fixed a bug in Neil's otherwise tidy hetero kernel.

GPy/kern/kern.py

@@ -717,7 +717,12 @@ def kern_test(kern, X=None, X2=None, verbose=False):
 
     if verbose:
         print("Checking gradients of K(X, X) wrt X.")
-    result = Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=verbose)
+    try:
+        result = Kern_check_dK_dX(kern, X=X, X2=None).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("dK_dX not implemented for " + kern.name)
     if result and verbose:
         print("Check passed.")
     if not result:
@@ -728,7 +733,12 @@ def kern_test(kern, X=None, X2=None, verbose=False):
 
     if verbose:
         print("Checking gradients of K(X, X2) wrt X.")
-    result = Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=verbose)
+    try:
+        result = Kern_check_dK_dX(kern, X=X, X2=X2).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("dK_dX not implemented for " + kern.name)
     if result and verbose:
         print("Check passed.")
     if not result:
@@ -739,7 +749,12 @@ def kern_test(kern, X=None, X2=None, verbose=False):
 
     if verbose:
         print("Checking gradients of Kdiag(X) wrt X.")
-    result = Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=verbose)
+    try:
+        result = Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=verbose)
+    except NotImplementedError:
+        result=True
+        if verbose:
+            print("dK_dX not implemented for " + kern.name)
     if result and verbose:
         print("Check passed.")
     if not result:

GPy/kern/parts/hetero.py

@@ -10,9 +10,12 @@ import GPy
 
 class Hetero(Kernpart):
     """
-    TODO: Need to constrain the function outputs positive (still thinking of best way of doing this!!! Yes, intend to use transformations, but what's the *best* way). Currently just squaring output.
+    TODO: Need to constrain the function outputs
+    positive (still thinking of best way of doing this!!! Yes, intend to use
+    transformations, but what's the *best* way). Currently just squaring output.
 
-    Heteroschedastic noise which depends on input location. See, for example, this paper by Goldberg et al.
+    Heteroschedastic noise which depends on input location. See, for example,
+    this paper by Goldberg et al.
 
     .. math::
 
@@ -20,15 +23,15 @@ class Hetero(Kernpart):
 
        where :math:`\sigma^2(x)` is a function giving the variance  as a function of input space and :math:`\delta_{i,j}` is the Kronecker delta function.
 
-        The parameters are the parameters of \sigma^2(x) which is a
+    The parameters are the parameters of \sigma^2(x) which is a
-        function that can be specified by the user, by default an
+    function that can be specified by the user, by default an
-        multi-layer peceptron is used.
+    multi-layer peceptron is used.
 
-        :param input_dim: the number of input dimensions
+    :param input_dim: the number of input dimensions
-        :type input_dim: int 
+    :type input_dim: int
-        :param mapping: the mapping that gives the lengthscale across the input space (by default GPy.mappings.MLP is used with 20 hidden nodes).
+    :param mapping: the mapping that gives the lengthscale across the input space (by default GPy.mappings.MLP is used with 20 hidden nodes).
-        :type mapping: GPy.core.Mapping
+    :type mapping: GPy.core.Mapping
-        :rtype: Kernpart object
+    :rtype: Kernpart object
 
     See this paper:
 
@@ -66,7 +69,7 @@ class Hetero(Kernpart):
 
     def K(self, X, X2, target):
         """Return covariance between X and X2."""
-        if X2==None or X2 is X:
+        if (X2 is None) or (X2 is X):
             target[np.diag_indices_from(target)] += self._Kdiag(X)
 
     def Kdiag(self, X, target):
@@ -79,13 +82,13 @@ class Hetero(Kernpart):
 
     def dK_dtheta(self, dL_dK, X, X2, target):
         """Derivative of the covariance with respect to the parameters."""
-        if X2==None or X2 is X:
+        if (X2 is None) or (X2 is X):
             dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]
             self.dKdiag_dtheta(dL_dKdiag, X, target)
 
     def dKdiag_dtheta(self, dL_dKdiag, X, target):
         """Gradient of diagonal of covariance with respect to parameters."""
-        target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None], X)*self.mapping.f(X)
+        target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)
 
     def dK_dX(self, dL_dK, X, X2, target):
         """Derivative of the covariance matrix with respect to X."""

GPy/kern/parts/kernpart.py

@@ -58,6 +58,8 @@ class Kernpart(object):
         raise NotImplementedError
     def dK_dX(self, dL_dK, X, X2, target):
         raise NotImplementedError
+    def dKdiag_dX(self, dL_dK, X, target):
+        raise NotImplementedError
 
 
 
@@ -97,6 +99,9 @@ class Kernpart_stationary(Kernpart):
         # wrt lengthscale is 0.
         target[0] += np.sum(dL_dKdiag)
 
+    def dKdiag_dX(self, dL_dK, X, target):
+        pass # true for all stationary kernels
+
 
 class Kernpart_inner(Kernpart):
     def __init__(self,input_dim):