[updates] merged update structure

GPy/kern/_src/hierarchical.py

@@ -10,11 +10,11 @@ class Hierarchical(Kernpart):
     A kernel part which can reopresent a hierarchy of indepencnce: a generalisation of independent_outputs
 
     """
-    def __init__(self,parts):
+    def __init__(self,parts,name='hierarchy'):
         self.levels = len(parts)
         self.input_dim = parts[0].input_dim + 1
         self.num_params = np.sum([k.num_params for k in parts])
-        self.name = 'hierarchy'
+        self.name = name
         self.parts = parts
 
         self.param_starts = np.hstack((0,np.cumsum([k.num_params for k in self.parts[:-1]])))

GPy/kern/_src/rbf.py

@@ -20,8 +20,6 @@ class RBF(Stationary):
     _support_GPU = True
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
         super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
-        self.weave_options = {}
-        self.group_spike_prob = False
         self.psicomp = PSICOMP_RBF()
         if self.useGPU:
             self.psicomp = PSICOMP_RBF_GPU()

GPy/kern/_src/stationary.py

@@ -171,7 +171,8 @@ class Stationary(Kern):
 
         #the lower memory way with a loop
         ret = np.empty(X.shape, dtype=np.float64)
-        [np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q]) for q in xrange(self.input_dim)]
+        for q in xrange(self.input_dim):
+            np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q])
         ret /= self.lengthscale**2
 
         return ret
@@ -309,6 +310,19 @@ class Matern52(Stationary):
 
 
 class ExpQuad(Stationary):
+    """
+    The Exponentiated quadratic covariance function. 
+
+    .. math::
+
+       k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
+
+    notes::
+     - Yes, this is exactly the same as the RBF covariance function, but the
+       RBF implementation also has some features for doing variational kernels
+       (the psi-statistics).
+
+    """
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='ExpQuad'):
         super(ExpQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
 

GPy/kern/_src/trunclinear.py

@@ -3,14 +3,10 @@
 
 
 import numpy as np
-from scipy import weave
 from kern import Kern
-from ...util.linalg import tdot
-from ...util.misc import param_to_array
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 from ...util.caching import Cache_this
-from ...core.parameterization import variational
 from ...util.config import *
 
 class TruncLinear(Kern):