def main():
""" Data import and analysis is performed using the two kernels. The analysis is stored in a text file."""
wb = xl.open_workbook(sys.argv[1]) #retrice dataset from the excel file
S = wb.sheet_by_index(0) #dataset sheet
allData = DI.get_patient_ts(
S, 7, 6) #Convert import data to our format data structure
allData = DI.remove_missing_tp(
allData, S, 7,
6) #remove missing time points from the time series data
normalizeValues(allData) #normalize data
kernel = sys.argv[2]
#Analysis for TWED kernel
if kernel == "twed":
Analysis = {
} #Pass the key in format "twedt1". Returns the most opimal parameters as a tuple of C,S,acc,Nu and Lambda
f = open("analysisTWED.txt", "r+")
for i in xrange(6):
data = cv.gen_time_split_data(allData,
i) #split the time series data
key = kernel + "T" + str(i)
Analysis[key] = chooseOptimalModel(
data, kernel) #perform model selection
strToWrite = key + ": " + str(Analysis[key][0]) + " " + str(
Analysis[key][1]) + " " + str(
Analysis[key][2]) + " " + str(
Analysis[key][3]) + " " + str(
Analysis[key][4]) + "\n"
f.write(strToWrite)
f.close()
else:
#Analysis for GERP kernel
Analysis = {
} #Pass the key in format "twedt1". Returns the most opimal parameters as a tuple of C,S,acc
f = open("analysisGERP.txt", "r+")
for i in xrange(6):
data = cv.gen_time_split_data(allData, i)
key = kernel + "T" + str(i)
Analysis[key] = chooseOptimalModel(
data, kernel) #perform model selection
strToWrite = key + ": " + str(Analysis[key][0]) + " " + str(
Analysis[key][1]) + " " + str(Analysis[key][2]) + "\n"
f.write(strToWrite)
f.close()
def main():
wb = xl.open_workbook("Dataset_S1.xls")
S = wb.sheet_by_index(0)
allData = DI.get_patient_ts(S, 7, 6)
#for (label, time, patient,ts) in allData:
# if(patient == str(1185163.0)):
# print ts
allData = DI.remove_missing_tp(allData, S, 7, 6)
#for (label, time, patient,ts) in allData:
#if(patient == str(1185163.0)):
#print ts
#print allData[0]
seg_data = cv.gen_time_split_data(allData, 4, 2, 5)
for val in seg_data:
(train, test) = val
print ("This is new iteration")
for(label, time, patient, ts) in train:
print label, time, patient, len(ts)
print("This is new dataset")
for(label, time, patient, ts) in test:
print label, time, patient, len(ts)
def main():
wb = xl.open_workbook("Dataset_S1.xls")
S = wb.sheet_by_index(0)
allData = DI.get_patient_ts(S, 7, 6)
#for (label, time, patient,ts) in allData:
# if(patient == str(1185163.0)):
# print ts
allData = DI.remove_missing_tp(allData, S, 7, 6)
#for (label, time, patient,ts) in allData:
#if(patient == str(1185163.0)):
#print ts
#print allData[0]
seg_data = cv.gen_time_split_data(allData, 4, 2, 5)
for val in seg_data:
(train, test) = val
print("This is new iteration")
for (label, time, patient, ts) in train:
print label, time, patient, len(ts)
print("This is new dataset")
for (label, time, patient, ts) in test:
print label, time, patient, len(ts)
def main():
""" Data import and analysis is performed using the two kernels. The analysis is stored in a text file."""
wb = xl.open_workbook(sys.argv[1]) #retrice dataset from the excel file
S = wb.sheet_by_index(0) #dataset sheet
allData = DI.get_patient_ts(S, 7, 6) #Convert import data to our format data structure
allData = DI.remove_missing_tp(allData, S, 7, 6) #remove missing time points from the time series data
normalizeValues(allData) #normalize data
kernel = sys.argv[2]
#Analysis for TWED kernel
if kernel == "twed":
Analysis = {} #Pass the key in format "twedt1". Returns the most opimal parameters as a tuple of C,S,acc,Nu and Lambda
f = open("analysisTWED.txt", "r+")
for i in xrange(6):
data = cv.gen_time_split_data(allData, i) #split the time series data
key = kernel+"T"+str(i)
Analysis[key] = chooseOptimalModel(data, kernel) #perform model selection
strToWrite = key+": "+str(Analysis[key][0])+" "+str(Analysis[key][1])+" "+str(Analysis[key][2])+" "+str(Analysis[key][3])+" "+str(Analysis[key][4])+"\n"
f.write(strToWrite)
f.close()
else:
#Analysis for GERP kernel
Analysis = {} #Pass the key in format "twedt1". Returns the most opimal parameters as a tuple of C,S,acc
f = open("analysisGERP.txt", "r+")
for i in xrange(6):
data = cv.gen_time_split_data(allData, i)
key = kernel+"T"+str(i)
Analysis[key] = chooseOptimalModel(data, kernel) #perform model selection
strToWrite = key+": "+str(Analysis[key][0])+" "+str(Analysis[key][1])+" "+str(Analysis[key][2])+"\n"
f.write(strToWrite)
f.close()
