dataset3Params.m

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 = 0.3;

% ====================== 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))
%

%{
steps = [ 0.01 0.03 0.1 0.3 1 3 10 30 ];
minError = Inf;
minC = Inf;
minSigma = Inf;
for i = 1:length(steps)
    for j = 1:length(steps)
        curC = steps(i);
        curSigma = steps(j);
        model = svmTrain(X, y, curC, @(x1, x2) gaussianKernel(x1, x2, curSigma));
        predictions = svmPredict(model, Xval);
        error = mean(double(predictions ~= yval));
        if error < minError
            minError = error;
            minC = curC;
            minSigma = curSigma;
        end
    end
end
C = minC;
sigma = minSigma;
%}

% The above takes a while to run, and this is the final outcome:

C = 1;
sigma = 0.1;

% =========================================================================

end