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") a.testClassifier() windowedData, windowLabels = a.windowSplitSourceDataTT() index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
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="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)
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()
a.addDataFiles(fileSourceName="chris_c.txt", fileSourcePath="../", startTime=100, stopTime=1600, label=3) a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4) a.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", 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="XGBClassifier") clf = a.loadDumpClassifier("XGBClassifier") a.testClassifier(classifier=clf) a.setFileSink(fileSinkName="chris", fileSinkPath="../") a.startLiveClassification() else: a.initFeatNormalization(dumpName="XGBClassifier") from xgboost import XGBClassifier clf = XGBClassifier() a.trainClassifier(classifier=clf) a.dumpClassifier(dumpName="XGBClassifier") a.testClassifier() windowedData, windowLabels = a.windowSplitSourceDataTT()
a.addDataFiles(fileSourceName="chris_c.txt", fileSourcePath="../", startTime=100, stopTime=1600, label=3) a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4) a.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", 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="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()
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="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)