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first draft for first deadline.

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Toni
2016-01-11 05:33:22 +01:00
parent 6231455fb0
commit 934f75873e
6 changed files with 225 additions and 42265 deletions
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42142
toni/octave/features.txt
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193
toni/octave/functions.m
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@@ -1,7 +1,6 @@
display("functions")
source("settings.m");
function files = getDataFiles(clsName, trainsetPerClass)
global setDataDir;
@@ -35,19 +34,21 @@ function samples = getSamplesForClass(clsName, trainsetPerClass, start, percent)
raw = {Gyro(start:pEnd,:), Accel(start:pEnd,:), Magnet(start:pEnd,:)}; # create cell array of the wanted samples
samples{i}.raw = raw; # write them into cell array for each Class
end
display(strcat("training data for '", clsName, "': ", num2str(length(samples)), " samples"));
disp(strcat("training data for '", clsName, "': ", num2str(length(samples)), " samples"));
end
function data = getRawTrainData()
#global trainsetPerClass;
global signalStart;
global signalEnd;
data = {};
data{1}.samples = getSamplesForClass("forwardbend", 9, 10, 0.95);
data{2}.samples = getSamplesForClass("kneebend", 11, 10, 0.95);
data{3}.samples = getSamplesForClass("pushups", 11, 10, 0.95);
data{4}.samples = getSamplesForClass("situps", 8, 10, 0.95);
data{5}.samples = getSamplesForClass("jumpingjack", 13, 10, 0.95);
data{1}.samples = getSamplesForClass("forwardbend", 9, signalStart, signalEnd);
data{2}.samples = getSamplesForClass("kneebend", 11, signalStart, signalEnd);
data{3}.samples = getSamplesForClass("pushups", 11, signalStart, signalEnd);
data{4}.samples = getSamplesForClass("situps", 8, signalStart, signalEnd);
data{5}.samples = getSamplesForClass("jumpingjack", 13, signalStart, signalEnd);
end
function plotData(data,outPath)
@@ -108,6 +109,12 @@ function windowedData = windowData(data)
global setWindowSliding;
windowedData = {};
global setMagnet;
global setAccel;
global setGyro;
global useSignalPCA;
global useSignalMG;
for k = 1:numel(data)
for j = 1:numel(data{k}.samples)
@@ -135,60 +142,68 @@ function windowedData = windowData(data)
for i = winBuffer:setWindowSliding:winEnd-winBuffer #steps for sliding/overlapping windows
#accel
winAccel = window(data{k}.samples{j}.raw{1}, i);
winsAccelX = [winsAccelX winAccel(:,1)];
winsAccelY = [winsAccelY winAccel(:,2)];
winsAccelZ = [winsAccelZ winAccel(:,3)];
if setAccel == true
#accel
winAccel = window(data{k}.samples{j}.raw{1}, i);
winsAccelX = [winsAccelX winAccel(:,1)];
winsAccelY = [winsAccelY winAccel(:,2)];
winsAccelZ = [winsAccelZ winAccel(:,3)];
[coeff1, score1] = princomp(winAccel);
winsAccelPCA = [winsAccelPCA score1(:,1)]; #choose the first axis (eigvec with the biggest eigvalue)
if useSignalPCA == true
[coeff1, score1] = princomp(winAccel');
winsAccelPCA = [winsAccelPCA score1(:,1)]; #choose the first axis (eigvec with the biggest eigvalue)
endif
winAccelMG = getMagnitude(winAccel);
winsAccelMG = [winsAccelMG winAccelMG];
if useSignalMG == true
winAccelMG = getMagnitude(winAccel);
winsAccelMG = [winsAccelMG winAccelMG];
endif
#gyro
