startTime=500,
stopTime=3300,
label=5)
a.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",
startTime=100,
stopTime=2900,
label=6)
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="dataOnly")
clf = a.loadDumpClassifier("dataOnly")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="dataOnly")
from sklearn import svm
clf = svm.SVC(kernel='rbf')
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="dataOnly")
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
b.addDataFiles(fileSourceName="nowalk2.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
b.addDataFiles(fileSourceName="nowalk3.txt",
fileSourcePath="../",
startTime=0,
stopTime=10000,
label=1)
dataSet = b.readDataSet(checkData=False, equalLength=True)
# dataSet := Array with shape dataSet[i][j, k], where i refers to the i-th file loaded, k indicates the sensor and
# j is the "time"-index.
wData, wLabels = b.windowSplitSourceDataTT(inputData=dataSet,
inputLabels=np.array([
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1
]))
wLabels = np.array(wLabels)
print(wLabels)
toggleI = 1