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