Workong on doing explicit gradients

GPy/likelihoods/Laplace.py

@@ -97,6 +97,19 @@ class Laplace(likelihood):
 
         a = mdot(dWi_dfhat, Ki, self.f_hat)
         b = np.dot(self.Sigma_tilde, Ki)
+        #dytil_dfhat = np.zeros(self.K.shape)
+        #for col in range(self.N):
+            #for row in range(self.N):
+                #t1 = 0
+                #for l in range(self.N):
+                    #t1 += dWi_dfhat[col, col]*Ki[col,l]*self.f_hat[l, 0]
+                ##t2 = np.zeros((1, self.N))
+                #t2 = np.dot(self.Sigma_tilde, Ki[:, col])
+                ##for k in range(self.N):
+                    ##t2[:] += self.Sigma_tilde[k, k]*Ki[k, col]
+                #dytil_dfhat[row, col] = (t1 + t2)[row]
+        #dytil_dfhat += np.eye(self.N)
+
         dytil_dfhat = - np.dot(dWi_dfhat, np.dot(Ki, self.f_hat)) + np.dot(self.Sigma_tilde, Ki) + np.eye(self.N)
         #dytil_dfhat = - (np.dot(dWi_dfhat, Ki)*self.f_hat[:, None] + np.dot(self.Sigma_tilde, Ki)).sum(-1) + np.eye(self.N)
         self.dytil_dfhat = dytil_dfhat

GPy/testing/laplace_approx.tests.py

@@ -61,7 +61,7 @@ class LaplaceTests(unittest.TestCase):
         real_var = 0.1
         #Start a function, any function
         #self.X = np.linspace(0.0, 10.0, 30)[:, None]
-        self.X = np.random.randn(2,1)
+        self.X = np.random.randn(9,1)
         #self.X = np.ones((10,1))
         Y = np.sin(self.X) + np.random.randn(*self.X.shape)*real_var
         self.Y = Y/Y.max()