winGyro = window(data{k}.samples{j}.raw{2}, i);
winsGyroX = [winsGyroX winGyro(:,1)];
winsGyroY = [winsGyroY winGyro(:,2)];
winsGyroZ = [winsGyroZ winGyro(:,3)];
endif
[coeff2, score2] = princomp(winGyro);
winsGyroPCA = [winsGyroPCA score2(:,1)];
if setGyro == true
#gyro
winGyro = window(data{k}.samples{j}.raw{2}, i);
winsGyroX = [winsGyroX winGyro(:,1)];
winsGyroY = [winsGyroY winGyro(:,2)];
winsGyroZ = [winsGyroZ winGyro(:,3)];
winGyroMG = getMagnitude(winGyro);
winsGyroMG = [winsGyroMG winGyroMG];
if useSignalPCA == true
[coeff2, score2] = princomp(winGyro');
winsGyroPCA = [winsGyroPCA score2(:,1)];
endif
#magnet
winMagnet = window(data{k}.samples{j}.raw{3}, i);
winsMagnetX = [winsMagnetX winMagnet(:,1)];
winsMagnetY = [winsMagnetY winMagnet(:,2)];
winsMagnetZ = [winsMagnetZ winMagnet(:,3)];
if useSignalMG == true
winGyroMG = getMagnitude(winGyro);
winsGyroMG = [winsGyroMG winGyroMG];
endif
[coeff3, score3] = princomp(winMagnet);
winsMagnetPCA = [winsMagnetPCA score3(:,1)];
endif
winMagnetMG = getMagnitude(winMagnet);
winsMagnetMG = [winsMagnetMG winMagnetMG];
if setMagnet == true
#magnet
winMagnet = window(data{k}.samples{j}.raw{3}, i);
winsMagnetX = [winsMagnetX winMagnet(:,1)];
winsMagnetY = [winsMagnetY winMagnet(:,2)];
winsMagnetZ = [winsMagnetZ winMagnet(:,3)];
if useSignalPCA == true
[coeff3, score3] = princomp(winMagnet');
winsMagnetPCA = [winsMagnetPCA score3(:,1)];
endif
if useSignalMG == true
winMagnetMG = getMagnitude(winMagnet);
winsMagnetMG = [winsMagnetMG winMagnetMG];
endif
endif
end
#write back data
windowedData{k}.samples{j}.raw{1}.wins = winsAccelX; #X
windowedData{k}.samples{j}.raw{2}.wins = winsAccelY; #Y
windowedData{k}.samples{j}.raw{3}.wins = winsAccelZ; #Z
windowedData{k}.samples{j}.raw{4}.wins = winsGyroX;
windowedData{k}.samples{j}.raw{5}.wins = winsGyroY;
windowedData{k}.samples{j}.raw{6}.wins = winsGyroZ;
windowedData{k}.samples{j}.raw{7}.wins = winsMagnetX;
windowedData{k}.samples{j}.raw{8}.wins = winsMagnetY;
windowedData{k}.samples{j}.raw{9}.wins = winsMagnetZ;
windowedData{k}.samples{j}.raw{10}.wins = winsAccelPCA;
windowedData{k}.samples{j}.raw{11}.wins = winsGyroPCA;
windowedData{k}.samples{j}.raw{12}.wins = winsMagnetPCA;
windowedData{k}.samples{j}.raw{13}.wins = winsAccelMG;
windowedData{k}.samples{j}.raw{14}.wins = winsGyroMG;
windowedData{k}.samples{j}.raw{15}.wins = winsMagnetMG;
#write back data
windowedData{k}.samples{j}.raw = [winsAccelX; winsAccelY; winsAccelZ; winsGyroX; winsGyroY; winsGyroZ; winsMagnetX; winsMagnetY; winsMagnetZ; winsAccelPCA; winsGyroPCA; winsMagnetPCA; winsAccelMG; winsGyroMG; winsMagnetMG];
end
end
end
@@ -199,43 +214,65 @@ function features = featureCalculation(data)
global setPSDBinSize;
features = [];
global setAutoCorr;
global setRMS;
global setPSD;
global setStatistic;
global setPCA;
for k = 1:numel(data)
for j = 1:numel(data{k}.samples)
for i = 1:numel(data{k}.samples{j}.raw)
for m = 1:numel(data{k}.samples{j}.raw{i}.wins)
currentWindow = data{k}.samples{j}.raw{i}.wins{m};
for i = 1:size(data{k}.samples{j}.raw, 1)
for m = 1:size(data{k}.samples{j}.raw, 2)
currentWindow = data{k}.samples{j}.raw{i, m};
#autocorrelation on window. split into 5 evenly spaced bins (frequencies are evenly spaced, not number of values ;) ) and calculate mean of bin.
