def E_logpX( self, SS ):
    lpX = np.zeros( self.K )
    for k in range(self.K):
      if SS.N[k] == 0:
        continue
      lpX[k] = SS.N[k]*self.comp[k].ElogdetLam() - \
                 self.comp[k].E_traceLam( SS.xxT[k] )
    return 0.5*np.sum( lpX ) - 0.5*np.sum(SS.N)*self.D*LOGTWOPI
dataset/ETHPy150Open daeilkim/refinery/refinery/bnpy/bnpy-dev/bnpy/obsmodel/ZMGaussObsModel.py/ZMGaussObsModel.E_logpX/original