How to find the constant value in a loop for a particular condition?

A = [0.01; 0.03; 0.1; 0.3; 1; 3; 10; 30];
% error = zeros(64,1);

for i= 1:8
    C = A(i);
    for j = 1:8
        sigma = A(j);
        model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
        predictions = svmPredict(model, Xval);
        error(i,j) = mean(double(predictions ~= yval));
    end
end
[p,q] = find(error == min(error(:)));

error gives me a 8x8 matrix. when I find minimum value of the matrix , it turns out that [5,3] indices of the matrix gives me the minimum value. Although I can find the corresponding C and sigma value manually as dimension of A is small. but if it is of high dimension then it is hard to find that. I just want to find out corresponding C and sigma value for which I get minimum value in the error matrix automatically.

Answer 1

[KALYAN ACHARJYA]

"I just want to find out corresponding C and sigma value for which I get minimum value in the error matrix automatically"

for i= 1:8
    for j = 1:8
        model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
        predictions = svmPredict(model, Xval);
        error(i,j) = mean(double(predictions ~= yval));
    end
end

As you have complete the iterations to find the error matrix, once you done that, find the indices (i and j) of minimum of error matrix.

[i,j] = find(error==min(error(:)));

Now get the corresponding C and sigma

C=A(i)
sigma=A(j)

Note that, if you have mutiple minimum same value in the matrix, you may get multiple indices of i and j

[Ritesh Chandra Tewari]

It was easy! I should have spent more time. Thanks!