ugs.py 文件源码

def word_log_likelihood(self):
        n_k = numpy.sum(self._n_kd, axis=1)
        assert(len(n_k) == self._K)

        log_likelihood = self._K * scipy.special.gammaln(self._V * self._eta)
        for topic_index in xrange(self._K):
            log_likelihood -= scipy.special.gammaln(self._V * self._eta + n_k[topic_index])
            for word_index in xrange(self._V):
                if self._n_kv[topic_index, word_index] > 0:
                    log_likelihood += scipy.special.gammaln(self._n_kv[topic_index, word_index] + self._eta) - scipy.special.gammaln(self._eta)

        return log_likelihood