nest2pos.py 文件源码

def compute_weights(data, Nlive):
    """Returns log_ev, log_wts for the log-likelihood samples in data,
    assumed to be a result of nested sampling with Nlive live points."""

    start_data=np.concatenate(([float('-inf')], data[:-Nlive]))
    end_data=data[-Nlive:]

    log_wts=np.zeros(data.shape[0])

    log_vols_start=np.cumsum(np.ones(len(start_data)+1)*np.log1p(-1./Nlive))-np.log1p(-1./Nlive)
    log_vols_end=np.zeros(len(end_data))
    log_vols_end[-1]=np.NINF
    log_vols_end[0]=log_vols_start[-1]+np.log1p(-1.0/Nlive)
    for i in range(len(end_data)-1):
        log_vols_end[i+1]=log_vols_end[i]+np.log1p(-1.0/(Nlive-i))

    log_likes = np.concatenate((start_data,end_data,[end_data[-1]]))

    log_vols=np.concatenate((log_vols_start,log_vols_end))
    log_ev = log_integrate_log_trap(log_likes, log_vols)

    log_dXs = logsubexp(log_vols[:-1], log_vols[1:])
    log_wts = log_likes[1:-1] + log_dXs[:-1]

    log_wts -= log_ev

    return log_ev, log_wts