matlab 错误使用feval_matlab - 使用feval未定义函数或变量'Sfun'时出错 - 堆栈内存溢出...

我一直使用R，因此对于Matlab还是很陌生，并且遇到了一些故障排除问题。 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，并导致以下错误：

使用feval未定义函数或变量'Sfun'时出错。

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

tbne_demo> tBNE_fun中的错误(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 %3.2en', 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| %3.2en'], ...

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 = %7.1en', 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 %3.2en', fit);

end

我知道这里几乎没有上下文，但是我怀疑我需要Simulink，因为Sfun是与Simulink相关的功能(？)。 该脚本需要两个工具箱：tensor_toolbox和FOptM。

非常感谢你的帮助，

保罗