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Implement grid search and determine best parameters for C and sigma

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neingeist 2014-11-13 23:34:04 +01:00
parent e67166bc8e
commit 203cbc997c
1 changed files with 31 additions and 4 deletions
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27
ex6/dataset3Params.m
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@ -23,11 +23,38 @@ sigma = 0.3;
% 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
% =========================================================================