added octave files for preprocessing: filtering, windowing and additional signal calculation (3x PCA, 3x Magnitude)

toni/octave/rawPlot.m

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+display("rawPlot")
+
+#load and plot raw data
+#{
+data structure of cellarray classes:
+classes[1 to 5]
+  samples[1 to trainsetPerClass]
+    raw[1 to 3] 
+      Accel[start:pEnd]
+      Gyro[start:pEnd]
+      Magnet[start:pEnd]
+      
+access the cells using classes{u}.samples{v}.raw{w}
+#}
+source("functions.m");
+
+trainsetPerClass = 6; #number of used trainsets for one class
+classes = {};
+classes = getRawTrainData(trainsetPerClass);
+
+#outPath = "/home/toni/Documents/handygames/HandyGames/toni/img/raw"
+#plotData(classes, outPath);
+
+#calc and plot filtered data
+filteredClasses = filterData(classes);
+
+#outPath = "/home/toni/Documents/handygames/HandyGames/toni/img/filtered";
+#plotData(filteredClasses, outPath);
+
+#create sliding windows and add 6 additional signals pca and magnitude
+#{
+data structure of windowedClasses:
+classes[1 to 5]
+  samples[1 to trainsetPerClass]
+    raw[1 to 9] <--- 9 different signals
+      WindowsAccel[2.5s at 200ms] 
+        Win, Win, Win, Win ... <--- single matrices
+      WindowsGyro[2.5s at 200ms]
+        Win, Win, Win, Win ... <--- single matrices
+      WindowsMagnet[2.5s at 200ms]
+        Win, Win, Win, Win ... <--- single matrices
+        
+    ---> add 6 additional sensors: pca and magnitude    
+      3x WindowsPCA (Accel, Gyro, Magnet)
+        Win, Win, Win, Win ... <--- single matrices
+      3x WindowsMagnitude (Accel, Gyro, Magnet)
+        Win, Win, Win, Win ... <--- single matrices  
+        
+access the cells using classes{u}.samples{v}.raw{w}.wins{}
+
+pca uses the eigenvector with the heighest eigenvalue as axis and projects the signals onto it for each sensor. 
+magnitude is calculated using sqrt(x^2 + y^2 + z^2) for each sensor. 
+#}
+windowedClasses = windowData(filteredClasses);
+
+#calculated features for the 5 signales (x, y, z, MG, PCA) of a sensor
+windowedFeatures = featureCalculation(windowedClasses);
+
+#train svm
+
+#run svm