X2 = X
grad = np.zeros_like(X)
if hasattr(X, "values"):X = X.values //remove the GPy wrapping to make passing into weave safe
if hasattr(X2, "values"):X2 = X2.values
stationary_cython.grad_X(X.shape[0], X.shape[1], X2.shape[0], X, X2, tmp, grad)
return grad/self.lengthscale**2
def gradients_X_diag(self, dL_dKdiag, X):
After Change
if X2 is None:
tmp = tmp + tmp.T
X2 = X
X, X2 = np.ascontiguousarray(X), np.ascontiguousarray(X2)
grad = np.zeros(X.shape)
stationary_cython.grad_X(X.shape[0], X.shape[1], X2.shape[0], X, X2, tmp, grad)
return grad/self.lengthscale**2
