def _preprocess(self, x, y, valid_x, valid_y):
self.normalize_x = data_helper.normalizeX(x, self.b)
self.normalize_valid_x = data_helper.normalizeX(valid_x, self.b)
# self.floorID_y = y[:, 2]
#
# self.floorID_valid_y = valid_y[:, 2]
self.normY.fit(y[:, 0], y[:, 1])
self.longitude_normalize_y, self.latitude_normalize_y = self.normY.normalizeY(
y[:, 0], y[:, 1])
self.longitude_normalize_valid_y, self.latitude_normalize_valid_y = self.normY.normalizeY(
valid_y[:, 0], valid_y[:, 1])
def _preprocess(self, x, y, valid_x, valid_y):
self.normalize_x = data_helper.normalizeX(x, self.b)
self.normalize_valid_x = data_helper.normalizeX(valid_x, self.b)
# self.pos_x,self.pos_y=y[:,0],y[:,1]
# self.normY.fit(y[:, 0], y[:, 1])
self.pos_x, self.pos_y = self.normY.normalizeY(y[:, 0], y[:, 1])
self.floorID_y = y[:, 2]
# self.buildingID_y = y[:, 3]
self.valid_pos_x, self.valid_pos_y = self.normY.normalizeY(
valid_y[:, 0], valid_y[:, 1])
self.floorID_valid_y = valid_y[:, 2]
def predict(self, x):
predict_buildingID=[]
predict_floorID=[]
predict_longitude=[]
predict_latitude=[]
x = data_helper.normalizeX(x,self.b)
#################predict_Location
if Run_Location:
self.position_model = load_model('Location_model/Location_model.h5')
predict_Location= self.position_model.predict(x)
predict_longitude, predict_latitude = self.normY.reverse_normalizeY(predict_Location[:, 0],
predict_Location[:, 1])
##################predict_floor
if Train_New_Floor:
predict_floorID = self.new_floor_model.predict(x)
predict_floorID = data_helper.oneHotDecode(predict_floorID)
elif Run_Floor:
self.floor_model = load_model('Floor_model/floor_model(AE_' + self.AE_floor_bottleneck + ')-Conv(' + self.floor_layers + ').h5')
predict_floorID = self.floor_model.predict(x)
predict_floorID = data_helper.oneHotDecode(predict_floorID)
################predict_building
if Run_Building:
self.building_model= load_model(os.path.join(Building_model_dir,('building_model(AE_' + self.AE_building_bottleneck + ')-3.h5')))
predict_buildingID = self.building_model.predict(x)
predict_buildingID = data_helper.oneHotDecode(predict_buildingID)
return predict_buildingID,predict_floorID ,predict_longitude,predict_latitude
def predict(self, x):
"""
Predict the tag by the model
You should train the model before calling this function
Arg: x - The feature array that you want to predict
Ret: The predict result whose shape is [num_of_row, 4]
"""
# Load model
self.load()
# Testing
x = data_helper.normalizeX(x)
predict_longitude = self.longitude_regression_model.predict(x)
predict_latitude = self.latitude_regression_model.predict(x)
predict_floor = self.floor_classifier.predict(x)
predict_building = self.building_classifier.predict(x)
# Reverse normalization
predict_longitude, predict_latitude = data_helper.reverse_normalizeY(predict_longitude, predict_latitude)
# Return the result
res = np.concatenate((np.expand_dims(predict_longitude, axis=-1),
np.expand_dims(predict_latitude, axis=-1)), axis=-1)
res = np.concatenate((res, np.expand_dims(predict_floor, axis=-1)), axis=-1)
res = np.concatenate((res, np.expand_dims(predict_building, axis=-1)), axis=-1)
return res
def predict(self, x):
predict_buildingID=[]
predict_floorID=[]
predict_longitude=[]
predict_latitude=[]
x = data_helper.normalizeX(x)
#################predict_Location
if Run_Location:
self.position_model = load_model('Location_model/Location_model.h5')
predict_Location= self.position_model.predict(x)
predict_longitude, predict_latitude = self.normY.reverse_normalizeY(predict_Location[:, 0],
predict_Location[:, 1])
##################predict_floor
if Run_Floor:
self.floor_model = load_model('Floor_model/floor_model(AE_' + self.AE_floor_bottleneck + ')-Conv(' + self.floor_layers + ').h5')
predict_floorID = self.floor_model.predict(x)
predict_floorID=predict_floorID[0]+predict_floorID[1]+predict_floorID[2]
predict_floorID=data_helper.oneHotDecode(predict_floorID)
# predict_floorID = data_helper.oneHotDecode_list(predict_floorID)
# predict_floorID=np.round(np.mean(predict_floorID,axis=0))
# predict_floorID = data_helper.oneHotDecode_list(predict_floorID)
# predict_floorID = np.round(np.median(predict_floorID, axis=0))
################predict_building
if Run_Building:
self.building_model= load_model(os.path.join(Building_model_dir,('building_model(AE_' + self.AE_building_bottleneck + ')-3.h5')))
predict_buildingID = self.building_model.predict(x)
predict_buildingID = data_helper.oneHotDecode(predict_buildingID)
return predict_buildingID,predict_floorID ,predict_longitude,predict_latitude
def predict(self, x):
x = data_helper.normalizeX(x, self.b)
self.floor_model = load_model(
os.path.join(Floor_model_dir, ('Floor_model.h5')))
predict_floorID = self.floor_model.predict(x)
predict_floorID = data_helper.oneHotDecode(predict_floorID)
return predict_floorID
def predict(self, x):
x = data_helper.normalizeX(x, self.b)
self.position_model = load_model(
os.path.join(Location_model_dir, ('Location_model.h5')))
predict_Location = self.position_model.predict(x)
predict_longitude, predict_latitude = self.normY.reverse_normalizeY(
predict_Location[:, 0], predict_Location[:, 1])
return predict_longitude, predict_latitude
def _preprocess(self, x, y):
"""
Data pre-processing for the x and y array
It's not recommend to use this function directly!
Arg: x - The feature array
y - The tag array
"""
self.normalize_x = data_helper.normalizeX(x)
self.longitude_normalize_y, self.latitude_normalize_y = data_helper.normalizeY(y[:, 0], y[:, 1])
self.floorID_y = y[:, 2]
self.buildingID_y = y[:, 3]
def predict(self, x):
x = data_helper.normalizeX(x)
predict_longitude = self.longitude_regression_model.predict(x)
predict_latitude = self.latitude_regression_model.predict(x)
predict_floorID = self.floor_model.predict(x)
predict_buildingID = self.building_model.predict(x)
# Reverse normalization
predict_longitude, predict_latitude = data_helper.reverse_normalizeY(
predict_longitude, predict_latitude)
predict_floorID = data_helper.oneHotDecode(predict_floorID)
predict_buildingID = data_helper.oneHotDecode(predict_buildingID)
# Return the result
res = np.concatenate((np.expand_dims(predict_longitude, axis=-1),
np.expand_dims(predict_latitude, axis=-1)),
axis=-1)
res = np.concatenate((res, np.expand_dims(predict_floorID, axis=-1)),
axis=-1)
res = np.concatenate(
(res, np.expand_dims(predict_buildingID, axis=-1)), axis=-1)
return res
def _preprocess(self, x, y, valid_x, valid_y):
self.normalize_x = data_helper.normalizeX(x, self.b)
self.normalize_valid_x = data_helper.normalizeX(valid_x, self.b)
self.floorID_y = y[:, 2]
self.floorID_valid_y = valid_y[:, 2]
