# mlclass / exercise-6 / octave / dataset3Params.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47``` ```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 ```