neingeist/coursera-ml-007-exercises
Archived
1
0
Fork 0
Code Releases Activity

Implement grid search and determine best parameters for C and sigma

Browse source
This commit is contained in:
neingeist 2014-11-13 23:34:04 +01:00
parent e67166bc8e
commit 203cbc997c
1 changed files with 31 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

35
ex6/dataset3Params.m
View file

@ -2,8 +2,8 @@ function [C, sigma] = dataset3Params(X, y, Xval, yval)
%EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise %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 %where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel %with RBF kernel
% [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and % [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. You should complete this function to return the optimal C and
% sigma based on a cross-validation set. % sigma based on a cross-validation set.
% %
@ -15,19 +15,46 @@ sigma = 0.3;
% Instructions: Fill in this function to return the optimal C and sigma % Instructions: Fill in this function to return the optimal C and sigma
% learning parameters found using the cross validation set. % learning parameters found using the cross validation set.
% You can use svmPredict to predict the labels on the cross % You can use svmPredict to predict the labels on the cross
% validation set. For example, % validation set. For example,
% predictions = svmPredict(model, Xval); % predictions = svmPredict(model, Xval);
% will return the predictions on the cross validation set. % will return the predictions on the cross validation set.
% %
% Note: You can compute the prediction error using % Note: You can compute the prediction error using
% mean(double(predictions ~= yval)) % 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
% ========================================================================= % =========================================================================