Zuordnung des Zelleninhalts zu einem Arrayobjekt ohne Zelle MATLAB

Question

Kann mir bitte jemand mit meinem Code helfen?

Ich stoße auf diesen Fehler, ich habe sogar einen Schritt-für-Schritt-Ansatz mit MATLAB-Breakpoints versucht, aber immer noch nichts. Jede Hilfe würde sehr geschätzt werden. Dank

Fehler: Zelleninhalt Zuordnung zu einem nicht-Zellen-Array Objekt

Wo: Zeile 52: A = x_train' * x_train + reg_gamma (I) train_l Auge (10,10);

reg_gamma=logspace(-6,3,10); 
rng(50) 

for i = 1:200 

     % Generate random w, x, and noise from standard Gaussian 
     w = randn(10,1); 
     x = randn(600,10); 
     noise = randn(600,1); 

     % Generate the dataset y, using: x'*w+noise 
     y = x*w + noise; 

     % Split data set into a training (100) and a test set (500) 
     x_train=x([1:100],:); 
     x_test=x([101:600],:); 
     y_train=y([1:100],:); 
     y_test=y([101:600],:); 
     train_l = length(y_train); 
     test_l=length(y_test); 

    for j = 1:length(reg_gamma) 

     % 5-fold Cross Validation 
     % From the split use the 100 training for 5-fold CV 
     n = size(x_train,1); 
     k=5; 

     % Split the 100 training into 5 subsets, 4 training and 1 validation 
     % So the training would be 4x20=80 and the validation 20. 
     xvalid{k,1} = []; 
     xtrain{k,1} = []; 
     yvalid{k,1} = []; 
     ytrain{k,1} = []; 

     % Perform the CV 
     chunk = floor(n/k); 

     xvalid{1} = x_train(1:chunk,:); 
     xtrain{1} = x_train(chunk+1:end,:); 
     yvalid{1} = y_train(1:chunk,:); 
     ytrain{1} = y_train(chunk+1:end,:); 

     for f = 2:k 
      xvalid{f} = x_train((f-1)*chunk+1:(f)*chunk,:); 
      xtrain{f} = [x_train(1:(f-1)*chunk,:); x_train(f*chunk+1:end, :)]; 
      yvalid{f} = y_train((f-1)*chunk+1:(f)*chunk,:); 
      ytrain{f} = [y_train(1:(f-1)*chunk,:); y_train(f*chunk+1:end, :)]; 
     end 

     % For every fold calculate the w and the validation score 
     for ff = 1:k 
     A{ff}=xtrain{ff}'*xtrain{ff}+reg_gamma(j)*80*eye(10,10); 
     B{ff}=xtrain{ff}'*ytrain{ff}; 
     w_trainCV{ff}=mldivide(A{ff},B{ff}); 

     sum_validCV{ff}=sum((xvalid{ff}*w_trainCV{ff} - yvalid{ff}).^2); 

     end 

     % Transform the cell arrays to matrix and vectors 
     C = cell2mat(w_trainCV); 

     D = cell2mat(sum_validCV); 
     D = D./20;% 20 is the length of the points for each validation fold 

     % Average w and Validation set for each \gamma 
     w_train(:,j) = mean(C,2); 
     MSE_valid(i,j) = mean(D); 

    end 

    % Check the smallest validation error (M) and find its position (I) 
    [M,I]=min(MSE_valid(1,:)); 

    % Calculate the optimal w (perform RR on gamma with the smallest 
    % validation error) on the new training set (100). 
    % Where (I) the position of the gamma with the smallest validation error. 
    At=x_train'*x_train+reg_gamma(I)*train_l*eye(10,10); 
    Bt=x_train'*y_train; 
    w_train100=mldivide(At,Bt); % The w which we will use on 100 training 

    % Compute the mean squared error on the test set 
    sum_test=sum((x_test*w_train100 - y_test).^2); 
    MSE_test(1,i) = sum_test/test_l; 

end 

UPDATE 1:

es gefunden, das ist, was passiert, wenn Sie zu müde sind, denke ich. Ich benutze A zweimal in meinem Code, also aus einem Zellen-Array mache ich es zu einer Matrix, dann versuche ich als Matrix Matrixinhalte zu speichern.

, dass der Täter war:

% Where (I) the position of the gamma with the smallest validation error. 
    A=x_train'*x_train+reg_gamma(I)*train_l*eye(10,10); 
    B=x_train'*y_train; 

Answer 1

es gefunden, das ist, was passiert, wenn Sie zu müde sind, denke ich. Ich benutze A zweimal in meinem Code, also mache ich aus einem Zellenarray eine Matrix, dann versuche ich als Matrix Matrixinhalte zu speichern.

, dass der Täter war:

% Where (I) the position of the gamma with the smallest validation error. 
    A=x_train'*x_train+reg_gamma(I)*train_l*eye(10,10); 
    B=x_train'*y_train;