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added classification, results are bad...

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Toni
2016-01-09 23:30:44 +01:00
parent 1af38ba3b5
commit 901291932e
14 changed files with 42297 additions and 47754 deletions
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138
toni/octave/functions.m
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@@ -1,8 +1,10 @@
display("functions")
source("settings.m");
function files = getDataFiles(clsName, trainsetPerClass)
setDataDir = "/home/toni/Documents/handygames/HandyGames/daten/";
global setDataDir;
dir = strcat(setDataDir, clsName, "/");
lst = readdir(dir);
@@ -36,13 +38,16 @@ function samples = getSamplesForClass(clsName, trainsetPerClass, start, percent)
display(strcat("training data for '", clsName, "': ", num2str(length(samples)), " samples"));
end
function data = getRawTrainData(trainsetPerClass)
function data = getRawTrainData()
#global trainsetPerClass;
data = {};
data{1}.samples = getSamplesForClass("forwardbend", trainsetPerClass, 10, 0.9);
data{2}.samples = getSamplesForClass("kneebend", trainsetPerClass, 10, 0.9);
data{3}.samples = getSamplesForClass("pushups", trainsetPerClass, 10, 0.9);
data{4}.samples = getSamplesForClass("situps", trainsetPerClass, 10, 0.9);
data{5}.samples = getSamplesForClass("jumpingjack", trainsetPerClass, 10, 0.9);
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);
end
function plotData(data,outPath)
@@ -75,17 +80,15 @@ function filteredData = filterData(data)
end
function win = window(rawVec, posMS)
global setWindowSize;
pos = posMS / 10;
#check if out of bounce, if yes- fill with zeros
#the last windows are sometimes filled with a lot of zeros... this could cause problems
if length(rawVec) <= pos+100-1
#win = rawVec(pos-100:length(rawVec),:);
#fillOut = zeros((pos+100-1) - length(rawVec), 3);
#length(fillOut)
#win = [win; fillOut];
#only full windows are accepted.
if length(rawVec) <= pos+(setWindowSize/2)-1
win = [];
else
win = rawVec(pos-100:pos+100-1,:); #100*10 ms is half the window size. that is 2s for each window.
win = rawVec(pos-(setWindowSize/2):pos+(setWindowSize/2)-1,:); #100*10 ms is half the window size.
endif
end
@@ -100,14 +103,24 @@ function out = getMagnitude(data)
end
function windowedData = windowData(data)
windowedData = {};
global setWindowSize;
global setWindowSliding;
windowedData = {};
for k = 1:numel(data)
for j = 1:numel(data{k}.samples)
winsAccel = {};
winsGyro = {};
winsMagnet = {};
#init
winsAccelX = {};
winsAccelY = {};
winsAccelZ = {};
winsGyroX = {};
winsGyroY = {};
winsGyroZ = {};
winsMagnetX = {};
winsMagnetY = {};
winsMagnetZ = {};
winsAccelPCA = {};
winsGyroPCA = {};
@@ -118,12 +131,15 @@ windowedData = {};
winsMagnetMG = {};
winEnd = length(data{k}.samples{j}.raw{1}) * 10; # *10ms
winBuffer = ((setWindowSize/2)+1) * 10;
for i = 1010:200:winEnd-1010 #200ms steps for sliding/overlapping windows
for i = winBuffer:setWindowSliding:winEnd-winBuffer #steps for sliding/overlapping windows
#accel
winAccel = window(data{k}.samples{j}.raw{1}, i);
winsAccel = [winsAccel winAccel];
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)
@@ -133,7 +149,9 @@ windowedData = {};
#gyro
winGyro = window(data{k}.samples{j}.raw{2}, i);
winsGyro = [winsGyro winGyro];
winsGyroX = [winsGyroX winGyro(:,1)];
winsGyroY = [winsGyroY winGyro(:,2)];
winsGyroZ = [winsGyroZ winGyro(:,3)];
