"chris_c.txt", "ankita_pos2_lrRl.txt", "igor2.txt"
]
dataLabels = np.array([0, 1, 2, 3, 4, 1, 0])
start = np.array([600, 300, 50, 100, 100, 100, 3500])
stop = np.array([3400, 1800, 1550, 1700, 1600, 3000, 6000])
########################################################################################################################
usedSensors = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print("Using following sensors: ", usedSensors)
########################################################################################################################
## load dataset:
########################################################################################################################
dataSet = readSeveralFiles(files=files,
startTimes=start,
stopTimes=stop,
path="",
numberOfIrSensors=10,
numberOfForceSensors=2,
equalLength=False,
checkData=False,
selectSensors=usedSensors)
########################################################################################################################
## normalize data
########################################################################################################################
for i in range(len(dataSet)):
dataSet[i] = scaleData(dataSet[i],
scaleAmp=True,
scaleMean=False,
scaleVar=True)
########################################################################################################################
## extract windows:
########################################################################################################################
def trainSVC():
files = [
"igor.txt", "ankita.txt", "chris_asymm.txt", "chris_pos2.txt",
"chris_c.txt", "ankita_pos2_lrRl.txt", "igor2.txt", "chris1.txt"
]
start = np.array([600, 300, 50, 100, 100, 100, 3500, 500])
stop = np.array([3400, 1800, 1550, 1700, 1600, 3000, 6000, 4500])
numberOfClasses = 5
fileLabels = np.array([0, 1, 2, 3, 4, 1, 0, 3])
usedSensors = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print("Using following sensors: ", usedSensors)
########################################################################################################################
testFrac = 0.05
numDomCoeffs = 20
numDomFreqs = 20
windowWidth = 100
windowShift = 10
numOfSensors = np.size(usedSensors)
########################################################################################################################
dataSet = readSeveralFiles(files=files,
startTimes=start,
stopTimes=stop,
path="",
numberOfIrSensors=10,
numberOfForceSensors=2,
equalLength=False,
checkData=False,
selectSensors=usedSensors)
for i in range(len(dataSet)):
dataSet[i] = scaleData(dataSet[i],
scaleAmp=True,
scaleMean=False,
scaleVar=True)
dataWindows = []
numberOfWindows = []
for i in range(len(dataSet)):
windows, numOfWindows = sliceAndWindowV3(data=dataSet[i],
windowWidth=windowWidth,
windowShift=windowShift,
enaCheck=False,
window='tukey',
alpha=0.1,
enaCWF=0)
dataWindows.append(windows)
numberOfWindows.append(numOfWindows)
dataWindows = np.array(dataWindows)
numberOfWindows = np.array(numberOfWindows)
print("Number of windows per dataset: ", numberOfWindows)
files[i] == "chris.txt"
features = []
labels = []
trainingFeatures = []
testFeatures = []
trainingLabels = []
testLabels = []
for i in range(len(dataWindows)):
index = np.linspace(0, len(dataWindows[i]) - 1, len(dataWindows[i]))
#random.shuffle(index)
if ((i == 17) | (i == 110)):
print("Dataset:", files[i], " with label:", fileLabels[i],
" is for test only...")
for j in range(numberOfWindows[i]):
f = extractSpectralFeatures(dataWindow=dataWindows[i][int(
index[j])],
numDomCoeffs=numDomCoeffs,
numDomFreqs=numDomFreqs,
sampleT=0.0165,
wavelet='haar')
testFeatures.append(f.T)
testLabels.append(fileLabels[i])
else:
for j in range(numberOfWindows[i]):
f = extractSpectralFeatures(dataWindow=dataWindows[i][int(
index[j])],
numDomCoeffs=numDomCoeffs,
numDomFreqs=numDomFreqs,
sampleT=0.0165,
wavelet='haar')
if j > int(testFrac * numberOfWindows[i]):
testFeatures.append(f.T)
testLabels.append(fileLabels[i])
else:
trainingFeatures.append(f.T)
trainingLabels.append(fileLabels[i])
#shuffledTrainingLabels, shuffledTrainingFeatures = shuffleData(trainingLabels, trainingFeatures)
#shuffledTestLabels, shuffledTestFeatures = shuffleData(testLabels, testFeatures)
## train svm:
clf = svm.SVC(kernel='rbf')
clf.fit(trainingFeatures, trainingLabels)
return clf
def splitDataTrainTest(files,
start,
stop,
fileLabels,
windowWidth=100,
windowShift=10,
numDomCoeffs=10,
numDomFreqs=10,
trainFrac=2 / 3,
statFeat=True,
shuffleData=False,
checkData=False):
usedSensors = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
dataSet = readSeveralFiles(files=files,
startTimes=start,
stopTimes=stop,
path="",
numberOfIrSensors=10,
numberOfForceSensors=2,
equalLength=True,
checkData=checkData,
useForce=True,
useBno=True,
useIr=True,
interpBno=True,
selectSensors=usedSensors)
dataWindows = []
numberOfWindows = []
for i in range(len(dataSet)):
windows, numOfWindows = sliceAndWindowV3(data=dataSet[i],
windowWidth=windowWidth,
windowShift=windowShift,
enaCheck=False,
window='tukey',
alpha=0.1,
enaCWF=0)
dataWindows.append(windows)
numberOfWindows.append(numOfWindows)
dataWindows = np.array(dataWindows)
numberOfWindows = np.array(numberOfWindows)
features = []
labels = []
trainingFeatures = []
testFeatures = []
trainingLabels = []
testLabels = []
for i in range(len(dataWindows)):
index = np.linspace(0, len(dataWindows[i]) - 1, len(dataWindows[i]))
if shuffleData:
random.shuffle(index)
for j in range(numberOfWindows[i]):
f = extractFeatures(dataWindows[i][int(index[j])],
numDomCoeffs=numDomCoeffs,
numDomFreqs=numDomFreqs,
statFeat=statFeat,
wavelet='haar')
if j > int(trainFrac * numberOfWindows[i] - 2):
testFeatures.append(f.T)
testLabels.append(fileLabels[i])
else:
trainingFeatures.append(f.T)
trainingLabels.append(fileLabels[i])
trainingFeatures = np.array(trainingFeatures)
testFeatures = np.array(testFeatures)
mean = []
std_dev = []
for i in range(np.size(trainingFeatures[0, ::])):
x = trainingFeatures[::, i]
mean.append(sum(x) / len(x))
std_dev.append(
(1 / len(x) * sum([(x_i - mean[i])**2 for x_i in x]))**0.5)
trainingFeatures[::,
i] = (trainingFeatures[::, i] - mean[i]) / std_dev[i]
testFeatures[::, i] = (testFeatures[::, i] - mean[i]) / std_dev[i]
return trainingFeatures, testFeatures, trainingLabels, testLabels, mean, std_dev