quantized_distribution.py 文件源码

def _sample_n(self, n, seed=None):
    lower_cutoff = self._lower_cutoff
    upper_cutoff = self._upper_cutoff
    with ops.name_scope("transform"):
      n = ops.convert_to_tensor(n, name="n")
      x_samps = self.distribution.sample_n(n=n, seed=seed)
      ones = array_ops.ones_like(x_samps)

      # Snap values to the intervals (j - 1, j].
      result_so_far = math_ops.ceil(x_samps)

      if lower_cutoff is not None:
        result_so_far = math_ops.select(result_so_far < lower_cutoff,
                                        lower_cutoff * ones, result_so_far)

      if upper_cutoff is not None:
        result_so_far = math_ops.select(result_so_far > upper_cutoff,
                                        upper_cutoff * ones, result_so_far)

      return result_so_far