transformed_distribution.py 文件源码

def log_pdf(self, y, *args, **kwargs):
        """
        :param y: a value sampled from the transformed distribution
        :type y: torch.autograd.Variable

        :returns: the score (the log pdf) of y
        :rtype: torch.autograd.Variable

        Scores the sample by inverting the bijector(s) and computing the score using the score
        of the base distribution and the log det jacobian
        """
        inverses = []
        next_to_invert = y
        for bijector in reversed(self.bijectors):
            inverse = bijector.inverse(next_to_invert)
            inverses.append(inverse)
            next_to_invert = inverse
        log_pdf_base = self.base_dist.log_pdf(inverses[-1], *args, **kwargs)
        log_det_jacobian = self.bijectors[-1].log_det_jacobian(y, *args, **kwargs)
        for bijector, inverse in zip(list(reversed(self.bijectors))[1:], inverses[:-1]):
            log_det_jacobian += bijector.log_det_jacobian(inverse, *args, **kwargs)
        return log_pdf_base - log_det_jacobian