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fixed technical description of gradients_X()

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Eric Kalosa-Kenyon 2020-01-14 11:52:03 -08:00
parent 585d9cc92b
commit 3c80c6e30f
1 changed files with 1 additions and 1 deletions
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doc/source/tuto_creating_new_kernels.rst
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@ -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)