changed the behaviour of pdinv: now returns L and Li as well as the inverse

GPy/kern/finite_dimensional.py

@@ -19,7 +19,7 @@ class finite_dimensional(kernpart):
         self.D = D
         self.F = F
         self.G = G
-        self.G_1 ,tmp = pdinv(G)
+        self.G_1 ,L,Li,logdet = pdinv(G)
         self.n = F.shape[0]
         if weights is not None:
             assert weights.shape==(self.n,)

GPy/models/GP_regression.py

@@ -71,7 +71,7 @@ class GP_regression(model):
     def set_param(self,p):
         self.kern.expand_param(p)
         self.K = self.kern.K(self.X,slices1=self.Xslices)
-        self.Ki,self.hld = pdinv(self.K)
+        self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
 
     def get_param(self):
         return self.kern.extract_param()
@@ -90,7 +90,7 @@ class GP_regression(model):
             return -0.5*np.sum(np.multiply(self.Ki, self.Youter))
 
     def log_likelihood(self):
-        complexity_term = -0.5*self.N*self.D*np.log(2.*np.pi) - self.D*self.hld
+        complexity_term = -0.5*self.N*self.D*np.log(2.*np.pi) - 0.5*self.D*self.K_logdet
         return complexity_term + self._model_fit_term()
 
     def dL_dK(self):

GPy/models/generalized_FITC.py

@@ -62,7 +62,7 @@ class generalized_FITC(model):
     def posterior_param(self):
         self.Knn_diag = self.kernel.Kdiag(self.X)
         self.Kmm = self.kernel.K(self.Z)
-        self.Kmmi, self.Kmm_hld = pdinv(self.Kmm)
+        self.Kmmi, self.Lmm, self.Lmmi, self.Kmm_logdet = pdinv(self.Kmm)
         self.Knm = self.kernel.K(self.X,self.Z)
         self.KmmiKmn = np.dot(self.Kmmi,self.Knm.T)
         self.Qnn = np.dot(self.Knm,self.KmmiKmn)
@@ -72,10 +72,10 @@ class generalized_FITC(model):
         self.Taut = self.ep_approx.tau_tilde/(1.+ self.ep_approx.tau_tilde*self.Diag0)
         self.KmnTaut = self.Knm.T*self.Taut[None,:]
         self.KmnTautKnm = np.dot(self.KmnTaut, self.Knm)
-        self.Woodbury_inv, self.Woodbury_hld = pdinv(self.Kmm + self.KmnTautKnm)
+        self.Woodbury_inv, self.Wood_L, self.Wood_Li, self.Woodbury_logdet = pdinv(self.Kmm + self.KmnTautKnm)
         self.Qnn_diag = self.Knn_diag - np.diag(self.Qnn) + 1./self.ep_approx.tau_tilde
         self.Qi = -np.dot(self.KmnTaut.T, np.dot(self.Woodbury_inv,self.KmnTaut)) + np.diag(self.Taut)
-        self.hld = 0.5*np.sum(np.log(self.Diag0 + 1./self.ep_approx.tau_tilde)) - self.Kmm_hld + self.Woodbury_hld
+        self.hld = 0.5*np.sum(np.log(self.Diag0 + 1./self.ep_approx.tau_tilde)) - 0.5*self.Kmm_logdet + 0.5*self.Woodbury_logdet
 
         self.Diag = self.Diag0/(1.+ self.Diag0 * self.ep_approx.tau_tilde)
         self.P = (self.Diag / self.Diag0)[:,None] * self.Knm

GPy/models/sparse_GP_regression.py

@@ -87,14 +87,10 @@ class sparse_GP_regression(GP_regression):
         self.V = self.beta*self.Y
         self.psi1V = np.dot(self.psi1, self.V)
         self.psi1VVpsi1 = np.dot(self.psi1V, self.psi1V.T)
-        self.Lm = jitchol(self.Kmm)
-        self.Lmi = chol_inv(self.Lm)
-        self.Kmmi = np.dot(self.Lmi.T, self.Lmi)
+        self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
         self.A = mdot(self.Lmi, self.psi2, self.Lmi.T)
         self.B = np.eye(self.M) + self.beta * self.A
-        self.LB = jitchol(self.B)
-        self.LBi = chol_inv(self.LB)
-        self.Bi = np.dot(self.LBi.T, self.LBi)
+        self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
         self.LLambdai = np.dot(self.LBi, self.Lmi)
         self.trace_K = self.psi0 - np.trace(self.A)
         self.LBL_inv = mdot(self.Lmi.T, self.Bi, self.Lmi)
@@ -124,7 +120,7 @@ class sparse_GP_regression(GP_regression):
         """
         A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta))
         B = -0.5*self.beta*self.D*self.trace_K
-        C = -self.D * np.sum(np.log(np.diag(self.LB)))
+        C = -0.5*self.D * self.B_logdet
         D = -0.5*self.beta*self.trYYT
         E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv)
         return A+B+C+D+E

GPy/util/linalg.py

@@ -78,23 +78,23 @@ def jitchol(A,maxtries=5):
 
 def pdinv(A):
     """
-    Arguments
-    ---------
     :param A: A DxD pd numpy array
 
-    Returns
-    -------
-    inv : the inverse of A
-    hld: 0.5* the log of the determinant of A
+    :rval Ai: the inverse of A
+    :rtype Ai: np.ndarray
+    :rval L: the Cholesky decomposition of A
+    :rtype L: np.ndarray
+    :rval Li: the Cholesky decomposition of Ai
+    :rtype Li: np.ndarray
+    :rval logdet: the log of the determinant of A
+    :rtype logdet: float64
     """
     L = jitchol(A)
-    hld = np.sum(np.log(np.diag(L)))
+    logdet = 2.*np.sum(np.log(np.diag(L)))
+    Li = chol_inv(L)
+    Ai = np.dot(Li.T,Li) #TODO: get the flapack routine form multiplying triangular matrices
 
-    inv = sp.lib.lapack.flapack.dpotri(L)[0]
-    # inv = linalg.flapack.dpotri(L,lower = 1)[0]
-    inv = np.tril(inv)+np.tril(inv,-1).T
-
-    return inv, hld
+    return Ai, L, Li, logdet
 
 
 def chol_inv(L):