bug fix: slicing was not checking dimensions

GPy/kern/_src/kern.py

@@ -21,14 +21,37 @@ class Kern(Parameterized):
         The base class for a kernel: a positive definite function
         which forms of a covariance function (kernel).
 
+        input_dim:
+
+            is the number of dimensions to work on. Make sure to give the 
+            tight dimensionality of inputs.
+            You moset likely want this to be the integer telling the number of 
+            input dimensions of the kernel.
+            If this is not an integer (!) we will work on the whole input matrix X,
+            and not check whether dimensions match or not (!).
+
+        active_dims:
+
+            is the active_dimensions of inputs X we will work on.
+            All kernels will get sliced Xes as inputs, if active_dims is not None
+            if active_dims is None, slicing is switched off and all X will be passed through as given.
+
         :param int input_dim: the number of input dimensions to the function
-        :param array-like|slice active_dims: list of indices on which dimensions this kernel works on
+        :param array-like|slice|None active_dims: list of indices on which dimensions this kernel works on, or none if no slicing
 
         Do not instantiate.
         """
         super(Kern, self).__init__(name=name, *a, **kw)
-        self.active_dims = active_dims# if active_dims is not None else slice(0, input_dim, 1)
+        try:
-        self.input_dim = input_dim
+            self.input_dim = int(input_dim)
+            self.active_dims = active_dims if active_dims is not None else slice(0, input_dim, 1)
+        except TypeError:
+            # input_dim is something else then an integer
+            self.input_dim = input_dim
+            if active_dims is not None:
+                print "WARNING: given input_dim={} is not an integer and active_dims={} is given, switching off slicing"
+            self.active_dims = None
+
         if self.active_dims is not None and self.input_dim is not None:
             assert isinstance(self.active_dims, (slice, list, tuple, np.ndarray)), 'active_dims needs to be an array-like or slice object over dimensions, {} given'.format(self.active_dims.__class__)
             if isinstance(self.active_dims, slice):
@@ -46,9 +69,7 @@ class Kern(Parameterized):
 
     @Cache_this(limit=10)
     def _slice_X(self, X):
-        if self.active_dims is not None:
+        return X[:, self.active_dims]
-            return X[:, self.active_dims]
-        return X
 
     def K(self, X, X2):
         """

GPy/kern/_src/kernel_slice_operations.py

@@ -34,7 +34,7 @@ class _Slice_wrap(object):
         self.k = k
         self.shape = X.shape
         if (self.k.active_dims is not None) and (self.k._sliced_X == 0):
-            #assert X.shape[1] > len(np.r_[self.k.active_dims]), "At least {} dimensional X needed".format(len(np.r_[self.k.active_dims]))
+            assert X.shape[1] >= len(np.r_[self.k.active_dims]), "At least {} dimensional X needed, X.shape={!s}".format(len(np.r_[self.k.active_dims]), X.shape)
             self.X = self.k._slice_X(X)
             self.X2 = self.k._slice_X(X2) if X2 is not None else X2
             self.ret = True