svgp working with reordered chols

GPy/inference/latent_function_inference/svgp.py

@@ -24,8 +24,6 @@ class SVGP(LatentFunctionInference):
 
         if np.any(np.isinf(Si)):
             raise ValueError("Cholesky representation unstable")
-            #S = S + np.eye(S.shape[0])*1e-5*np.max(np.max(S))
-            #Si, Lnew, _,_ = linalg.pdinv(S)
 
         #compute mean function stuff
         if mean_function is not None:
@@ -35,27 +33,21 @@ class SVGP(LatentFunctionInference):
             prior_mean_u = np.zeros((num_inducing, num_outputs))
             prior_mean_f = np.zeros((num_data, num_outputs))
 
-
         #compute kernel related stuff
         Kmm = kern.K(Z)
         Kmn = kern.K(Z, X)
         Knn_diag = kern.Kdiag(X)
-        Kmmi, Lm, Lmi, logdetKmm = linalg.pdinv(Kmm)
+        Lm = linalg.jitchol(Kmm)
+        logdetKmm = 2.*np.sum(np.log(np.diag(Lm)))
+        Kmmi, _ = linalg.dpotri(Lm)
 
         #compute the marginal means and variances of q(f)
-        A = np.dot(Kmmi, Kmn)
+        A, _ = linalg.dpotrs(Lm, Kmn)
         mu = prior_mean_f + np.dot(A.T, q_u_mean - prior_mean_u)
         LA = L.reshape(-1, num_inducing).dot(A).reshape(num_outputs, num_inducing, num_data)
-        #LA = np.empty((num_outputs, num_inducing, num_data))
-        #Af = np.asfortranarray(A)
-        #for Li, LAi in zip(L, LA):
-            #LAi[:,:] = dtrmm(1., Li.T, Af, side=0, lower=0, trans_a=1, overwrite_b=0)
-            #stop
-        #assert np.allclose(LA, LA_)
-
+        #TODO? possibly use dtrmm for the above line?
         v = (Knn_diag - np.sum(A*Kmn,0))[:,None] + np.sum(np.square(LA),1).T
 
-
         #compute the KL term
         Kmmim = np.dot(Kmmi, q_u_mean)
         KLs = -0.5*logdetS -0.5*num_inducing + 0.5*logdetKmm + 0.5*np.sum(Kmmi[None,:,:]*S,1).sum(1) + 0.5*np.sum(q_u_mean*Kmmim,0)
@@ -90,7 +82,7 @@ class SVGP(LatentFunctionInference):
         #derivatives of expected likelihood, assuming zero mean function
         Adv = A[None,:,:]*dF_dv.T[:,None,:] # As if dF_Dv is diagonal, D, M, N
         Admu = A.dot(dF_dmu)
-        Adv = np.ascontiguousarray(Adv) # makes for faster operations later...
+        Adv = np.ascontiguousarray(Adv) # makes for faster operations later...(inc dsymm)
         AdvA = np.dot(Adv.reshape(-1, num_data),A.T).reshape(num_outputs, num_inducing, num_inducing )
         tmp = np.sum([np.dot(a,s) for a, s in zip(AdvA, S)],0).dot(Kmmi)
         dF_dKmm = -Admu.dot(Kmmim.T) + AdvA.sum(0) - tmp - tmp.T