[autoCorr] = xcorr(currentWindow);
[binNum, binCenter] = hist(autoCorr, setAutocorrelationBinSize); #define bins for the data.
binSize = abs(binCenter(end-1) - binCenter(end));
binEdges = linspace(binCenter(1)-(binSize/2), binCenter(end)+(binSize/2), setAutocorrelationBinSize+1);
[binNumc, binIdx] = histc(autoCorr, binEdges);
binMeans = getBinMean(autoCorr, binIdx, setAutocorrelationBinSize);
if setAutoCorr == true
#autocorrelation on window. split into 5 evenly spaced bins (amplitudes are evenly spaced, not number of values ;) ) and calculate mean of bin.
[autoCorr] = xcorr(currentWindow);
[binNum, binCenter] = hist(autoCorr, setAutocorrelationBinSize); #define bins for the data.
binSize = abs(binCenter(end-1) - binCenter(end));
binEdges = linspace(binCenter(1)-(binSize/2), binCenter(end)+(binSize/2), setAutocorrelationBinSize+1);
[binNumc, binIdx] = histc(autoCorr, binEdges);
binMeans = getBinMean(autoCorr, binIdx, setAutocorrelationBinSize);
#calculate the root-mean-square (RMS) of the signal
rms = sqrt(mean(currentWindow.^2));
endif
#power bands 0.5 to 25hz (useful if the windows are greater then 4s and window sizes to 256, 512..)
[powerBand, w] = periodogram(currentWindow); #fills up fft with zeros
powerEdges = logspace(log10(0.5), log10(25), setPSDBinSize + 2); #logarithmic bin spaces for 10 bins
triFilter = getTriangularFunction(powerEdges, length(powerBand)*2 - 2);
for l = 1:numel(triFilter)
filteredBand = triFilter{l} .* powerBand;
psd(l) = sum(filteredBand); #sum freq (no log and no dct)
end
if setRMS == true
#calculate the root-mean-square (RMS) of the signal
rms = sqrt(mean(currentWindow.^2));
endif
#statistical features
windowMean = mean(currentWindow);
windowSTD = std(currentWindow); #standard deviation
windowVariance = var(currentWindow);
windowKurtosis = kurtosis(currentWindow); #(ger. Wölbung)
windowIQR = iqr(currentWindow); #interquartile range
if setPSD == true
#power bands 0.5 to 25hz (useful if the windows are greater then 4s and window sizes to 256, 512..)
[powerBand, w] = periodogram(currentWindow); #fills up fft with zeros
powerEdges = logspace(log10(0.5), log10(25), setPSDBinSize + 2); #logarithmic bin spaces for 10 bins
triFilter = getTriangularFunction(powerEdges, length(powerBand)*2 - 2);
for l = 1:numel(triFilter)
filteredBand = triFilter{l} .* powerBand;
psd(l) = sum(filteredBand); #sum freq (no log and no dct)
end
endif
if setStatistic == true
#statistical features
windowMean = mean(currentWindow);
windowSTD = std(currentWindow); #standard deviation
windowVariance = var(currentWindow);
windowKurtosis = kurtosis(currentWindow); #(ger. Wölbung)
windowIQR = iqr(currentWindow); #interquartile range
endif
pcaFeature = [];
if setPCA == true
#pca on windows
[coeff, score] = princomp(currentWindow');
pcaFeature = currentWindow' * coeff(:, 1:10); #10 pca feature
endif
#put everything together
classLabel = k; #what class?
sampleLabel = j; #what sample?