[coeff2, score2] = princomp(winGyro);
winsGyroPCA = [winsGyroPCA score2(:,1)];
@@ -143,7 +161,9 @@ windowedData = {};
#magnet
winMagnet = window(data{k}.samples{j}.raw{3}, i);
winsMagnet = [winsMagnet winMagnet];
winsMagnetX = [winsMagnetX winMagnet(:,1)];
winsMagnetY = [winsMagnetY winMagnet(:,2)];
winsMagnetZ = [winsMagnetZ winMagnet(:,3)];
[coeff3, score3] = princomp(winMagnet);
winsMagnetPCA = [winsMagnetPCA score3(:,1)];
@@ -154,53 +174,68 @@ windowedData = {};
end
#write back data
windowedData{k}.samples{j}.raw{1}.wins = winsAccel;
windowedData{k}.samples{j}.raw{2}.wins = winsGyro;
windowedData{k}.samples{j}.raw{3}.wins = winsMagnet;
windowedData{k}.samples{j}.raw{4}.wins = winsAccelPCA;
windowedData{k}.samples{j}.raw{5}.wins = winsGyroPCA;
windowedData{k}.samples{j}.raw{6}.wins = winsMagnetPCA;
windowedData{k}.samples{j}.raw{7}.wins = winsAccelMG;
windowedData{k}.samples{j}.raw{8}.wins = winsGyroMG;
windowedData{k}.samples{j}.raw{9}.wins = winsMagnetMG;
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;
end
end
end
function features = featureCalculation(data)
features = [];
global setAutocorrelationBinSize;
global setPSDBinSize;
features = [];
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};
currentWindow = currentWindow(:,1);
#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, 5); #define 5 bins for the data.
[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), 6);
binEdges = linspace(binCenter(1)-(binSize/2), binCenter(end)+(binSize/2), setAutocorrelationBinSize+1);
[binNumc, binIdx] = histc(autoCorr, binEdges);
binMeans = getBinMean(autoCorr, binIdx, 5);
binMeans = getBinMean(autoCorr, binIdx, setAutocorrelationBinSize);
#calculate the root-mean-square (RMS) of the signal
rms = sqrt(mean(currentWindow.^2));
#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), 10 + 2); #logarithmic bin spaces for 10 bins
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
#statistical features
windowMean = mean(currentWindow);
windowSTD = std(currentWindow); #standard deviation
windowVariance = var(currentWindow);
windowKurtosis = kurtosis(currentWindow); #(ger. Wölbung)
windowIQR = iqr(currentWindow); #interquartile range
#put everything together
classLabel = k; #what class?
features = [features; classLabel, binMeans, rms, psd];
sampleLabel = j; #what sample?
features = [features; sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR];
end
end
end
@@ -209,29 +244,34 @@ end
function value = getBinMean(data, idx, numBins)
#search for an idx == numBins+1. this index occurs if a value == lastEdge, thanks histc...
isSix = find(idx == numBins+1);
if isSix != 0
idx(isSix) = numBins;
endif
value = [];
value = [];
for i = 1:numBins
flagBinMembers = (idx == i);
binMembers = data(flagBinMembers);
# if length(binMembers) == 0
# idx
#data
# input = 'balala'
#endif
value(i) = mean(binMembers);
if length(binMembers) == 0
value(i) = 0;
else
value(i) = mean(binMembers);
endif
end
end
#triangular functions. (edges of the triangles; num fft values -> nfft.)
function triFilter = getTriangularFunction(edges, nfft)
global samplerateHZ;
#get idx of the edges within the samples. thanks to fft each sample represents a frequency.
# idx * samplerate / nfft = hertz of that idx
for i = 1:length(edges)
edgesByIdx(i) = floor((nfft + 1) * edges(i)/100); #100hz is the samplerate
edgesByIdx(i) = floor((nfft + 1) * edges(i)/samplerateHZ);
end
#generate the triangle filters