def evaluate(original_file, test_file):
ori_arr = read_file_test(original_file, 13, )
test_arr = read_file_test(test_file, 13)
ori_arr = ori_arr[:,1:13] # take 12 coefficients
test_arr = test_arr[:,1:13]
subtract = (ori_arr - test_arr) ** 2
rms = sqrt(subtract.sum()) #/(ori_arr.shape[0] * ori_arr.shape[1])
print rms #, ori_arr.shape[0] * ori_arr.shape[1]
#subtract = (ori_arr - test_arr)
print subtract.max()
return rms
def _testing_DNN(self):
if self.space:
test_dir = '/home/danglab/Phong/TestData/Features_Norm_Space/'
else:
test_dir = '/home/danglab/Phong/TestData/Features_Norm/'
for type_test in sorted(os.listdir(test_dir)):
if not type_test.endswith('zip'):
type_test_dir = test_dir + type_test + '/'
print type_test_dir
dnn_predict_dir = '/home/danglab/DNN_Predict/Features_Norm/' + self.hidden_layer +'SQR/' + self.artic + '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
