使用feval未定义函数或变量'Sfun'时出错

Question

我一直使用R，所以我对Matlab很新，并遇到了一些故障排除问题。我正在运行一些张量分解方法的代码（可在此处获取：https://github.com/caobokai/tBNE）。首先，我尝试运行演示代码，该代码生成模拟数据以运行该方法，从而导致以下错误：

使用feval未定义函数或变量'Sfun'时出错。

OptStiefelGBB出错（第199行）[F，G] = feval（fun，X，varargin {：}）; out.nfe = 1;

tbne_demo中的错误> tBNE_fun（第124行）S，@ Sfun，opts，B，P，X，L，D，W，Y，alpha，beta）;

这是我正在运行的代码块：

    clear
    clc

    addpath(genpath('./tensor_toolbox'));
addpath(genpath('./FOptM'));
rng(5489, 'twister');

m = 10;
n = 10;
k = 10; % rank for tensor
[X, Z, Y] = tBNE_data(m, n, k); % generate the tensor, guidance and label

[T, W] = tBNE_fun(X, Z, Y, k);

[~, y1] = max(Y, [], 2);
[~, y2] = max(T{3} * W, [], 2);
fprintf('accuracy %3.2e\n', sum(y1 == y2) / n);
function [X, Z, Y] = tBNE_data(m, n, k)
    B = randn(m, k);
    S = randn(n, k);
    A = {B, B, S};
    X = ktensor(A);

    Z = randn(n, 4);

    Y = zeros(n, 2);
    l = ceil(n / 2);
    Y(1 : l, 1) = 1;
    Y(l + 1 : end, 2) = 1;

    X = tensor(X);
end
function [T, W] = tBNE_fun(X, Z, Y, k)
% t-BNE computes brain network embedding based on constrained tensor factorization
%
% INPUT
% X: brain networks stacked in a 3-way tensor
% Z: side information
% Y: label information
% k: rank of CP factorization
%
% OUTPUT
% T is the factor tensor containing
%   vertex factor matrix B = T{1} and
%   subject factor matrix S = T{3}
% W is the weight matrix
%
% Example: see tBNE_demo.m
%
% Reference:
% Bokai Cao, Lifang He, Xiaokai Wei, Mengqi Xing, Philip S. Yu, 
% Heide Klumpp and Alex D. Leow. t-BNE: Tensor-based Brain Network Embedding.
% In SDM 2017.
%
% Dependency:
% [1] Matlab tensor toolbox v 2.6
% Brett W. Bader, Tamara G. Kolda and others
% http://www.sandia.gov/~tgkolda/TensorToolbox 
% [2] A feasible method for optimization with orthogonality constraints
% Zaiwen Wen and Wotao Yin
% http://www.math.ucla.edu/~wotaoyin/papers/feasible_method_matrix_manifold.html

    %% set algorithm parameters
    printitn = 10;
    maxiter = 200;
    fitchangetol = 1e-4;

    alpha = 0.1; % weight for guidance
    beta = 0.1; % weight for classification loss
    gamma = 0.1; % weight for regularization

    u = 1e-6;
    umax = 1e6;
    rho = 1.15;

    opts.record = 0;
    opts.mxitr = 20;
    opts.xtol = 1e-5;
    opts.gtol = 1e-5;
    opts.ftol = 1e-8;

    %% compute statistics
    dim = size(X);
    normX = norm(X);
    numClass = size(Y, 2);
    m = dim(1);
    n = dim(3);
    l = size(Y, 1);
    D = [eye(l), zeros(l, n - l)];
    L = diag(sum(Z * Z')) - Z * Z';

    %% initialization
    B = randn(m, k);
    P = B;
    S = randn(n, k);
    S = orth(S);
    W = randn(k, numClass);
    U = zeros(m, k); % Lagrange multipliers

    %% main loop
    fit = 0;
    for iter = 1 : maxiter
        fitold = fit;
        % update B
        ete = (S' * S) .* (P' * P); % compute E'E
        b = 2 * ete + u * eye(k);
        c = 2 * mttkrp(X, {B, P, S}, 1) + u * P + U;
        B = c / b;

        % update P
        ftf = (S' * S) .* (B' * B); % compute F'F
        b = 2 * ftf + u * eye(k);
        c = 2 * mttkrp(X, {B, P, S}, 2) + u * B - U;
        P = c / b;

        % update U
        U = U + u * (P - B);

        % update u
        u = min(rho * u, umax);

        % update S
        tic;
        [S, out] = OptStiefelGBB(...
            S, @Sfun, opts, B, P, X, L, D, W, Y, alpha, beta);
        tsolve = toc;
        fprintf(...
            ['[S]: obj val %7.6e, cpu %f, #func eval %d, ', ...
            'itr %d, |ST*S-I| %3.2e\n'], ...
            out.fval, tsolve, out.nfe, out.itr, norm(S' * S - eye(k), 'fro'));

        % update W
        H = D * S;
        W = (H' * H + gamma * eye(k)) \ H' * Y;

        % compute the fit
        T = ktensor({B, P, S});
        normresidual = sqrt(normX ^ 2 + norm(T) ^ 2 - 2 * innerprod(X, T));
        fit = 1 - (normresidual / normX);
        fitchange = abs(fitold - fit);

        if mod(iter, printitn) == 0
            fprintf(' Iter %2d: fitdelta = %7.1e\n', iter, fitchange);
        end

        % check for convergence
        if (iter > 1) && (fitchange < fitchangetol)
            break;
        end
    end

    %% clean up final results
    T = arrange(T); % columns are normalized

    fprintf('factorization error %3.2e\n', fit);
end

我知道这里的上下文很少，但我怀疑我需要有Simulink，因为Sfun是一个与Simulink相关的函数（？）。该脚本需要两个工具箱：tensor_toolbox和FOptM。

可在：https://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html https://github.com/andland/FOptM

非常感谢你的帮助，

保罗

Answer 1

虽然SFun是Simulink S-Function的常用缩写，但在这种情况下，错误与Simulink无关，而且名称是巧合。 （没有专门称为Sfun的Simulink相关功能，它只是一个通用术语。）

您的错误消息中包含@Sfun，这是在MATLAB中创建function handle到称为Sfun的（m代码）函数的一种方式。我将从您展示的代码中总结出这是优化中使用的成本函数。

如果查看代码所基于的代码（tBNE_fun.m），您会​​看到文件末尾有一个名为Sfun的函数。这是你缺少的。