ldaseqmodel.py 文件源码

def update_phi(self, doc_number, time):
        """
        Update variational multinomial parameters, based on a document and a time-slice.
        This is done based on the original Blei-LDA paper, where:
        log_phi := beta * exp(?(gamma)), over every topic for every word.

        TODO: incorporate lee-sueng trick used in **Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001**.
        """
        num_topics = self.lda.num_topics
        # digamma values
        dig = np.zeros(num_topics)

        for k in range(0, num_topics):
            dig[k] = digamma(self.gamma[k])

        n = 0   # keep track of iterations for phi, log_phi
        for word_id, count in self.doc:
            for k in range(0, num_topics):
                self.log_phi[n][k] = dig[k] + self.lda.topics[word_id][k]

            log_phi_row = self.log_phi[n]
            phi_row = self.phi[n]

            # log normalize
            v = log_phi_row[0]
            for i in range(1, len(log_phi_row)):
                v = np.logaddexp(v, log_phi_row[i])

            # subtract every element by v
            log_phi_row = log_phi_row - v 
            phi_row = np.exp(log_phi_row)
            self.log_phi[n] = log_phi_row
            self.phi[n] = phi_row
            n +=1 # increase iteration

        return self.phi, self.log_phi