input_dim(self):
@@ -211,15 +220,15 @@ class Kern(Parameterized):
     def get_most_significant_input_dimensions(self, which_indices=None):
         """
         Determine which dimensions should be plotted
-        
+
         Returns the top three most signification input dimensions
-        
+
         if less then three dimensions, the non existing dimensions are
         labeled as None, so for a 1 dimensional input this returns
         (0, None, None).
-        
-        :param which_indices: force the indices to be the given indices. 
-        :type which_indices: int or tuple(int,int) or tuple(int,int,int) 
+
+        :param which_indices: force the indices to be the given indices.
+        :type which_indices: int or tuple(int,int) or tuple(int,int,int)
         """
         if which_indices is None:
             which_indices = np.argsort(self.input_sensitivity())[::-1][:3]
@@ -235,7 +244,7 @@ class Kern(Parameterized):
                 input_1, input_2 = which_indices, None
             except ValueError:
                 # which_indices was a list or array like with only one int
-                input_1, input_2 = which_indices[0], None            
+                input_1, input_2 = which_indices[0], None
         return input_1, input_2, input_3
 
 

GPy/kern/src/rbf.py

@@ -47,12 +47,13 @@ class RBF(Stationary):
         return dc
 
     def __setstate__(self, state):
+        self.use_invLengthscale = False
         return super(RBF, self).__setstate__(state)
 
     def spectrum(self, omega):
         assert self.input_dim == 1 #TODO: higher dim spectra?
         return self.variance*np.sqrt(2*np.pi)*self.lengthscale*np.exp(-self.lengthscale*2*omega**2/2)
-    
+
     def parameters_changed(self):
         if self.use_invLengthscale: self.lengthscale[:] = 1./np.sqrt(self.inv_l+1e-200)
         super(RBF,self).parameters_changed()
@@ -85,7 +86,7 @@ class RBF(Stationary):
 
     def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
         return self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[3:]
-    
+
     def update_gradients_diag(self, dL_dKdiag, X):
         super(RBF,self).update_gradients_diag(dL_dKdiag, X)
         if self.use_invLengthscale: self.inv_l.gradient =self.lengthscale.gradient*(self.lengthscale**3/-2.)