def run(positions, plotEvery=1):
encoder = OneDDepthEncoder(positions=positions,
radius=5,
wrapAround=True,
nPerPosition=28,
wPerPosition=3,
minVal=0,
maxVal=1)
fetcher = Fetcher()
plotter = Plotter(encoder)
learner = QLearner(ACTIIONS, n=1008)
lastState = None
lastAction = None
while True:
outputData = fetcher.sync()
if outputData is None:
continue
if fetcher.skippedTimesteps > 0:
print ("Warning: skipped {0} timesteps, "
"now at {1}").format(fetcher.skippedTimesteps, fetcher.timestep)
if not ("reset" in outputData and
"ForwardsSweepSensor" in outputData and
"steer" in outputData):
print ("Warning: Missing data on timestep {0}: {1}".format(
fetcher.timestep, outputData))
continue
if outputData["reset"]:
print "Reset."
sensor = outputData["ForwardsSweepSensor"]
steer = outputData["steer"]
reward = outputData.get("reward") or 0
encoding = encoder.encode(numpy.array(sensor))
if lastState is not None:
learner.update(lastState, str(lastAction), encoding, str(steer), reward)
value = learner.value(encoding)
qValues = {}
for action in ACTIIONS:
qValues[action] = learner.qValue(encoding, action)
fetcher.inputData["qValues"] = qValues
fetcher.inputData["bestAction"] = learner.bestAction(encoding)
plotter.update(sensor, encoding, steer, reward, value, qValues)
# if fetcher.timestep % plotEvery == 0:
# plotter.render()
lastState = encoding
lastAction = steer
def run():
params = model_params.MODEL_PARAMS
model = ModelFactory.create(params)
model.enableInference({"predictedField": "vector"})
anomalyLikelihoodHelper = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=100,
estimationSamples=100
)
lastPosition = None
lastTime = None
fetcher = Fetcher()
while True:
data = fetcher.sync()
if data is None:
continue
if fetcher.skippedTimesteps > 0:
print "WARNING: Skipped {0} timesteps.".format(fetcher.skippedTimesteps)
time = data["timestamp"]
position = np.array([data["x"], data["y"], data["z"]]).astype(int)
if lastPosition is None:
lastPosition = position
lastTime = time
radius = calculateRadius(lastPosition, position, lastTime, time)
modelInput = {
"vector": (position, radius)
}
result = model.run(modelInput)
anomalyScore = result.inferences["anomalyScore"]
anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability(
random.random(), anomalyScore
)
fetcher.inputData["anomalyScore"] = anomalyScore
fetcher.inputData["anomalyLikelihood"] = anomalyLikelihood
print anomalyScore, anomalyLikelihood
lastPosition = position
lastTime = time
def run():
fetcher = Fetcher()
while True:
outputData = fetcher.sync()
if outputData is None:
continue
print "Timestep: {0}".format(fetcher.timestep)
print outputData
if fetcher.skippedTimesteps > 0:
print ("Warning: skipped {0} timesteps, "
"now at {1}").format(fetcher.skippedTimesteps, fetcher.timestep)
if outputData["reset"]:
print "Reset."
print "============"
def run():
fetcher = Fetcher()
while True:
outputData = fetcher.sync()
if outputData is None:
continue
print "Timestep: {0}".format(fetcher.timestep)
print outputData
if fetcher.skippedTimesteps > 0:
print("Warning: skipped {0} timesteps, "
"now at {1}").format(fetcher.skippedTimesteps,
fetcher.timestep)
if outputData["reset"]:
print "Reset."
print "============"
def collectDepthData(outputDir=_OUTPUT_DIR):
fetcher = Fetcher()
holdingDownKey = False
while True:
outputData = fetcher.sync()
if outputData is None or "collectKeyPressed" not in outputData:
holdingDownKey = False
continue
if outputData["collectKeyPressed"] == 0:
holdingDownKey = False
continue
if holdingDownKey:
print "Stop holding down the key!"
continue
print "Collecting data."
holdingDownKey = True
sensor = outputData["ForwardsSweepSensor"]
depthData = np.array(sensor)
dt = time.strftime("%y%m%d%H%M%S")
depthID = 'depth_'+dt+'.txt'
depthOutputPath = os.path.join(os.path.dirname(__file__),
outputDir,
depthID)
np.savetxt(depthOutputPath, depthData)
print "Wrote out to ", depthOutputPath
def collectDepthData(outputDir=_OUTPUT_DIR):
fetcher = Fetcher()
holdingDownKey = False
while True:
outputData = fetcher.sync()
if outputData is None or "collectKeyPressed" not in outputData:
holdingDownKey = False
continue
if outputData["collectKeyPressed"] == 0:
holdingDownKey = False
continue
if holdingDownKey:
print "Stop holding down the key!"
continue
print "Collecting data."
holdingDownKey = True
sensor = outputData["ForwardsSweepSensor"]
depthData = np.array(sensor)
dt = time.strftime("%y%m%d%H%M%S")
depthID = 'depth_'+dt+'.txt'
depthOutputPath = os.path.join(os.path.dirname(__file__),
outputDir,
depthID)
np.savetxt(depthOutputPath, depthData)
print "Wrote out to ", depthOutputPath
class UnityEnvironment:
def __init__(self, ):
self.fetcher = Fetcher()
self.actions = [[0, 0], [1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0],
[-1, -1], [0, -1], [1, -1]]
self.curr_score = 0
self.curr_step = 0
def reset(self):
state, _, _ = self._get_obs()
self.curr_score = 0
self.curr_step = 0
self.fetcher.inputData = {'Reset': True}
self.fetcher.sync()
time.sleep(0.04)
state, _, _, _ = self.step(0)
return state
def step(self, action):
hor = self.actions[action][0]
vert = self.actions[action][1]
self.fetcher.inputData = {'Horizontal': hor, 'Vertical': vert}
self.fetcher.sync()
time.sleep(0.04)
self.curr_step += 1
state, reward, terminal = self._get_obs()
if self.curr_step > 1000:
terminal = True
return state, reward, terminal, []
def numActions(self):
return len(self.actions)
def _get_obs(self):
data = None
count = 0
while data is None:
count += 1
data = self.fetcher.sync()
time.sleep(0.001)
if count > 1000:
print("Error getting data from server.")
new_score = data.get('Score')
if new_score is not None:
reward = max(new_score - self.curr_score, 0)
self.curr_score = new_score
else:
reward = 0
terminal = data.get('Terminal') or False
objects = []
for k, v in data.items():
if 'obj' in k:
class_one_hot = [1.0, 0.0] if (v['tag']
== "Player") else [0.0, 1.0]
pos = v['position']
vel = v['velocity']
pos = [pos['y'], pos['x'], pos['z']]
vel = [vel['y'], vel['x'], vel['z']]
objects.append(np.array(class_one_hot + pos + vel))
if (len(objects) == 0):
objects = np.zeros((1, 8))
return objects, reward, terminal