dirichlet.py 文件源码

def batch_log_pdf(self, x):
        """
        Evaluates log probability density over one or a batch of samples.

        Each of alpha and x can be either a single value or a batch of values batched along dimension 0.
        If they are both batches, their batch sizes must agree.
        In any case, the rightmost size must agree.

        :param torch.autograd.Variable x: A value (if x.dim() == 1) or or batch of values (if x.dim() == 2).
        :param alpha: A vector of concentration parameters.
        :type alpha: torch.autograd.Variable or None.
        :return: log probability densities of each element in the batch.
        :rtype: torch.autograd.Variable of torch.Tensor of dimension 1.
        """
        alpha = self.alpha.expand(self.shape(x))
        x_sum = torch.sum(torch.mul(alpha - 1, torch.log(x)), -1)
        beta = log_beta(alpha)
        batch_log_pdf_shape = self.batch_shape(x) + (1,)
        return (x_sum - beta).contiguous().view(batch_log_pdf_shape)