def setHQem(nu): # rule 2D matrix; vk 2D data = si.io.loadmat('fmin.mat', squeeze_me=True, struct_as_record=False) t_Q = tm.time() #clock:matlab function, return the date and time WEIGHT_INDEX = 5 vo = data['vobj'] Ny = data['y'].shape[0] Nv = data['y'].shape[1] rule = data['EM_iter'].rule vk = data['vk'] noise_var = data['pixelnoisevar'] p_theta_eta = data['p_theta_eta'] y = data['y'] Nzi = data['EM_iter'].rule.shape[0] tilde_b = data['tilde_b'] eta = data['eta'] # print (type(vo[0].flags[0])) Nc = vo[0].cbar.shape[0] #vo, a cell array, each element is a virus object #cbar, a vector of double precision, attribute of the object #matlab mathwork HQem = np.zeros((Nc, Nc), float) #numpy array #eta = 0 nu = vo[eta].nu if len(nu.shape)<2: d1 = nu.shape[0] nu = si.reshape(nu, [d1, 1]) lndetV = np.sum(np.log(nu)) #b_fn = 'callti.out.read_by_C' #lmax = vo[eta].clnp.l.max(0) #il in matlab is l in python, ndarray #tilde_b = fun.rd_b(b_fn, lmax) for n in range(Nzi): #n an index for a matrix Rabc = fun.euler2R(rule[n, 0:3]) #rule is a matrix L = fun.setL_nord(Rabc, vo[eta].clnp.l, vo[eta].clnp.n, vk, vo[eta].Htable, vo[eta].map_unique2lp, tilde_b) #L is a ndarray #clnp.in happens to be a keyword in python, how to fix this. lndet_Sigma = fun.lndet_fast(noise_var, L, nu, lndetV) #ndarray Lc = np.dot(L, vo[eta].cbar) #cbar is a list. Transferred to a ndarray before multiplication. Output is a ndarray. y_Lc = (y.transpose()-Lc).transpose() #np.substract(y, Lc) Good! Translate bxsfun() in Python wri = np.dot(rule[n][WEIGHT_INDEX], p_theta_eta[eta][:, n]) #.*, element-wise; *, dot product in matlab ???No matrix, all ndarrays??? sum_wri = np.sum(wri) D, M = fun.LT_SigmaInv_fast(noise_var, L, vo[eta].nu, y_Lc) #D, M is ndarray according to translation of Yu repwri = np.tile(np.sqrt(wri.T), (Nc, 1)) D = D * repwri D = np.dot(D, D.T) y_Lc_Sigma = fun.y_Lc_Sigma_fast(noise_var, L, nu, y_Lc) #ndarray HQem = HQem + (-2 * D + M * sum_wri) * M HQem = -HQem # sys.stdout.write('set_Q_dV_unc time: %d\n' % (tm.time() - t_Q)) return HQem
def setQem(nu ): # rule 2D matrix; vk 2D data = si.io.loadmat('fmin.mat', squeeze_me=True, struct_as_record=False) if any( nuele <= 0 for nuele in nu ): print("element in nu <=0") t_Q = tm.time() #clock:matlab function, return the date and time WEIGHT_INDEX = 5 vo = data['vobj'] rule = data['EM_iter'].rule vk = data['vk'] noise_var = data['pixelnoisevar'] p_theta_eta = data['p_theta_eta'] y = data['y'] Nzi = data['EM_iter'].rule.shape[0] tilde_b = data['tilde_b'] eta = data['eta'] # vo = vobj # rule = rule # vk = vk # noise_var = pixelnoisevar # p_theta_eta = p_theta_eta # y = y # Nzi = shape # print (type(vo[0].flags[0])) Nc = vo[0].cbar.shape[0] #vo, a cell array, each element is a virus object #cbar, a vector of double precision, attribute of the object #matlab mathwork Qem = 0 gQem = np.zeros((Nc,), float) #numpy array #eta = 0 # nu = vo[eta].nu #easy for testing with matlab data, I think we should # delete this code after testing if len(nu.shape)<2: d1 = nu.shape[0] nu = si.reshape(nu, [d1, 1]) lndetV = np.sum(np.log(nu)) #b_fn = 'callti.out.read_by_C' #lmax = vo[eta].clnp.l.max(0) #il in matlab is l in python, ndarray ############################# #tilde_b = fun.rd_b(b_fn, lmax) for n in range(Nzi): #n an index for a matrix ################################ Rabc = fun.euler2R(rule[n, 0:3]) #rule is a matrix L = fun.setL_nord(Rabc, vo[eta].clnp.l, vo[eta].clnp.n, vk, vo[eta].Htable, vo[eta].map_unique2lp, tilde_b) #L is a ndarray #clnp.in happens to be a keyword in python, how to fix this. lndet_Sigma = fun.lndet_fast(noise_var, L, nu, lndetV) #ndarray Lc = np.dot(L, vo[eta].cbar) #cbar is a list. Transferred to a ndarray before multiplication. Output is a ndarray. y_Lc = (y.transpose()-Lc).transpose() #change [eta][0][:,n]->[eta][:,n] wri = np.dot(rule[n][WEIGHT_INDEX], p_theta_eta[eta][:, n]) #.*, element-wise; *, dot product in matlab ???No matrix, all ndarrays??? sum_wri = np.sum(wri) D, M = fun.LT_SigmaInv_fast(noise_var, L, vo[eta].nu, y_Lc) #D, M is ndarray according to translation of Yu repwri = np.tile(np.sqrt(wri.T), (Nc, 1)) D = D * repwri D = np.dot(D, D.T) y_Lc_Sigma = fun.y_Lc_Sigma_fast(noise_var, L, nu, y_Lc) #ndarray Qem = Qem + (-0.5) * (np.dot(lndet_Sigma + y_Lc_Sigma, wri)) gQem = gQem + np.diag(D - np.dot(sum_wri, M)) Qem = -Qem gQem = -gQem temp = np.around(-Qem, decimals=16) sys.stdout.write('Q = %.16f\n'% temp) sys.stdout.write('set_Q_dV_unc time: %d\n' % (tm.time() - t_Q)) return Qem, gQem