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)