dataset3Params.m

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))
%
trange=[0.01,0.03,0.1,0.3,1,3,10,30];
merror=10^6;
cs=C;
sig=sigma;
for i=1:size(trange,2)
  for j=1:size(trange,2)
    C=trange(i);
    sigma=trange(j);
    model=svmTrain(X,y,C,@(x1,x2)gaussianKernel(x1,x2,sigma));
    predictions=svmPredict(model,Xval);
    error=mean(double(predictions~=yval));
    if error<merror,
      cs=C;
      sig=sigma;
      merror=error;
    endif
  endfor
endfor
C=cs;
sigma=sig;






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

end