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