exponential.py 文件源码

def _sample_n(self, n, seed=None):
    shape = array_ops.concat(([n], array_ops.shape(self._lam)), 0)
    # Sample uniformly-at-random from the open-interval (0, 1).
    sampled = random_ops.random_uniform(
        shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
                            self.dtype.as_numpy_dtype(1.)),
        maxval=array_ops.ones((), dtype=self.dtype),
        seed=seed,
        dtype=self.dtype)
    return -math_ops.log(sampled) / self._lam