student_t.py 文件源码

def _sample_n(self, n, seed=None):
    # The sampling method comes from the well known fact that if X ~ Normal(0,
    # 1), and Z ~ Chi2(df), then X / sqrt(Z / df) ~ StudentT(df).
    shape = array_ops.concat(0, ([n], self.batch_shape()))
    normal_sample = random_ops.random_normal(
        shape, dtype=self.dtype, seed=seed)
    half = constant_op.constant(0.5, self.dtype)
    df = self.df * array_ops.ones(self.batch_shape(), dtype=self.dtype)
    gamma_sample = random_ops.random_gamma(
        [n,], half * df, beta=half, dtype=self.dtype,
        seed=distribution_util.gen_new_seed(seed, salt="student_t"))
    samples = normal_sample / math_ops.sqrt(gamma_sample / df)
    return samples * self.sigma + self.mu