Hi,
The provided MATLAB code appears to be performing parameter tuning for a Support Vector Regression (SVR) model using the LIBSVM library. The loop iterates through various combinations of C (cost) and gamma parameters to identify the optimal configuration based on cross-validation results. However, it's important to note that epsilon (epsilon) in SVR is typically a fixed hyperparameter, and it might not be necessary to include it in the optimization loop.
Here is the updated code:
bestcv = inf; % Best cross-validation error initialized to infinity
bestc = 0; % Initialize best C
bestg = 0; % Initialize best gamma
bestp = 0; % Initialize best epsilon
% Loop over the range of parameters
for log2c = -1:10
for log2g = -1:5
for log2p = -10:-7
% Construct the command string with spaces correctly placed
cmd = sprintf('-v 5 -s 3 -c %f -g %f -p %f', 2^log2c, 2^log2g, 2^log2p);
% Perform cross-validation
cv = svmtrain(Y, X, cmd);
% Update the best parameters if the current CV error is lower
if (cv < bestcv)
bestcv = cv;
bestc = 2^log2c;
bestg = 2^log2g;
bestp = 2^log2p;
end
% Print the current parameters and the best ones found so far
fprintf('%g %g %g %g (best c=%g, g=%g, p=%g, rate=%g)\n', log2c, log2g, log2p, cv, bestc, bestg, bestp, bestcv);
end
end
end
Here are the changes:
- Corrected the cmd string formatting to ensure proper spacing around parameters.
- Initialized bestc, bestg, and bestp before the loop.
Hope it helps.
