#train features using svm
display("Train Features")
page_screen_output(0);
page_output_immediately(1);
more off;

#load all features
# features = [sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR];
load "eval/512/features_512_xyz_nomag_psd18.txt"; #matrix is also called features

# split features into training and test features using leave-one-out method
# class idx:
# idx 1 -> forwardbend
# idx 2 -> kneebend
# idx 3 -> pushups
# idx 4 -> situps
# idx 5 -> jumpingjack

#remove features for evaluation
#features(:, 3:7) = []; #remove binMeans
#features(:, 8:25) = []; #remove psd
#features(:, 26) = []; #remove rms
#features(:, 27:31) = []; #remove statistical stuff

size(features, 2)

#samples_class = features(:,1:2);
#for numClass = 1 : max(samples_class(:,2)) 
#  for numSample = 1 : max(samples_class(find(samples_class(:,2)==1),1))
    
    t=cputime;

    # define which sampleSet is used as testset and not for training.    
    leaveOut = randperm(length(features), 1000);
    
    numSample = 2;
    numClass = 5;
    #leaveOut = find(features(:,1) == numSample & features(:,2) == numClass); #sampleset x class y
    testFeatures = features(leaveOut, :); #set testSignal
    features(leaveOut,:) = []; #remove the testFeatures
    features(:,1) = []; #remove the sampleLabel 
    

    # bring the feature matrix into libsvm format. 
    # 1. the label vector: 
    trainLabel = features(:,1);

    # 2. sparse matrix with every feature in one column:
    features(:,1) = []; #remove the classLabel
    trainFeatures = sparse(features);

    #write out libsvm file
    #libsvmwrite(strcat("trainFeatures_512_", num2str(numClass), "_", num2str(numSample), ".txt"), trainLabel, trainFeatures);

    # before training we need to scale the feature values
    minimums = min(trainFeatures);
    ranges = max(trainFeatures) - minimums;

    trainFeatures = (trainFeatures - repmat(minimums, size(trainFeatures, 1), 1)) ./ repmat(ranges, size(trainFeatures, 1), 1); 

    # training: svm with default settings
    model = svmtrain(trainLabel, trainFeatures, '-h 0 -c 32768 -g 8 -q');

    disp("Classify Features");
    # for testing we need to scale again
    testLabel = testFeatures(:,2);
    testFeatures(:,1:2) = []; #remove the labels
    testFeatures = sparse(testFeatures);

    #write out libsvm file
    #libsvmwrite(strcat("testFeatures_512_", num2str(numClass), "_", num2str(numSample), ".txt"), testLabel, testFeatures);

    testFeatures = (testFeatures - repmat(minimums, size(testFeatures, 1), 1)) ./ repmat(ranges, size(testFeatures, 1), 1);

    # classification
    [predict_label, accuracy, dec_values] = svmpredict(testLabel, testFeatures, model);
    
       
    # get evaluation matrix
    evaluation = [];
    for i = 1:5
      for j = 1:5          
        evaluation(i,j) = sum(testLabel == i & predict_label == j);
      end
    end
    disp(evaluation);
    
    save evaluation_512.txt evaluation accuracy;
       
    printf('Total cpu time: %f seconds\n', cputime-t);
#  end
#end