function [C, sigma] = dataset3Params(X, y, Xval, yval)
%DATASET3PARAMS 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] = DATASET3PARAMS(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))
%
min = 999;
CA = [0.01; 0.03; 0.1; 0.3; 1; 3; 10; 30];
sigmaA = [0.01; 0.03; 0.1; 0.3; 1; 3; 10; 30];



for i = 1 : length(CA)
	for j = 1 : length(sigmaA)
		model = svmTrain(X, y, CA(i), @(x1, x2) gaussianKernel(x1, x2, sigmaA(j)));
		predictions = svmPredict(model, Xval);
		tempMin = mean(double(predictions ~= yval));
		if tempMin < min
			min = tempMin;
			C = CA(i);
			sigma = sigmaA(j);
		end
	end
end

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

end