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")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
for i in range(int(len(windowedData))):
if i / len(windowedData) < 0.8:
trainingData.append(windowedData[index[i]])
trainingLabels.append(windowLabels[index[i]])
else:
testData.append(windowedData[index[i]])
testLabels.append(windowLabels[index[i]])
fileSourcePath="../",
startTime=14000,
stopTime=22000,
label=1)
# a.addDataFiles(fileSourceName="chris_c.txt", fileSourcePath="../", startTime=100, stopTime=1600, label=1)
# a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=1)
# a.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=1)
# a.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=2900, label=1)
# a.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=1)
dataSet = a.readDataSet(equalLength=False, checkData=False)
windowData, windowLabels = a.windowSplitSourceDataTT(inputData=dataSet)
features = a.initFeatNormalization(inputData=windowData)
features = a.featureNormalization(features=features, initDone=True)
features = np.array(features)
windowLabels = np.array(windowLabels)
features0 = features[windowLabels == 0]
features1 = features[windowLabels == 1]
m0 = []
m1 = []
diff = []
for i in range(int(len(features[0, ::]))):
#f = kernelDensityEstimator(x=features0[::, f0], h=0.15)
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
toggleA = 1
print("Number of windows, walk: ", str(np.size(wLabels[wLabels == 0])))
print("Number of windows, no walk: ", str(np.size(wLabels[wLabels == 1])))
for i in range(len(wLabels)):
if wLabels[i] == 0:
className="markus")
a.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",
startTime=100,
stopTime=2900,
label=6,
className="igor")
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
windows, labels = a.windowSplitSourceDataTT()
features = a.initFeatNormalization(windows)
# create the RFE model and select 3 attributes
features = np.array(features)
labels = np.array(labels)
model = XGBClassifier()
model = RFE(model, 100)
model.fit(features, labels)
# summarize the selection of the attributes
print(model.support_)
print(model.ranking_)
fov = a.returnFeatureIndices()
print(fov)