bmlingam_pm3.py 文件源码

def _causal_effect(
    hparams, mu1, mu1s_, mu2, mu2s_, tau_cmmn, obs1, obs2, Normal, floatX):
    u"""Distribution of observations.
    """
    if hparams['causality'] == [1, 2]:
        # ---- Model 1: x1 -> x2 ----
        x1s = obs1(mu=mu1 + mu1s_)
        b = Normal('b', mu=np.float32(0.), 
                   tau=np.float32(1 / tau_cmmn[1]), dtype=floatX)
        x2s = obs2(mu=mu2 + mu2s_ + b * (x1s - mu1 - mu1s_)) \
              if hparams['subtract_mu_reg'] else \
              obs2(mu=mu2 + mu2s_ + b * x1s)

    else:
        # ---- Model 2: x2 -> x1 ----
        x2s = obs2(mu=mu2 + mu2s_)
        b = Normal('b', mu=np.float32(0.), 
                   tau=np.float32(1 / tau_cmmn[0]), dtype=floatX)
        x1s = obs1(mu=mu1 + mu1s_ + b * (x2s - mu2 - mu2s_)) \
              if hparams['subtract_mu_reg'] else \
              obs1(mu=mu1 + mu1s_ + b * x2s)

    return x1s, x2s, b