coursera-ml-007-exercises/ex6/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))
%

grid_search = 0;

if grid_search
  % Grid search
  load ex6data3.mat
  %grid = [0.01, 0.03];
  grid = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];
  results = [];
  for C = grid
    for sigma = grid

      fprintf('== C = %.2f, sigma = %.2f\n', C, sigma);
      model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
      predictions = svmPredict(model, Xval);
      error = mean(double(predictions ~= yval));
      fprintf('error = %.2f\n\n', error);

      results(end + 1,:) = [C, sigma, error];

    end
  end

  [_, best_i] = min(results(:,3));
  C = results(best_i, 1);
  sigma = results(best_i, 2);
  error = results(best_i, 3);
  fprintf('Best: C = %.2f, sigma = %.2f with error = %.2f\n', C, sigma, error);
else
  % Found through the grid search above
  C = 1.00;
  sigma = 0.10;
end

% =========================================================================

end