gaussian_components.py 文件源码

def log_marg_k(self, k):
        """
        Return the log marginal probability of the data vectors assigned to
        component `k`.

        The log marginal probability p(X) = p(x_1, x_2, ..., x_N) is returned
        for the data vectors assigned to component `k`. See (266) in Murphy's
        bayesGauss notes, p. 21.
        """
        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)
        i = np.arange(1, self.D + 1, dtype=np.int)
        return (
            - self.counts[k]*self.D/2.*self._cached_log_pi
            + self.D/2.*math.log(self.prior.k_0) - self.D/2.*math.log(k_N)
            + self.prior.v_0/2.*slogdet(self.prior.S_0)[1]
            - v_N/2.*slogdet(S_N)[1]
            + np.sum(
                self._cached_gammaln_by_2[v_N + 1 - i] - 
                self._cached_gammaln_by_2[self.prior.v_0 + 1 - i]
                )
            )