def pdf_link(self, link_f, y, Y_metadata=None):
        """
        Likelihood function given link(f)

        .. math::
            p(y_{i}|\\lambda(f_{i})) = \\lambda(f_{i})\\exp (-y\\lambda(f_{i}))

        :param link_f: latent variables link(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: likelihood evaluated for this point
        :rtype: float
        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        log_objective = link_f*np.exp(-y*link_f)
        return np.exp(np.sum(np.log(log_objective)))
def pdf_link ( self , link_f , y , Y_metadata = None ) : assert np . atleast_1d ( link_f ) . shape == np . atleast_1d ( y ) . shape log_objective = link_f * np . exp ( - y * link_f ) return np . exp ( np . sum ( np . log ( log_objective ) ) )
Likelihood function given link(f)

        .. math::
            p(y_{i}|\\lambda(f_{i})) = \\lambda(f_{i})\\exp (-y\\lambda(f_{i}))

        :param link_f: latent variables link(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: likelihood evaluated for this point
        :rtype: float