air.py 文件源码

def step_infer(self, r, q, y, *params):
        '''Step inference function for IRVI.inference scan.

        Args:
            r: theano randomstream variable
            q: T.tensor. Current approximate posterior parameters
            y: T.tensor. Data sample
            params: list of shared variables
        Returns:
            q: T.tensor. New approximate posterior parameters
            cost: T.scalar float. Negative lower bound of current parameters
        '''

        model = self.model
        prior_params = model.get_prior_params(*params)

        h        = (r <= q[None, :, :]).astype(floatX)
        py       = model.p_y_given_h(h, *params)
        log_py_h = -model.conditional.neg_log_prob(y[None, :, :], py)
        log_ph   = -model.prior.step_neg_log_prob(h, *prior_params)
        log_qh   = -model.posterior.neg_log_prob(h, q[None, :, :])
        log_p     = log_py_h + log_ph - log_qh
        w_tilde = get_w_tilde(log_p)
        cost    = -log_p.mean()
        q_ = (w_tilde[:, :, None] * h).sum(axis=0)
        q  = self.inference_rate * q_ + (1 - self.inference_rate) * q
        return q, cost