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))
%
	C     = [0.01 0.03 0.1 0.3 1 3 10 30];
	sigma = [0.01 0.03 0.1 0.3 1 3 10 30];
	best_error	=1;
	for i=1:length(C)
		C_new=C(i);
		for j=1:length(sigma)
			sigma_new=sigma(j);	
			model= svmTrain(X, y, C_new, @(x1, x2) gaussianKernel(x1, x2, sigma_new)); 

			predictions=svmPredict(model, Xval);

			predictions_error=mean(double(predictions ~= yval));

			fprintf('New error min for C_new, sigma_new =%f,%f is = %f ', C_new, sigma_new,predictions_error);

			if(predictions_error<=best_error)
				C_optimal=C_new;
				sigma_optimal=sigma_new;
				best_error=predictions_error;
			end

		end
	end


	C     =		C_optimal;
	sigma =		sigma_optimal;
	fprintf('New optimal value of C and sigma is= %f, %f', C,sigma);


% values = [0.01 0.03 0.1 0.3 1 3 10 30];
% error_min = inf;


% fprintf('chill hommie i am looking for C and sigma, yo values\n');
% for C = values
% for sigma = values
%    fprintf('.');
%    model = svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
%    err   = mean(double(svmPredict(model, Xval) ~= yval));
%    if( err <= error_min )
%     C_final     = C;
%      sigma_final = sigma;
%      error_min   = err;
%      fprintf('new min found C, sigma = %f, %f with error = %f', C_final, sigma_final, error_min)
%    end
%  end
% end
% C     = C_final;
% sigma = sigma_final;

% fprintf('Best value C, sigma = [%f %f] with prediction error = %f\n', C, sigma, error_min);




% =========================================================================
% end