testIndex = dataIndex
trainIndex = dataIndex
relevantMarkers = np.asarray([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,
19,20,21,22,23,24,25,26,27,28])-1
mode = 'CSV'
numClasses = 57
keepProb = .8
'''
Choose the reference sample.
'''
print('Choose the reference sample between ' + str(trainIndex))
start = tm.time()
refSampleInd = dh.chooseReferenceSample(dataPath, trainIndex,
relevantMarkers, mode,
choice)
end = tm.time()
print('reference sample selection time: ' + str(end - start))
print('Load the target ' + str(trainIndex[refSampleInd]))
target = dh.loadDeepCyTOFData(dataPath, trainIndex[refSampleInd],
relevantMarkers, mode)
# Pre-process sample.
if choice!=5:
target = dh.preProcessSamplesCyTOFData(target)
'''
Train the de-noising auto encoder.
'''
numClasses = len(actual.columns)
keepProb = .8
'''
dataIndex = np.arange(1, 16 + 1)
trainIndex = dataIndex
testIndex = dataIndex
relevantMarkers = np.asarray([1, 2, 3, 4, 5, 6, 7, 8]) - 1
mode = 'CSV'
numClasses = 4
keepProb = .8
'''
'''
Choose the reference sample.
'''
print('Choose the reference sample')
refSampleInd = dh.chooseReferenceSample(data_dir, trainIndex,
relevantMarkers, mode)
target = dh.loadDeepCyTOFData(data_dir, trainIndex[refSampleInd],
relevantMarkers, mode)
# for data_path in data_paths:
# data = pd.read_csv(data_path, compression='gzip', error_bad_lines=False)
# actual = pd.read_csv(data_path.replace("/x/","/y/"), compression='gzip', error_bad_lines=False)
# Pre-process sample. Don't need to, my samples are cleaned and processed
target = dh.preProcessSamplesCyTOFData(target)
'''
Train the de-noising auto encoder.
'''
print('Train the de-noising auto encoder.')
res_dir = data_dir.replace("raw/", "results/").replace(
"/x/", "/deepCyTOF_models/" + str(trainNum) + "/")