Source

mlclass / exercise-6 / octave / dataset3Params.m

Full commit
function [C, sigma] = dataset3Params(X, y, Xval, yval)
%EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
%   [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and 
%   sigma. You should complete this function to return the optimal C and 
%   sigma based on a cross-validation set.
%

% You need to return the following variables correctly.
C = 1;
sigma = 0.1;

% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
%               learning parameters found using the cross validation set.
%               You can use svmPredict to predict the labels on the cross
%               validation set. For example, 
%                   predictions = svmPredict(model, Xval);
%               will return the predictions on the cross validation set.
%
%  Note: You can compute the prediction error using 
%        mean(double(predictions ~= yval))
%

% note: the following had to be commented out for submission due to time out
% issues
results = []
minval = 50000000;
C = 0;
sigma = 0;
for Ci = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]
    for sigmai = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]
        model= svmTrain(X, y, Ci, @(x1, x2) gaussianKernel(x1, x2, sigmai));
        mn = sum((svmPredict(model,Xval) - yval).^2);
        %results = [results; Ci sigmai mn];
        if (mn < minval) 
            C = Ci;
            sigma = sigmai;
            minval = mn;
        end
    end
end
[C sigma minval]
% =========================================================================

end