student_t.py 文件源码

def _sample_n(self, n, seed=None):
    # The sampling method comes from the fact that if:
    #   X ~ Normal(0, 1)
    #   Z ~ Chi2(df)
    #   Y = X / sqrt(Z / df)
    # then:
    #   Y ~ StudentT(df).
    shape = array_ops.concat([[n], self.batch_shape()], 0)
    normal_sample = random_ops.random_normal(shape, dtype=self.dtype, seed=seed)
    df = self.df * array_ops.ones(self.batch_shape(), dtype=self.dtype)
    gamma_sample = random_ops.random_gamma(
        [n],
        0.5 * df,
        beta=0.5,
        dtype=self.dtype,
        seed=distribution_util.gen_new_seed(seed, salt="student_t"))
    samples = normal_sample * math_ops.rsqrt(gamma_sample / df)
    return samples * self.scale + self.loc  # Abs(scale) not wanted.