#train features using svm
display("Train Features")

#load all features
# features = [sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR];
load "features.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

# define which sampleSet is used as testset and not for training.
leaveOut = find(features(:,1) == 3 & features(:,2) == 2); #sampleset 3 class 2
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);

# 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);

display("Classify Features")
# for testing we need to scale again
testLabel = testFeatures(:,2);
testFeatures(:,1:2) = []; #remove the labels
testFeatures = (testFeatures - repmat(minimums, size(testFeatures, 1), 1)) ./ repmat(ranges, size(testFeatures, 1), 1);

# classification
[predict_label, accuracy, dec_values] = svmpredict(testLabel, testFeatures, model);