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