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
