def _testing_original_file(self, test_dir, type_data):
dnn_predict_dir = '/home/danglab/3P/B/' + self.hidden_layer +'SQR/' + self.artic + type_data + 'full_EMA/' + 'test_' + str(self.test_number) + '/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
for afile in self.missing_filename_list:
test_arr, factors = read_file_test(test_dir + afile + '_in.txt', self.n_input_f, "factors") #read a missing_feature
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
#test_arr[:,36:test_arr.shape[1]] = 0 # loai bo EMA data
#print test_arr
#break
self._write_predict_2_file(dnn_predict_dir + afile + '.txt', energy, self.predict(test_arr), factors) # write result to file
def _testing_noise_space(self, test_dir, type_data):
for type_test in sorted(os.listdir(test_dir)):
if (not type_test.endswith('zip')) and 'output' not in type_test:
type_test_dir = test_dir + type_test + '/'
print type_test_dir
dnn_predict_dir = '/home/danglab/3P/B/' + self.hidden_layer +'SQR/' + self.artic + type_data + 'test_' + str(self.test_number) + '/' + type_test + '/'
if not os.path.exists(dnn_predict_dir):
os.makedirs(dnn_predict_dir)
print type_test
duration = type_test.split('_')[1] # 50ms, 100ms
#listtest = sorted(os.listdir(type_test_dir))
#for afile in listtest:
for prefix_file in self.missing_filename_list:
afile = prefix_file + '_' + duration + '_in.txt'
test_arr, factors = read_file_test(type_test_dir + afile, self.n_input_f, "factors") #read a missing_feature
find_ = [m.start() for m in re.finditer('_', afile)]
energy = test_arr[:,0] #ko cho energy vao DNN
test_arr = test_arr[:,1:self.n_input_f]
#print factors
self._write_predict_2_file(dnn_predict_dir + afile.replace(afile[find_[4]:len(afile)-4],''), energy, self.predict(test_arr), factors) # write result to file
