ivec.py 文件源码

def expectation_tv(self, data_list):
    N, F = read_data(data_list, self.nmix, self.ndim)
    nfiles = F.shape[0]
    nframes = N.sum()
    LU = np.zeros((self.nmix, self.tv_dim * (self.tv_dim + 1) // 2))
    RU = np.zeros((self.tv_dim, self.nmix * self.ndim))
    LLK = 0.
    T_invS = self.Tm / self.Sigma
    T_iS_Tt = self.comp_T_invS_Tt()
    parts = 2500  # modify this based on your HW resources (e.g., memory)
    nbatch = int(nfiles / parts + 0.99999)
    for batch in range(nbatch):
      start = batch * parts
      fin = min((batch + 1) * parts, nfiles)
      length = fin - start
      N1 = N[start:fin]
      F1 = F[start:fin]
      L1 = N1.dot(T_iS_Tt)
      B1 = F1.dot(T_invS.T)
      Ex = np.empty((length, self.tv_dim))
      Exx = np.empty((length, self.tv_dim * (self.tv_dim + 1) // 2))
      llk = np.zeros((length, 1))
      for ix in range(length):
        L = np.zeros((self.tv_dim, self.tv_dim))
        L[self.itril] = L1[ix]
        L += np.tril(L, -1).T + self.Im
        Cxx = inv(L)
        B = B1[ix][:, np.newaxis]
        this_Ex = Cxx.dot(B)
        llk[ix] = self.res_llk(this_Ex, B)
        Ex[ix] = this_Ex.T
        Exx[ix] = (Cxx + this_Ex.dot(this_Ex.T))[self.itril]
      RU += Ex.T.dot(F1)
      LU += N1.T.dot(Exx)
      LLK += llk.sum()
    self.Tm = None
    tmp_string = ''.join(random.choices(string.ascii_uppercase + string.digits, k=16))
    tmpfile = self.tmpdir + 'tmat_' + tmp_string + '.h5'
    h5write(tmpfile, LU, 'LU')
    return RU, LLK, nframes