igmm.py 文件源码

def log_marg(self):
        """Return log marginal of data and component assignments: p(X, z)"""

        # Log probability of component assignment P(z|alpha)
        # Equation (10) in Wood and Black, 2008
        # Use \Gamma(n) = (n - 1)!
        facts_ = gammaln(self.components.counts[:self.components.K])
        facts_[self.components.counts[:self.components.K] == 0] = 0  # definition of log(0!)
        log_prob_z = (
            (self.components.K - 1)*math.log(self.alpha) + gammaln(self.alpha)
            - gammaln(np.sum(self.components.counts[:self.components.K])
            + self.alpha) + np.sum(facts_)
            )

        log_prob_X_given_z = self.components.log_marg()

        return log_prob_z + log_prob_X_given_z