gaussian_components.py 文件源码

def rand_k(self, k):
        """
        Return a random mean vector and covariance matrix from the posterior
        NIW distribution for component `k`.
        """
        k_N = self.prior.k_0 + self.counts[k]
        v_N = self.prior.v_0 + self.counts[k]
        m_N = self.m_N_numerators[k]/k_N
        S_N = self.S_N_partials[k] - k_N*np.outer(m_N, m_N)
        sigma = np.linalg.solve(cholesky(S_N).T, np.eye(self.D))   # don't understand this step
        sigma = wishart.iwishrnd(sigma, v_N, sigma)
        mu = np.random.multivariate_normal(m_N, sigma/k_N)
        return mu, sigma