

def gradients_X(self, dL_dK, X, X2, target):
    'derivative of the covariance matrix with respect to X.'
    AX = np.dot(target, self.transform)
    if (X2 is None):
        X2 = X
        ZX2 = AX
    else:
        AX2 = np.dot(X2, self.transform)
    self.k.gradients_X(dL_dK, X, X2, target)
    self.k.gradients_X(dL_dK, AX, X2, target)
    self.k.gradients_X(dL_dK, X, AX2, target)
    self.k.gradients_X(dL_dK, AX, AX2, target)

dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/src/todo/symmetric.py Symmetric.gradients_X/VarMisuse@26/53 `X`->`target`