def executeWndchrm(self):
command = ["wndchrm", "train", data_io.get_training_folder(), data_io.get_wndchrm_datafit()]
subprocess.call(" ".join(command), shell=True)
(train, target) = self.parseWndchrmOutput()
outfile = open(data_io.get_wndchrm_dataset(), "wb")
np.savez(outfile, train=train, target=target)
return (train, target)
def executeWndchrm(self, namesObservations):
#wndchrm classify -Ttestset.fit dataset.fit folder
testingFolder = os.path.join(data_io.get_testing_folder(), data_io.get_test_folder())
command = ["wndchrm", "classify", "-T%s" %(data_io.get_wndchrm_datafit_test()) ,
data_io.get_wndchrm_datafit(), testingFolder]
subprocess.call(" ".join(command), shell=True)
valid = self.parseWndchrmOutput()
outfile = open(data_io.get_wndchrm_dataset_test(), "wb")
np.savez(outfile, valid=valid, namesObservations=namesObservations)
return valid
def parseWndchrmOutput(self):
output = open(data_io.get_wndchrm_datafit(),"r")
train = []
target = []
line = output.readline()
while not "positiveSamples" in line:
line = output.readline()
line = output.readline()
while line != "":
train.append([np.float64(num) for num in line.split(" ")])
line = output.readline()
toAppend = 1 if "positiveSamples" in line else 0
target.append(toAppend)
line = output.readline()
train = np.array(train)
target = np.array(target)
return (train, target)