Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex6-008/mlclass-ex6/dataset3Params.m

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+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))
+%
+
+merr = 1000000000;
+
+for i=-4:3
+	for j=-4:3
+		c = exp(i); % this will generate sequance close to 
+		s = exp(j);	%			0.01 0.03 0.1 0.3 1 3 10 30
+		model= svmTrain(X, y, c, @(x1, x2) gaussianKernel(x1, x2, s));
+		predictions = svmPredict(model, Xval);
+		err = mean(double(predictions ~= yval));
+		if err < merr
+			C = c;
+			sigma = s;
+			merr = err;
+		end
+	end
+end
+
+% =========================================================================
+
+end