DiagGaussObsModel.py 文件源码

def _E_logL(self, k=None):
        '''
        Returns
        -------
        E_logL : 1D array, size D
        '''
        if k == 'all':
            # retVec : K x D
            retVec = LOGTWO - np.log(self.Post.beta.copy())  # no strided!
            retVec += digamma(0.5 * self.Post.nu)[:, np.newaxis]
            return retVec
        elif k is None:
            nu = self.Prior.nu
            beta = self.Prior.beta
        else:
            nu = self.Post.nu[k]
            beta = self.Post.beta[k]
        return LOGTWO - np.log(beta) + digamma(0.5 * nu)