def LoadTrainingData(namelist): print namelist trainingData = [] trainingLabel = [] i = 0 # Load the data in numpy's type for name in namelist: data = np.genfromtxt(name, delimiter=',') print data.shape # Do preprocessing & moving average temp_ind=data[:,0] temp_ind = temp_ind.reshape((len(temp_ind),1)) data = np.hstack((temp_ind, dynamicRangeCheck(data[:,1:7]))) [axis1, axis2, axis3, axis4, axis5, axis6] = Preprocessing(data, maxLen=250, n=5) cleanedData = np.array([axis1, axis2, axis3, axis4, axis5, axis6]) # Collect data which has been processing trainingData.append(cleanedData) # Create training label trainingLabel.extend([i for _ in range(axis1.shape[0])]) i+=1 trainingLabel = trainingLabel * len(trainingData[0]) # Training Model modelPool, p_pool, p_table, testingData, _, scaleRange, scaleMin, rangeOfData, LogRegPool = TrainingModel(trainingData, trainingLabel) return modelPool, p_pool, trainingData, trainingLabel, scaleRange, scaleMin, LogRegPool
def DataRepresent(trainingData, trainingLabel, rawdata_test, scaleRange, scaleMin): # Preprocessing temp_ind=rawdata_test[:,0] temp_ind = temp_ind.reshape((len(temp_ind),1)) rawdata_test = np.hstack((temp_ind, dynamicRangeCheck(rawdata_test[:,1:7]))) [axis1, axis2, axis3, axis4, axis5, axis6] = Preprocessing(rawdata_test, maxLen=192, n=5) testingData = np.array([axis1, axis2, axis3, axis4, axis5, axis6]) testingFeature = np.zeros((testingData.shape[1], 1)) # Vectorization for x in testingData: testingFeature = np.insert(testingFeature, testingFeature.shape[1], Vectorize(x), axis=1) testingFeature = np.delete(testingFeature, 0, axis=1) # Envelope for idx in range(len(trainingData[0])): tmp = [] for i in range(len(trainingData)): tmp.extend(trainingData[i][idx].tolist()) envelopeResult = np.array(envelope(np.array(trainingLabel[idx*len(tmp):(idx+1)*len(tmp)]), tmp, testingData[idx].tolist(), 1)) testingFeature = np.insert(testingFeature, testingFeature.shape[1], envelopeResult.T, axis=1) # Max-min Normalize testingFeature = (testingFeature-scaleMin)/scaleRange return testingFeature
def DataRepresent(dataPool, trainingLabel, rawdata): # Preprocessing temp_ind=rawdata[:,0] temp_ind = temp_ind.reshape((len(temp_ind),1)) result = np.hstack((temp_ind, dynamicRangeCheck(rawdata[:,1:7]))) [axis1, axis2, axis3, axis4, axis5, axis6] = Preprocessing(result, maxLen=250, n=5) testingData = np.array([axis1, axis2, axis3, axis4, axis5, axis6]) #print testingData testingFeature = np.zeros((testingData.shape[1], 1)) # Envelope for idx in range(6): training_sample = [] map(lambda i: training_sample.extend(dataPool[i][idx].tolist()), xrange(4)) envelopeResult = np.array(envelope(np.array(trainingLabel[idx*len(training_sample):(idx+1)*len(training_sample)]), training_sample, testingData[idx].tolist(), 1)) testingFeature = np.insert(testingFeature, testingFeature.shape[1], envelopeResult.T, axis=1) print testingFeature testingFeature = np.delete(testingFeature, 0, axis=1) return testingFeature