function spr_libsvm_demo

echo on;

addpath libsvm-mat-2.8-1 -end
addpath e6887 -end

pause

load heart_scale.mat;
whos

pause

heart_scale_inst(1:10,:)
heart_scale_label(1:10,:)
pause

I = heart_scale_label==1;
posData = heart_scale_inst(I,:);
negData = heart_scale_inst(~I,:);

visual_2d_fisher_newlibsvm(posData, negData)
pause

[heart_scale_inst , scale_parameter] = svm_scale_firsttime_newlibsvm(heart_scale_inst);
whos

pause

trainSet = heart_scale_inst(1:210,:);
testSet = heart_scale_inst(211:end,:);
trainLabel = heart_scale_label(1:210);
testLabel = heart_scale_label(211:end);
pause

[bestGamma, bestC, bestCVAccuracy,plotdata] = svm_learn_gridsearch_RBF_parameter_newlibsvm(trainSet,trainLabel,5);
imagesc(plotdata.x_gamma,plotdata.y_C,plotdata.parameter2D_plot);
ylabel('C, penalty parameter');
xlabel('gamma, RBF kernel parameter');
title('accuracy in the joint parameter space obtained by grid search');
axis xy;
colorbar;
pause

model = svmtrain(trainLabel,trainSet,sprintf('-s 0 -t 2 -g %f -c %f',bestGamma,bestC));
pause

[predict_label, accuracy] = svmpredict(testLabel, testSet, model);
accuracy
pause

accuracy