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dataset3Params.m
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dataset3Params.m
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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))
fprintf('suggests best [C, sigma] values\n');
dim_result = eye(64,3);
error_min = 0;
values = [0.01 0.03 0.1 0.3 1 3 10 30];
for C_val = values
for sigma_val = values
fprintf('Train and evaluate (on cross validation set) for\n[C_val, sigma_val] = [%f %f]\n',C_val,sigma_val);
error_min = error_min + 1;
model = svmTrain(X, y, C_val, @(x1, x2) gaussianKernel(x1, x2, sigma_val));
predictions = svmPredict(model, Xval);
pred_error = mean(double(predictions ~= yval));
fprintf('prediction error: %f\n', pred_error);
dim_result(error_min,:) = [C_val, sigma_val, pred_error];
end
end
result_sort = sortrows(dim_result, 3);
C = result_sort(1,1);
sigma = result_sort(1,2);
fprintf('\nFound Best value [C, sigma] = [%f %f] with prediction error = %f\n', C, sigma, pred_error);
end