crowd_model.py 文件源码

def eval_F(list_data, worker, mu, C, W):
        """
        F = log N(T, mu, C) + SUM q() log Ber(L_ij | T)
        list_il can be from different datasets:
        list_il = list: each elem is a list from a dataset
        """    

        res = scipy.stats.multivariate_normal.logpdf(W, mean = mu, cov = C, allow_singular = True)

        for dt, data in enumerate(list_data):
            # dataset dt: sen = W[2*dt], fpr = W[2*dt+1]
            sen, fpr = W[2*dt], W[2*dt+1]
            if worker in data.dic_wl:
             for (item, label) in data.dic_wl[worker]:
                # prob of being 1
                qz = data.qz[item]
                res += qz     * np.log( Ber(S(sen), label) )
                #res += qz     * ( label*np.log(S(sen)) + (1-label)*np.log(S(sen))  )
                res += (1-qz) * np.log( Ber(S(fpr), label) )


        return res