gamma.py 文件源码

def _sample_alpha(self, n=1):
        eps = 1e-5
        loglikelihood = lambda alpha: (alpha - 1) * np.log(self.a_0+eps) + alpha * self.c_0 * np.log(self.beta+eps) \
                                      - self.b_0 * np.log(special.gamma(alpha))
        likelihood = lambda alpha: np.exp(loglikelihood(alpha))
        stop = 15
        alpha_space = np.linspace(0, stop, num=old_div(stop, 0.001))
        alpha_dist = likelihood(alpha_space)
        alpha_dist = old_div(alpha_dist, alpha_dist.sum())
        return np.random.choice(a=alpha_space, p=alpha_dist, size=n)