startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="MLPClassifier")
clf = a.loadDumpClassifier("MLPClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="MLPClassifier")
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="MLPClassifier")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
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")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
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="KNeighborsClassifier")
clf = a.loadDumpClassifier("KNeighborsClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="KNeighborsClassifier")
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=4, metric='euclidean')
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="KNeighborsClassifier")
a.testClassifier()
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)
# plt.scatter(i*np.ones(np.size(features0[::, f0])), features0[::, f0], c='r', marker="s", alpha=0.5)
# m0.append(kernelDensityEstimator(features0[::, f0], h=0.15))
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="DecisionTreeClassifier")
clf = a.loadDumpClassifier("DecisionTreeClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="DecisionTreeClassifier")
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="DecisionTreeClassifier")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="GaussianNB")
clf = a.loadDumpClassifier("GaussianNB")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="GaussianNB")
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="GaussianNB")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
trainingData = []
trainingLabels = []
testData = []
testLabels = []
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)
plt.scatter(fov, model.ranking_)