46 lines
1.5 KiB
Matlab
Executable File
46 lines
1.5 KiB
Matlab
Executable File
function [C, sigma] = dataset3Params(X, y, Xval, yval)
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%EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise
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%where you select the optimal (C, sigma) learning parameters to use for SVM
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%with RBF kernel
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% [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and
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% sigma. You should complete this function to return the optimal C and
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% sigma based on a cross-validation set.
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%
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% You need to return the following variables correctly.
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C = 1;
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sigma = 0.3;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return the optimal C and sigma
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% learning parameters found using the cross validation set.
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% You can use svmPredict to predict the labels on the cross
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% validation set. For example,
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% predictions = svmPredict(model, Xval);
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% will return the predictions on the cross validation set.
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%
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% Note: You can compute the prediction error using
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% mean(double(predictions ~= yval))
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%
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merr = 1000000000;
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for i=-4:3
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for j=-4:3
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c = exp(i); % this will generate sequance close to
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s = exp(j); % 0.01 0.03 0.1 0.3 1 3 10 30
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model= svmTrain(X, y, c, @(x1, x2) gaussianKernel(x1, x2, s));
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predictions = svmPredict(model, Xval);
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err = mean(double(predictions ~= yval));
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if err < merr
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C = c;
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sigma = s;
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merr = err;
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end
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end
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end
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% =========================================================================
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end
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