hsmm_states.py 文件源码

def aBl_einsum(self):
        if self._aBBl is None:
            sigmas = np.array([[c.sigmas for c in d.components] for d in self.obs_distns])
            Js = -1./(2*sigmas)
            mus = np.array([[c.mu for c in d.components] for d in self.obs_distns])

            # all_likes is T x Nstates x Ncomponents
            all_likes = \
                    (np.einsum('td,td,nkd->tnk',self.data,self.data,Js)
                        - np.einsum('td,nkd,nkd->tnk',self.data,2*mus,Js))
            all_likes += (mus**2*Js - 1./2*np.log(2*np.pi*sigmas)).sum(2)

            # weights is Nstates x Ncomponents
            weights = np.log(np.array([d.weights.weights for d in self.obs_distns]))
            all_likes += weights[na,...]

            # aBl is T x Nstates
            aBl = self._aBl = np.logaddexp.reduce(all_likes, axis=2)
            aBl[np.isnan(aBl).any(1)] = 0.

            aBBl = self._aBBl = np.empty((self.Tblock,self.num_states))
            for idx, (start,stop) in enumerate(self.changepoints):
                aBBl[idx] = aBl[start:stop].sum(0)

        return self._aBBl