def main(self):
shutil.rmtree("./log/", ignore_errors=True)
obj_for_afm_dafm = afm_data_generator(self.args)
data_for_afm_dafm = obj_for_afm_dafm.main()
predictions = []
if (not self.args.afm[0] == None):
for index, data in enumerate(data_for_afm_dafm):
###print ("testing", index)
if index == 0:
predictions.append(self.fit_predict_afm(data[0], data[1], data[2], data[3], data[4]))
else:
if self.args.dafm[1] == "Yes":
predictions.append(self.fit_predict_batch_dafm(data[0], data[1]))
else:
predictions.append(self.fit_predict_dafm(data[0], data[1], data[2], data[3], data[4], data[5], data[6], data[7], data[8]))
elif (not self.args.dafm[0] == None):
for data in data_for_afm_dafm:
if self.args.dafm[1] == "Yes":
predictions.append(self.fit_predict_batch_dafm(data[0], data[1]))
else:
predictions.append(self.fit_predict_dafm(data[0], data[1], data[2], data[3], data[4], data[5], data[6], data[7], data[8]))
else:
for data in data_for_afm_dafm:
predictions.append(self.fit_predict_dkt(data[0], data[1], data[2], data[3], data[4], data[5], data[6], data[7]))
print (predictions)
outfile = open(workingDir + "output.txt", 'a')
outfile.write (str(predictions) + "\n")
outfile.close()
def main(self):
shutil.rmtree(self.args.source_path + self.args.dataset[0] + "log/",
ignore_errors=True)
obj_for_afm_dafm = afm_data_generator(self.args)
data_for_afm_dafm = obj_for_afm_dafm.main()
predictions = []
if (not self.args.afm[0] == None):
for index, data in enumerate(data_for_afm_dafm):
print("testing", index)
if index == 0:
predictions.append(
self.fit_predict_afm(data[0], data[1], data[2],
data[3], data[4]))
else:
if self.args.dafm[1] == "Yes":
predictions.append(
self.fit_predict_batch_dafm(data[0], data[1]))
else:
predictions.append(
self.fit_predict_dafm(data[0], data[1], data[2],
data[3], data[4], data[5],
data[6], data[7], data[8]))
elif (not self.args.dafm[0] == None):
for data in data_for_afm_dafm:
if self.args.dafm[1] == "Yes":
predictions.append(
self.fit_predict_batch_dafm(data[0], data[1]))
else:
predictions.append(
self.fit_predict_dafm(data[0], data[1], data[2],
data[3], data[4], data[5],
data[6], data[7], data[8]))
else:
for data in data_for_afm_dafm:
predictions.append(
self.fit_predict_dkt(data[0], data[1], data[2], data[3],
data[4], data[5], data[6], data[7]))
print(predictions)