def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
        """
        Gradient of the log likelihood function at y, given link(f) w.r.t link(f)

        .. math::
            \\frac{d \\ln p(y_{i}|\lambda(f_{i}))}{d\\lambda(f)} = \\frac{1}{\\lambda(f)} - y_{i}

        :param link_f: latent variables (f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param Y_metadata: Y_metadata which is not used in exponential distribution
        :returns: gradient of likelihood evaluated at points
        :rtype: Nx1 array

        """
        grad = 1./link_f - y
        #grad = y/(link_f**2) - 1./link_f
        return grad
dataset/ETHPy150Open SheffieldML/GPy/GPy/likelihoods/exponential.py/Exponential.dlogpdf_dlink/original