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implement the gradient_X for standard_periodic kernel

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Zhenwen Dai 2016-03-29 14:45:55 +01:00
parent 1b85b45a7e
commit fd2fc557c6
1 changed files with 11 additions and 8 deletions
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19
GPy/kern/src/standard_periodic.py
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@ -155,11 +155,14 @@ class StdPeriodic(Kern):
self.period.gradient = 0 self.period.gradient = 0
self.lengthscale.gradient = 0 self.lengthscale.gradient = 0
# def gradients_X(self, dL_dK, X, X2=None): def gradients_X(self, dL_dK, X, X2=None):
# """derivative of the covariance matrix with respect to X.""" K = self.K(X, X2)
# if X2 is None:
# raise NotImplemented("Periodic kernel: dK_dX not implemented") dL_dK = dL_dK+dL_dK.T
# X2 = X
# def gradients_X_diag(self, dL_dKdiag, X): dX = -np.pi*((dL_dK*K)[:,:,None]*np.sin(2*np.pi/self.period*(X[:,None,:] - X2[None,:,:]))/(2.*np.square(self.lengthscale)*self.period)).sum(1)
# return dX
# raise NotImplemented("Periodic kernel: dKdiag_dX not implemented")
def gradients_X_diag(self, dL_dKdiag, X):
return np.zeros(X.shape)