From 3c80c6e30f5db8c05547f0ffd186e257074103a9 Mon Sep 17 00:00:00 2001 From: Eric Kalosa-Kenyon Date: Tue, 14 Jan 2020 11:52:03 -0800 Subject: [PATCH] fixed technical description of gradients_X() --- doc/source/tuto_creating_new_kernels.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/tuto_creating_new_kernels.rst b/doc/source/tuto_creating_new_kernels.rst index 05e32af0..386c2991 100644 --- a/doc/source/tuto_creating_new_kernels.rst +++ b/doc/source/tuto_creating_new_kernels.rst @@ -182,7 +182,7 @@ Computes the derivative of the likelihood with respect to the inputs The partial derivative matrix is, in this case, comes out as an :math:`n \times q` np.array. :: def gradients_X(self,dL_dK,X,X2): - """derivative of the likelihood matrix with respect to X, calculated using dK_dX""" + """derivative of the likelihood with respect to X, calculated using dL_dK*dK_dX""" if X2 is None: X2 = X dist2 = np.square((X-X2.T)/self.lengthscale)