labels.py 文件源码

def resample(self):
        al, o = np.log(self.alpha_0), self.obs_distn
        self.z = ma.masked_array(self.z,mask=np.zeros(self.z.shape))
        model = self.model

        for n in np.random.permutation(self.data.shape[0]):
            # mask out n
            self.z.mask[n] = True

            # form the scores and sample them
            ks = list(model._get_occupied())
            scores = np.array([
                np.log(model._get_counts(k))+ o.log_predictive(self.data[n],model._get_data_withlabel(k)) \
                        for k in ks] + [al + o.log_marginal_likelihood(self.data[n])])

            idx = sample_discrete_from_log(scores)
            if idx == scores.shape[0]-1:
                self.z[n] = self._new_label(ks)
            else:
                self.z[n] = ks[idx]

            # sample
            # note: the mask gets fixed by assigning into the array
            self.z[n] = sample_discrete_from_log(np.array(scores))