def process_observations(self, obs):
if self.last_obs is not None:
# t0 = self.last_obs['timestamp']
# t1 = obs['timestamp']
# delta = t1 - t0
u = obs['commands']
y0 = self.last_obs['observations']
y1 = obs['observations']
self.last_data = (y0, u, y1)
# self.info('t0: %.3f t1: %.3f delta: %.3f u: %s' % (t0, t1, delta, u))
if self.shape is not None:
y0 = scipy_image_resample(y0, self.shape)
y1 = scipy_image_resample(y1, self.shape)
try:
self.diffeosystem_estimator.update(y0=y0, u0=u, y1=y1)
except DiffeoSystemEstimatorInterface.LearningConverged as e:
msg = 'DiffeoSystem converged: %s' % str(e)
raise PassiveAgentInterface.LearningConverged(msg)
self.last_obs = obs
def process_observations(self, obs):
if self.last_obs is not None:
# t0 = self.last_obs['timestamp']
# t1 = obs['timestamp']
# delta = t1 - t0
u = obs['commands']
y0 = self.last_obs['observations']
y1 = obs['observations']
self.last_data = (y0, u, y1)
# self.info('t0: %.3f t1: %.3f delta: %.3f u: %s' % (t0, t1, delta, u))
if self.shape is not None:
y0 = scipy_image_resample(y0, self.shape)
y1 = scipy_image_resample(y1, self.shape)
try:
self.diffeosystem_estimator.update(y0=y0, u0=u, y1=y1)
except DiffeoSystemEstimatorInterface.LearningConverged as e:
msg = 'DiffeoSystem converged: %s' % str(e)
raise PassiveAgentInterface.LearningConverged(msg)
self.last_obs = obs
def read_all(self):
data_central = DataCentral(self.boot_root)
log_index = data_central.get_log_index()
observations = log_index.read_all_robot_streams(id_robot=self.id_robot)
pairs = pairwise(observations)
i = 0
for bd1, bd2 in pairs:
i += 1
if i % 100 == 1:
print('read %d' % i)
y0 = bd1['observations']
y1 = bd2['observations']
u = bd1['commands']
if self.shape is not None:
y0 = scipy_image_resample(y0, self.shape, order=0)
np.clip(y0, 0, 1, y0)
y1 = scipy_image_resample(y1, self.shape, order=0)
np.clip(y1, 0, 1, y1)
log_item = LogItem(y0=y0, y1=y1, u=u, x0=None)
yield log_item
