quantized_distribution.py 文件源码

def _log_cdf(self, y):
    lower_cutoff = self._lower_cutoff
    upper_cutoff = self._upper_cutoff

    # Recall the promise:
    # cdf(y) := P[Y <= y]
    #         = 1, if y >= upper_cutoff,
    #         = 0, if y < lower_cutoff,
    #         = P[X <= y], otherwise.

    # P[Y <= j] = P[floor(Y) <= j] since mass is only at integers, not in
    # between.
    j = math_ops.floor(y)

    result_so_far = self.distribution.log_cdf(j)

    # Broadcast, because it's possible that this is a single distribution being
    # evaluated on a number of samples, or something like that.
    j += array_ops.zeros_like(result_so_far)

    # Re-define values at the cutoffs.
    if lower_cutoff is not None:
      neg_inf = -np.inf * array_ops.ones_like(result_so_far)
      result_so_far = math_ops.select(j < lower_cutoff, neg_inf, result_so_far)
    if upper_cutoff is not None:
      result_so_far = math_ops.select(j >= upper_cutoff,
                                      array_ops.zeros_like(result_so_far),
                                      result_so_far)

    return result_so_far