| """current learned gravity-aware residual conditioned on independent historic energy records.""" |
| import numpy as np |
| from robotics_moe import RoboticsMoE51,prediction as parent_prediction |
| from robotics_moe import Prepared51 |
|
|
| class PreparedCalibrated(Prepared51): |
| def __init__(self,data,mean=None,std=None): |
| super().__init__(data,mean,std) |
| past=np.asarray(data['history_calibration'],np.float32) |
| |
| |
| logmachine=past[:,:6].mean(axis=1) |
| if mean is None: |
| self.mean['past']=past.mean(axis=0) |
| self.std['past']=np.maximum(past.std(axis=0),.025) |
| pn=np.clip((past-self.mean['past'])/self.std['past'],-7,7) |
| self.norm['history']=np.concatenate([self.norm['history'],pn],axis=1).astype(np.float32) |
| self.logmachine=logmachine.copy() |
| self.nominal_energy_prior=self.prior[:,1].copy() |
| self.prior[:,1]*=np.exp(logmachine) |
| self.target=np.log(np.maximum(self.y,1e-6)/np.maximum(self.prior,1e-6)).astype(np.float32) |
|
|
| class RoboticsMoECalibrated(RoboticsMoE51): |
| def __init__(self):super().__init__(history_dim=36) |
|
|
|
|
| def prediction(model,ds,batch=512,device=None,use_history=True): |
| p=parent_prediction(model,ds,batch=batch,device=device,use_history=use_history) |
| if not use_history: |
| |
| p[:,1]*=np.exp(-ds.logmachine) |
| return p |
|
|
