Merge branch 'params' of github.com:SheffieldML/GPy into params

GPy/core/parameterization/param.py

@@ -86,6 +86,9 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parentable
         if self._gradient_ is None:
             self._gradient_ = numpy.zeros(self._realshape_)
         return self._gradient_
+    @gradient.setter
+    def gradient(self, val):
+        self.gradient[:] = val
         
     #===========================================================================
     # Pickling operations

GPy/inference/latent_function_inference/posterior.py

@@ -81,13 +81,16 @@ class Posterior(object):
     def covariance(self):
         if self._covariance is None:
             #LiK, _ = dtrtrs(self.woodbury_chol, self._K, lower=1)
-            self._covariance = self._K - self._K.dot(self.woodbury_inv).dot(self._K)
+            self._covariance = np.tensordot(np.dot(np.atleast_3d(self.woodbury_inv).T, self._K), self._K, [1,0]).T
+            #self._covariance = self._K - self._K.dot(self.woodbury_inv).dot(self._K)
         return self._covariance
 
     @property
     def precision(self):
         if self._precision is None:
-            self._precision, _, _, _ = pdinv(self.covariance)
+            self._precision = np.zeros(np.atleast_3d(self.covariance).shape) # if one covariance per dimension
+            for p in xrange(self.covariance.shape[-1]):
+                self._precision[:,:,p] = pdinv(self.covariance[:,:,p])[0]
         return self._precision
 
     @property

GPy/util/warping_functions.py

@@ -3,8 +3,6 @@
 
 
 import numpy as np
-import scipy as sp
-import pylab as plt
 
 class WarpingFunction(object):
     """
@@ -39,6 +37,7 @@ class WarpingFunction(object):
     def plot(self, psi, xmin, xmax):
         y = np.arange(xmin, xmax, 0.01)
         f_y = self.f(y, psi)
+        from matplotlib import pyplot as plt
         plt.figure()
         plt.plot(y, f_y)
         plt.xlabel('y')