def do_teach_ng(sensors_dir_ng, sensor, store, swarm):
swarm_config_path = sensors_dir_ng + sensor + '/stores/' + store + '/swarms/' + swarm + '/'
store_path = sensors_dir_ng + sensor + '/stores/' + store + '/out.csv'
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json') as json_file:
swarm_config = json.load(json_file)
print(swarm_config)
print swarm_config['inferenceArgs']['predictedField']
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference(
{"predictedField": swarm_config['inferenceArgs']['predictedField']})
target = open(swarm_config_path + "teach.csv", 'w')
spamwriter = csv.writer(target,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
fieldNames = map(lambda x: x['fieldName'], swarm_config['includedFields'])
columnNames = copy.copy(fieldNames)
presteps = swarm_config['inferenceArgs']['predictionSteps']
for prediction in presteps:
columnNames.append(swarm_config['inferenceArgs']['predictedField'] +
'_predicted_' + str(prediction))
spamwriter.writerow(columnNames)
with open(store_path, 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
for row in spamreader:
print ', '.join(row)
#prepare input for model
run_set = {}
for index, name in enumerate(fieldNames):
run_set[name] = float(row[index])
#run the model
result = model.run(run_set)
print result.rawInput
print result.inferences['multiStepBestPredictions']
#spamwriter.writerow([float(row[0]), float(row[1]), float(row[2]), float(row[3]), result.inferences['multiStepBestPredictions'][4]])
out_row = []
for index, name in enumerate(fieldNames):
out_row.append(float(row[index]))
for index, predstep in enumerate(presteps):
out_row.append(
result.inferences['multiStepBestPredictions'][predstep])
spamwriter.writerow(out_row)
model.save(str(swarm_config_path + "/model_save"))
def __init__(self,name,admin_in,admin_out,sensor_spec, sensors_dir,sensor_in,store,swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor. __init__(self,name=name,admin_in=admin_in, admin_out=admin_out,sensor_spec=sensor_spec, sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in +'/stores/' + store + '/swarms/' + swarm +'/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json')as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference({"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(name=self.name, lstnr=AbstractSensorListener(self.name,topic = '/topic/' +self.sensor_in,config=self.swarm_config_ng,model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +self.sensor_in, id=2, ack='auto')
self.values = []
self.self_announcement()
def do_teach_ng(sensors_dir_ng,sensor,store,swarm):
swarm_config_path = sensors_dir_ng + sensor +'/stores/' + store + '/swarms/' + swarm +'/'
store_path = sensors_dir_ng + sensor +'/stores/' + store + '/out.csv'
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json')as json_file:
swarm_config = json.load(json_file)
print(swarm_config)
print swarm_config['inferenceArgs']['predictedField']
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference({"predictedField": swarm_config['inferenceArgs']['predictedField']})
target = open(swarm_config_path+"teach.csv", 'w')
spamwriter = csv.writer(target, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
fieldNames = map(lambda x: x['fieldName'],swarm_config['includedFields'])
columnNames = copy.copy(fieldNames)
presteps = swarm_config['inferenceArgs']['predictionSteps']
for prediction in presteps:
columnNames.append(swarm_config['inferenceArgs']['predictedField']+'_predicted_' + str(prediction))
spamwriter.writerow(columnNames)
with open(store_path, 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
for row in spamreader:
print ', '.join(row)
#prepare input for model
run_set = {}
for index, name in enumerate(fieldNames):
run_set[name] = float(row[index])
#run the model
result = model.run(run_set)
print result.rawInput
print result.inferences['multiStepBestPredictions']
#spamwriter.writerow([float(row[0]), float(row[1]), float(row[2]), float(row[3]), result.inferences['multiStepBestPredictions'][4]])
out_row = []
for index, name in enumerate(fieldNames):
out_row.append(float(row[index]))
for index, predstep in enumerate(presteps):
out_row.append(result.inferences['multiStepBestPredictions'][predstep])
spamwriter.writerow(out_row)
model.save(str(swarm_config_path + "/model_save"))
def __init__(self, name, admin_in, admin_out, sensor_spec, sensors_dir,
sensor_in, store, swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor.__init__(self,
name=name,
admin_in=admin_in,
admin_out=admin_out,
sensor_spec=sensor_spec,
sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in + '/stores/' + store + '/swarms/' + swarm + '/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json') as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(
getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference(
{"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(
name=self.name,
lstnr=AbstractSensorListener(self.name,
topic='/topic/' + self.sensor_in,
config=self.swarm_config_ng,
model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(
name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +
self.sensor_in,
id=2,
ack='auto')
self.values = []
self.self_announcement()
