mmsb_vb.py 文件源码

def run_e_step(self):
        """ compute variational expectations
        """
        ll = 0.

        for p in range(self.N):
            for q in range(self.N):
                new_phi = np.zeros(self.K)

                for g in range(self.K):
                    new_phi[g] = np.exp(psi(self.gamma[p,g])-psi(np.sum(self.gamma[p,:]))) * np.prod(( (self.B[g,:]**self.Y[p,q])
                        * ((1.-self.B[g,:])**(1.-self.Y[p,q])) )
                        ** self.phi[q,p,:] )
                self.phi[p,q,:] = new_phi/np.sum(new_phi)

                new_phi = np.zeros(self.K)
                for h in range(self.K):
                    new_phi[h] = np.exp(psi(self.gamma[q,h])-psi(np.sum(self.gamma[q,:]))) * np.prod(( (self.B[:,h]**self.Y[p,q])
                        * ((1.-self.B[:,h])**(1.-self.Y[p,q])) )
                        ** self.phi[p,q,:] )
                self.phi[q,p,:] = new_phi/np.sum(new_phi)

                for k in range(self.K):
                    self.gamma[p,k] = self.alpha[k] + np.sum(self.phi[p,:,k]) + np.sum(self.phi[:,p,k])
                    self.gamma[q,k] = self.alpha[k] + np.sum(self.phi[q,:,k]) + np.sum(self.phi[:,q,k])

        return ll