features = [features; sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR];
features = [features; sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR, pcaFeature];
end
end
end
@@ -258,7 +295,7 @@ function value = getBinMean(data, idx, numBins)
if length(binMembers) == 0
value(i) = 0;
else
value(i) = mean(binMembers);
value(i) = sum(binMembers);
endif
end
end

9
toni/octave/genfeatures.m
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@@ -13,6 +13,8 @@ classes[1 to 5]
access the cells using classes{u}.samples{v}.raw{w}
#}
source("functions.m");
source("settings.m")
global setWindowSize;
classes = {};
classes = getRawTrainData();
@@ -45,12 +47,12 @@ classes[1 to 5]
3x WindowsMagnitude (Accel, Gyro, Magnet)
Win, Win, Win, Win ... <--- single matrices
access the cells using classes{u}.samples{v}.raw{w}.wins{}
access the cells using classes{u}.samples{v}.raw{signal, window)
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);
windowedClasses = windowData(classes);
#calculated features for the 5 signales (x, y, z, MG, PCA) of a sensor
#{
@@ -60,7 +62,8 @@ data structure of features
features = featureCalculation(windowedClasses);
#save features
save features.txt features;
name = strcat("features_", num2str(setWindowSize),"_xyz_nomag_psd18.txt");
save(name, 'features');
display("saved features into features.txt");

20
toni/octave/settings.m
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@@ -1,8 +1,22 @@
#global trainsetPerClass = 6; #number of used trainsets for one class
global setDataDir = "/home/toni/Documents/handygames/HandyGames/daten/";
global setWindowSize = 256; #in samples per window. even integer!
global setWindowSliding = 320; #in ms - (sampling rate is 10 ms.. so numSamples*10)
global setWindowSize = 512; #in samples per window. even integer!
global setWindowSliding = 160; #in ms - (sampling rate is 10 ms.. so numSamples*10)
global setAutocorrelationBinSize = 5;
global setPSDBinSize = 10;
global setPSDBinSize = 18;
global samplerateHZ = 100;
global setMagnet = false;
global setAccel = true;
global setGyro = true;
global useSignalPCA = false;
global useSignalMG = false;
global signalStart = 1510;
global signalEnd = 0.8;
global setAutoCorr = true;
global setRMS = true;
global setPSD = true;
global setStatistic = true;
global setPCA = false; #not implementend / buggy

13
toni/octave/todo.txt
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@@ -23,7 +23,7 @@ autocorrelation bins. jedes signal window wird in 5 frequenz bins unterteilt und
energy features:
RMS
power spectrum (|fft|^2). features über logarithmisch überlappende vierrecksfilter, da dreieicksfilter hier keinen sinn ergeben, oder? Wie bei Audio noch eine DCT auf die Ergebnisse des Dreiecksfilters um unwichtige rauszuwerfen?
power spectrum (|fft|^2). features über logarithmisch überlappende dreiecksfilter Wie bei Audio noch eine DCT auf die Ergebnisse des Dreiecksfilters um unwichtige rauszuwerfen?
statistical features:
mean
@@ -47,10 +47,13 @@ Training:
one leave out.
Prasi:
pure klassifikation. ohne zu schecken was macht das fenster vorher.
svm aufwand?
autokorrelation: ist ein signal eigentlich periodisch, wenn ich paar lags einbaue? peak in der mitte sagt ja. wir sind jetzt an der amplitude interessiert. da die meisten übungen wohl periodisch sind ;).

99
toni/octave/training.m
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@@ -1,9 +1,12 @@
#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 "features.txt"; #matrix is also called features
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:
@@ -13,37 +16,79 @@ load "features.txt"; #matrix is also called features
# 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
#remove features for evaluation
#features(:, 3:7) = []; #remove binMeans
#features(:, 8:25) = []; #remove psd
#features(:, 26) = []; #remove rms
#features(:, 27:31) = []; #remove statistical stuff
# bring the feature matrix into libsvm format.
# 1. the label vector:
trainLabel = features(:,1);
size(features, 2)
# 2. sparse matrix with every feature in one column:
features(:,1) = []; #remove the classLabel
trainFeatures = sparse(features);
#samples_class = features(:,1:2);
#for numClass = 1 : max(samples_class(:,2))
# for numSample = 1 : max(samples_class(find(samples_class(:,2)==1),1))
# before training we need to scale the feature values
minimums = min(trainFeatures);
ranges = max(trainFeatures) - minimums;
t=cputime;
trainFeatures = (trainFeatures - repmat(minimums, size(trainFeatures, 1), 1)) ./ repmat(ranges, size(trainFeatures, 1), 1);
# define which sampleSet is used as testset and not for training.
leaveOut = randperm(length(features), 1000);
# 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);
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