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

Conflicts:
	GPy/models/GP.py

GPy/models/mrd.py

@@ -41,8 +41,40 @@ class MRD(model):
     :param kernel:
         kernel to use
     """
-    #TODO allow different kernels for different outputs
-    #def __init__(self, *Ylist, **kwargs):
+    def __init__(self,likelihood_list,Q,M=10,names=None,kernels=None,initX='PCA',initz='permute',_debug=False, **kwargs):
+        if names is None:
+            self.names = ["{}".format(i + 1) for i in range(len(likelihood_list))]
+
+        #sort out the kernels
+        if kernels is None:
+            kernels = [None]*len(likelihood_list)
+        elif isinstance(kernels,kern.kern):
+            kernels = [kernels.copy() for i in range(len(likelihood_list))]
+        else:
+            assert len(kernels)==len(likelihood_list), "need one kernel per output"
+            assert all([isinstance(k, kern.kern) for k in kernels]), "invalid kernel object detected!"
+
+        self.Q = Q
+        self.M = M
+        self.N = self.gref.N
+        self.NQ = self.N * self.Q
+        self.MQ = self.M * self.Q
+
+        self._init = True
+        X = self._init_X(initx, likelihood_list)
+        Z = self._init_Z(initz, X)
+        self.bgplvms = [Bayesian_GPLVM(l, k, X=X, Z=Z, M=self.M, **kwargs) for l,k in zip(likelihood_list,kernels)]
+
+        del self._init
+
+        self.gref = self.bgplvms[0]
+        nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
+        self.nparams = nparams.cumsum()
+
+        model.__init__(self) # @UndefinedVariable
+
+
+
     def __init__(self, *likelihood_list, **kwargs):
         if kwargs.has_key("_debug"):
             self._debug = kwargs['_debug']

GPy/models/sparse_GP.py

@@ -234,7 +234,7 @@ class sparse_GP(GP):
                 Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
                 var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
         else:
-            assert which_parts=='all', "swithching out parts of variational kernels is not implemented"
+            # assert which_parts=='all', "swithching out parts of variational kernels is not implemented"
             Kx = self.kern.psi1(self.Z, Xnew, X_variance_new)#, which_parts=which_parts) TODO: which_parts
             mu = np.dot(Kx, self.Cpsi1V)
             if full_cov: