added classification, results are bad...

toni/octave/training.m

@@ -0,0 +1,49 @@
+#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);
+
+
+