if __name__ == '__main__':
# global parameters for distance measures (Manhatten/Euclidean, sym. gem)
squared, symmetric = True, True
# read dataset number and split number
try:
datasetN, splitN = int(sys.argv[1]), int(sys.argv[2])
except:
raise Exception \
("python2 1NNResampledTest.py datasetnumber(int) splitnumber(int)")
# read the dataset
(testLabels, testSet), (trainLabels, trainSet) = ucr.read(datasetN)
# merge dataset
labels, items = ucr.merge(testLabels, testSet, trainLabels, trainSet)
# open file for the logging of results
f = open("./results/dn_%s-sn_%s-sq_%s-sy_%s" %
(datasetN, splitN, squared, symmetric), "w")
# if splitN == 0 use UCR-split else use random split
if splitN > 0:
# determine split ratio from UCR canonical split and resample
rho = float(len(trainSet))/(len(trainSet)+len(testSet))
sss = st.StratifiedShuffleSplit(labels, 500,
test_size=rho, random_state=0)
test_index, train_index = list(sss)[splitN-1]
# create training and test set
if __name__ == '__main__':
# global parameters for distance measures (Manhatten/Euclidean, sym. gem)
squared, symmetric = True, True
# read dataset number and split number
try:
datasetN, splitN = int(sys.argv[1]), int(sys.argv[2])
except:
raise Exception \
("python2 1NNResampledTest.py datasetnumber(int) splitnumber(int)")
# read the dataset
(testLabels, testSet), (trainLabels, trainSet) = ucr.read(datasetN)
# merge dataset
labels, items = ucr.merge(testLabels, testSet, trainLabels, trainSet)
# open file for the logging of results
f = open(
"./results/dn_%s-sn_%s-sq_%s-sy_%s" %
(datasetN, splitN, squared, symmetric), "w")
# if splitN == 0 use UCR-split else use random split
if splitN > 0:
# determine split ratio from UCR canonical split and resample
rho = float(len(trainSet)) / (len(trainSet) + len(testSet))
sss = st.StratifiedShuffleSplit(labels,
500,
test_size=rho,
random_state=0)
