very basic GP_regression demo is working

GPy/models/GP_EP.py

@@ -7,7 +7,7 @@ import pylab as pb
 from scipy import stats, linalg
 from .. import kern
 from ..inference.Expectation_Propagation import EP,Full
-from ..inference.likelihoods import likelihood,probit,poisson,gaussian
+from ..inference.likelihoods import likelihood,probit#,poisson,gaussian
 from ..core import model
 from ..util.linalg import pdinv,jitchol
 from ..util.plot import gpplot

GPy/models/GP_regression.py

@@ -29,7 +29,7 @@ class GP_regression(model):
 
     def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.bias(X.shape[1]) + kern.white(X.shape[1])
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1])
 
         # parse arguments
         self.Xslices = Xslices
@@ -103,7 +103,7 @@ class GP_regression(model):
         return dL_dK
 
     def log_likelihood_gradients(self):
-        return self.kern.dK_dtheta(self.X,partial=self.dL_dK())
+        return self.kern.dK_dtheta(partial=self.dL_dK(),X=self.X)
 
     def predict(self,Xnew, slices=None):
         """

GPy/models/__init__.py

@@ -6,6 +6,6 @@ from GP_regression import GP_regression
 from sparse_GP_regression import sparse_GP_regression
 from GPLVM import GPLVM
 from warped_GP import warpedGP
-from simple_GP_EP import GP_EP
+from GP_EP import GP_EP
 from generalized_FITC import generalized_FITC
 from sparse_GPLVM import sparse_GPLVM

GPy/models/generalized_FITC.py

@@ -9,7 +9,7 @@ from .. import kern
 from ..core import model
 from ..util.linalg import pdinv,mdot
 from ..util.plot import gpplot
-from ..inference.Expectation_Propagation import EP,Full,DTC,FITC
+from ..inference.Expectation_Propagation import EP,Full,FITC
 from ..inference.likelihoods import likelihood,probit
 
 class generalized_FITC(model):
@@ -17,14 +17,12 @@ class generalized_FITC(model):
         """
         Naish-Guzman, A. and Holden, S. (2008) implemantation of EP with FITC.
 
-        Arguments
-        ---------
-        X : input observations
-        likelihood : Output's likelihood (likelihood class)
-        kernel : a GPy kernel
-        inducing : Either an array specifying the inducing points location or a scalar defining their number.
-        epsilon_ep : EP convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
-        powerep : Power-EP parameters (eta,delta) - 2x1 numpy array (floats)
+        :param X: input observations
+        :param likelihood: Output's likelihood (likelihood class)
+        :param kernel: a GPy kernel
+        :param inducing:  Either an array specifying the inducing points location or a scalar defining their number.
+        :param epsilon_ep: EP convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
+        :param powerep: Power-EP parameters (eta,delta) - 2x1 numpy array (floats)
         """
         assert isinstance(kernel,kern.kern)
         self.likelihood = likelihood