importance.py 文件源码

def logpdf(self, rowid, targets, constraints=None, inputs=None):
        if constraints is None:
            constraints = {}
        if inputs is None:
            inputs = {}
        # Compute joint probability.
        samples_joint, weights_joint = zip(*[
            self.weighted_sample(
                rowid, [], gu.merged(targets, constraints), inputs)
            for _i in xrange(self.accuracy)
        ])
        logp_joint = gu.logmeanexp(weights_joint)
        # Compute marginal probability.
        samples_marginal, weights_marginal = zip(*[
            self.weighted_sample(rowid, [], constraints, inputs)
            for _i in xrange(self.accuracy)
        ]) if constraints else ({}, [0.])
        if all(isinf(l) for l in weights_marginal):
            raise ValueError('Zero density constraints: %s' % (constraints,))
        logp_constraints = gu.logmeanexp(weights_marginal)
        # Return log ratio.
        return logp_joint - logp_constraints