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changed ordering of explanation to get to the point fast and provide additional details after

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Eric Kalosa-Kenyon 2020-01-08 16:10:58 -08:00
parent c58104c943
commit 585d9cc92b
1 changed files with 4 additions and 3 deletions
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7
doc/source/tuto_creating_new_kernels.rst
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@ -179,9 +179,7 @@ Computes the derivative of the likelihood with respect to the inputs
\frac{\partial L}{\partial K} \frac{\partial K}{\partial X}
The partial derivative matrix is, in this case, comes out as an :math:`n \times q` np.array.
Were the number of parameters to be larger than 1 or the number of dimensions likewise any larger
than 1, the calculated partial derivitive would be a 3- or 4-tensor. ::
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"""
@ -191,6 +189,9 @@ than 1, the calculated partial derivitive would be a 3- or 4-tensor. ::
dK_dX = -self.variance*self.power * (X-X2.T)/self.lengthscale**2 * (1 + dist2/2./self.lengthscale)**(-self.power-1)
return np.sum(dL_dK*dK_dX,1)[:,None]
Were the number of parameters to be larger than 1 or the number of dimensions likewise any larger
than 1, the calculated partial derivitive would be a 3- or 4-tensor.
:py:func:`~GPy.kern.src.kern.Kern.gradients_X_diag` ``(self,dL_dKdiag,X)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~